SageMaker Core

Introduction

Welcome to the sagemaker-core Python SDK, an SDK designed to provide an object-oriented interface for interacting with Amazon SageMaker resources. It offers full parity with SageMaker APIs, allowing developers to leverage all SageMaker capabilities directly through the SDK. sagemaker-core introduces features such as dedicated resource classes, resource chaining, auto code completion, comprehensive documentation and type hints to enhance the developer experience as well as productivity.

Key Features

• Object-Oriented Interface: Provides a structured way to interact with SageMaker resources, making it easier to manage them using familiar object-oriented programming techniques.

• Resource Chaining: Allows seamless connection of SageMaker resources by passing outputs as inputs between them, simplifying workflows and reducing the complexity of parameter management.

• Full Parity with SageMaker APIs: Ensures access to all SageMaker capabilities through the SDK, providing a comprehensive toolset for building and deploying machine learning models.

• Abstraction of Low-Level Details: Automatically handles resource state transitions and polling logic, freeing developers from managing these intricacies and allowing them to focus on higher-level tasks.

• Auto Code Completion: Enhances the developer experience by offering real-time suggestions and completions in popular IDEs, reducing syntax errors and speeding up the coding process.

• Comprehensive Documentation and Type Hints: Provides detailed guidance and type hints to help developers understand functionalities, write code faster, and reduce errors without complex API navigation.

• Incorporation of Intelligent Defaults: Integrates the previous SageMaker SDK feature of intelligent defaults, allowing developers to set default values for parameters like IAM roles and VPC configurations. This streamlines the setup process, enabling developers to focus on customizations specific to their use case.

Benefits

• Simplified Development: By abstracting low-level details and providing intelligent defaults, developers can focus on building and deploying machine learning models without getting bogged down by repetitive tasks.

• Increased Productivity: The SDK’s features, such as auto code completion and type hints, help developers write code faster and with fewer errors.

• Enhanced Readability: Resource chaining and dedicated resource classes result in more readable and maintainable code.

Docs and Examples

Learn more about the sagemaker-core SDK and its features by visting the What’s New Announcement.

For examples and walkthroughs, see the SageMaker Core Examples.

For detailed documentation, including the API reference, see Read the Docs.

SageMaker Core Resources

class sagemaker_core.main.resources.Action(*, action_name, action_arn=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, action_type=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Action

Parameters:

• action_name (str)

• action_arn (str | None)

• source (ActionSource | None)

• action_type (str | None)

• description (str | None)

• status (str | None)

• properties (Dict[str, str] | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• metadata_properties (MetadataProperties | None)

• lineage_group_arn (str | None)

action_name

The name of the action.

Type:

str

action_arn

The Amazon Resource Name (ARN) of the action.

Type:

str | None

source

The source of the action.

Type:

sagemaker_core.main.shapes.ActionSource | None

action_type

The type of the action.

Type:

str | None

description

The description of the action.

Type:

str | None

status

The status of the action.

Type:

str | None

properties

A list of the action’s properties.

Type:

Dict[str, str] | None

creation_time

When the action was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

When the action was last modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

metadata_properties

Type:

sagemaker_core.main.shapes.MetadataProperties | None

lineage_group_arn

The Amazon Resource Name (ARN) of the lineage group.

Type:

str | None

classmethod create(action_name, source, action_type, description=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Action resource

Parameters:

• action_name (str) – The name of the action. Must be unique to your account in an Amazon Web Services Region.

• source (ActionSource) – The source type, ID, and URI.

• action_type (str) – The action type.

• description (str | None) – The description of the action.

• status (str | None) – The status of the action.

• properties (Dict[str, str] | None) – A list of properties to add to the action.

• metadata_properties (MetadataProperties | None)

• tags (List[Tag] | None) – A list of tags to apply to the action.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Action resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Action | None

delete()

Delete a Action resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(action_name, session=None, region=None)

Get a Action resource

Parameters:

• action_name (str) – The name of the action to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Action resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Action | None

classmethod get_all(source_uri=<sagemaker_core.main.utils.Unassigned object>, action_type=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Action resources

Parameters:

• source_uri (str | None) – A filter that returns only actions with the specified source URI.

• action_type (str | None) – A filter that returns only actions of the specified type.

• created_after (datetime | None) – A filter that returns only actions created on or after the specified time.

• created_before (datetime | None) – A filter that returns only actions created on or before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• next_token – If the previous call to ListActions didn’t return the full set of actions, the call returns a token for getting the next set of actions.

• max_results – The maximum number of actions to return in the response. The default value is 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Action resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[Action]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Action resource

Returns:

The Action resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Action | None

update(description=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, properties_to_remove=<sagemaker_core.main.utils.Unassigned object>)

Update a Action resource

Parameters:

• properties_to_remove (List[str] | None) – A list of properties to remove.

• description (str | None)

• status (str | None)

• properties (Dict[str, str] | None)

Returns:

The Action resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

Action | None

class sagemaker_core.main.resources.Algorithm(*, algorithm_name, algorithm_arn=<sagemaker_core.main.utils.Unassigned object>, algorithm_description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, training_specification=<sagemaker_core.main.utils.Unassigned object>, inference_specification=<sagemaker_core.main.utils.Unassigned object>, validation_specification=<sagemaker_core.main.utils.Unassigned object>, algorithm_status=<sagemaker_core.main.utils.Unassigned object>, algorithm_status_details=<sagemaker_core.main.utils.Unassigned object>, product_id=<sagemaker_core.main.utils.Unassigned object>, certify_for_marketplace=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Algorithm

Parameters:

• algorithm_name (str)

• algorithm_arn (str | None)

• algorithm_description (str | None)

• creation_time (datetime | None)

• training_specification (TrainingSpecification | None)

• inference_specification (InferenceSpecification | None)

• validation_specification (AlgorithmValidationSpecification | None)

• algorithm_status (str | None)

• algorithm_status_details (AlgorithmStatusDetails | None)

• product_id (str | None)

• certify_for_marketplace (bool | None)

algorithm_name

The name of the algorithm being described.

Type:

str

algorithm_arn

The Amazon Resource Name (ARN) of the algorithm.

Type:

str | None

creation_time

A timestamp specifying when the algorithm was created.

Type:

datetime.datetime | None

training_specification

Details about training jobs run by this algorithm.

Type:

sagemaker_core.main.shapes.TrainingSpecification | None

algorithm_status

The current status of the algorithm.

Type:

str | None

algorithm_status_details

Details about the current status of the algorithm.

Type:

sagemaker_core.main.shapes.AlgorithmStatusDetails | None

algorithm_description

A brief summary about the algorithm.

Type:

str | None

inference_specification

Details about inference jobs that the algorithm runs.

Type:

sagemaker_core.main.shapes.InferenceSpecification | None

validation_specification

Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.

Type:

sagemaker_core.main.shapes.AlgorithmValidationSpecification | None

product_id

The product identifier of the algorithm.

Type:

str | None

certify_for_marketplace

Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.

Type:

bool | None

classmethod create(algorithm_name, training_specification, algorithm_description=<sagemaker_core.main.utils.Unassigned object>, inference_specification=<sagemaker_core.main.utils.Unassigned object>, validation_specification=<sagemaker_core.main.utils.Unassigned object>, certify_for_marketplace=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Algorithm resource

Parameters:

• algorithm_name (str) – The name of the algorithm.

• training_specification (TrainingSpecification) – Specifies details about training jobs run by this algorithm, including the following: The Amazon ECR path of the container and the version digest of the algorithm. The hyperparameters that the algorithm supports. The instance types that the algorithm supports for training. Whether the algorithm supports distributed training. The metrics that the algorithm emits to Amazon CloudWatch. Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs. The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

• algorithm_description (str | None) – A description of the algorithm.

• inference_specification (InferenceSpecification | None) – Specifies details about inference jobs that the algorithm runs, including the following: The Amazon ECR paths of containers that contain the inference code and model artifacts. The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference. The input and output content formats that the algorithm supports for inference.

• validation_specification (AlgorithmValidationSpecification | None) – Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm’s training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm’s inference code.

• certify_for_marketplace (bool | None) – Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Algorithm resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Algorithm | None

delete()

Delete a Algorithm resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

None

classmethod get(algorithm_name, session=None, region=None)

Get a Algorithm resource

Parameters:

• algorithm_name (str) – The name of the algorithm to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Algorithm resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Algorithm | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Algorithm resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only algorithms created after the specified time (timestamp).

• creation_time_before (datetime | None) – A filter that returns only algorithms created before the specified time (timestamp).

• max_results – The maximum number of algorithms to return in the response.

• name_contains (str | None) – A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

• next_token – If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.

• sort_by (str | None) – The parameter by which to sort the results. The default is CreationTime.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Algorithm resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Algorithm]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Algorithm resource

Returns:

The Algorithm resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Algorithm | None

wait_for_delete(poll=5, timeout=None)

Wait for a Algorithm resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Algorithm resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'InProgress', 'Completed', 'Failed', 'Deleting']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.App(*, domain_id, app_type, app_name, app_arn=<sagemaker_core.main.utils.Unassigned object>, user_profile_name=<sagemaker_core.main.utils.Unassigned object>, space_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, last_health_check_timestamp=<sagemaker_core.main.utils.Unassigned object>, last_user_activity_timestamp=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, resource_spec=<sagemaker_core.main.utils.Unassigned object>, built_in_lifecycle_config_arn=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource App

Parameters:

• domain_id (str)

• app_type (str)

• app_name (str)

• app_arn (str | None)

• user_profile_name (str | None)

• space_name (str | None)

• status (str | None)

• last_health_check_timestamp (datetime | None)

• last_user_activity_timestamp (datetime | None)

• creation_time (datetime | None)

• failure_reason (str | None)

• resource_spec (ResourceSpec | None)

• built_in_lifecycle_config_arn (str | None)

app_arn

The Amazon Resource Name (ARN) of the app.

Type:

str | None

app_type

The type of app.

Type:

str

app_name

The name of the app.

Type:

str

domain_id

The domain ID.

Type:

str

user_profile_name

The user profile name.

Type:

str | None

space_name

The name of the space. If this value is not set, then UserProfileName must be set.

Type:

str | None

status

The status.

Type:

str | None

last_health_check_timestamp

The timestamp of the last health check.

Type:

datetime.datetime | None

last_user_activity_timestamp

The timestamp of the last user’s activity. LastUserActivityTimestamp is also updated when SageMaker AI performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.

Type:

datetime.datetime | None

creation_time

The creation time of the application. After an application has been shut down for 24 hours, SageMaker AI deletes all metadata for the application. To be considered an update and retain application metadata, applications must be restarted within 24 hours after the previous application has been shut down. After this time window, creation of an application is considered a new application rather than an update of the previous application.

Type:

datetime.datetime | None

failure_reason

The failure reason.

Type:

str | None

resource_spec

The instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.

Type:

sagemaker_core.main.shapes.ResourceSpec | None

built_in_lifecycle_config_arn

The lifecycle configuration that runs before the default lifecycle configuration

Type:

str | None

classmethod create(domain_id, app_type, app_name, user_profile_name=<sagemaker_core.main.utils.Unassigned object>, space_name=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, resource_spec=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a App resource

Parameters:

• domain_id (str) – The domain ID.

• app_type (str) – The type of app.

• app_name (str) – The name of the app.

• user_profile_name (str | object | None) – The user profile name. If this value is not set, then SpaceName must be set.

• space_name (str | object | None) – The name of the space. If this value is not set, then UserProfileName must be set.

• tags (List[Tag] | None) – Each tag consists of a key and an optional value. Tag keys must be unique per resource.

• resource_spec (ResourceSpec | None) – The instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance. The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The App resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

App | None

delete()

Delete a App resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(domain_id, app_type, app_name, user_profile_name=<sagemaker_core.main.utils.Unassigned object>, space_name=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a App resource

Parameters:

• domain_id (str) – The domain ID.

• app_type (str) – The type of app.

• app_name (str) – The name of the app.

• user_profile_name (str | None) – The user profile name. If this value is not set, then SpaceName must be set.

• space_name (str | None) – The name of the space.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The App resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

App | None

classmethod get_all(sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, domain_id_equals=<sagemaker_core.main.utils.Unassigned object>, user_profile_name_equals=<sagemaker_core.main.utils.Unassigned object>, space_name_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all App resources

Parameters:

• next_token – If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• max_results – This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• sort_by (str | None) – The parameter by which to sort the results. The default is CreationTime.

• domain_id_equals (str | None) – A parameter to search for the domain ID.

• user_profile_name_equals (str | None) – A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.

• space_name_equals (str | None) – A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed App resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[App]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a App resource

Returns:

The App resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

App | None

wait_for_delete(poll=5, timeout=None)

Wait for a App resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a App resource to reach certain status.

Parameters:

• target_status (Literal['Deleted', 'Deleting', 'Failed', 'InService', 'Pending']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.AppImageConfig(*, app_image_config_name, app_image_config_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, kernel_gateway_image_config=<sagemaker_core.main.utils.Unassigned object>, jupyter_lab_app_image_config=<sagemaker_core.main.utils.Unassigned object>, code_editor_app_image_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource AppImageConfig

Parameters:

• app_image_config_name (str)

• app_image_config_arn (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• kernel_gateway_image_config (KernelGatewayImageConfig | None)

• jupyter_lab_app_image_config (JupyterLabAppImageConfig | None)

• code_editor_app_image_config (CodeEditorAppImageConfig | None)

app_image_config_arn

The ARN of the AppImageConfig.

Type:

str | None

app_image_config_name

The name of the AppImageConfig.

Type:

str

creation_time

When the AppImageConfig was created.

Type:

datetime.datetime | None

last_modified_time

When the AppImageConfig was last modified.

Type:

datetime.datetime | None

kernel_gateway_image_config

The configuration of a KernelGateway app.

Type:

sagemaker_core.main.shapes.KernelGatewayImageConfig | None

jupyter_lab_app_image_config

The configuration of the JupyterLab app.

Type:

sagemaker_core.main.shapes.JupyterLabAppImageConfig | None

code_editor_app_image_config

The configuration of the Code Editor app.

Type:

sagemaker_core.main.shapes.CodeEditorAppImageConfig | None

classmethod create(app_image_config_name, tags=<sagemaker_core.main.utils.Unassigned object>, kernel_gateway_image_config=<sagemaker_core.main.utils.Unassigned object>, jupyter_lab_app_image_config=<sagemaker_core.main.utils.Unassigned object>, code_editor_app_image_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a AppImageConfig resource

Parameters:

• app_image_config_name (str) – The name of the AppImageConfig. Must be unique to your account.

• tags (List[Tag] | None) – A list of tags to apply to the AppImageConfig.

• kernel_gateway_image_config (KernelGatewayImageConfig | None) – The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

• jupyter_lab_app_image_config (JupyterLabAppImageConfig | None) – The JupyterLabAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.

• code_editor_app_image_config (CodeEditorAppImageConfig | None) – The CodeEditorAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The AppImageConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

AppImageConfig | None

delete()

Delete a AppImageConfig resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(app_image_config_name, session=None, region=None)

Get a AppImageConfig resource

Parameters:

• app_image_config_name (str) – The name of the AppImageConfig to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The AppImageConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

AppImageConfig | None

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, modified_time_before=<sagemaker_core.main.utils.Unassigned object>, modified_time_after=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all AppImageConfig resources

Parameters:

• max_results – The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.

• next_token – If the previous call to ListImages didn’t return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.

• name_contains (str | None) – A filter that returns only AppImageConfigs whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only AppImageConfigs created on or before the specified time.

• creation_time_after (datetime | None) – A filter that returns only AppImageConfigs created on or after the specified time.

• modified_time_before (datetime | None) – A filter that returns only AppImageConfigs modified on or before the specified time.

• modified_time_after (datetime | None) – A filter that returns only AppImageConfigs modified on or after the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed AppImageConfig resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[AppImageConfig]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a AppImageConfig resource

Returns:

The AppImageConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

AppImageConfig | None

update(kernel_gateway_image_config=<sagemaker_core.main.utils.Unassigned object>, jupyter_lab_app_image_config=<sagemaker_core.main.utils.Unassigned object>, code_editor_app_image_config=<sagemaker_core.main.utils.Unassigned object>)

Update a AppImageConfig resource

Returns:

The AppImageConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Parameters:

• kernel_gateway_image_config (KernelGatewayImageConfig | None)

• jupyter_lab_app_image_config (JupyterLabAppImageConfig | None)

• code_editor_app_image_config (CodeEditorAppImageConfig | None)

Return type:

AppImageConfig | None

class sagemaker_core.main.resources.Artifact(*, artifact_arn, artifact_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, artifact_type=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Artifact

Parameters:

• artifact_arn (str)

• artifact_name (str | None)

• source (ArtifactSource | None)

• artifact_type (str | None)

• properties (Dict[str, str] | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• metadata_properties (MetadataProperties | None)

• lineage_group_arn (str | None)

artifact_name

The name of the artifact.

Type:

str | None

artifact_arn

The Amazon Resource Name (ARN) of the artifact.

Type:

str

source

The source of the artifact.

Type:

sagemaker_core.main.shapes.ArtifactSource | None

artifact_type

The type of the artifact.

Type:

str | None

properties

A list of the artifact’s properties.

Type:

Dict[str, str] | None

creation_time

When the artifact was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

When the artifact was last modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

metadata_properties

Type:

sagemaker_core.main.shapes.MetadataProperties | None

lineage_group_arn

The Amazon Resource Name (ARN) of the lineage group.

Type:

str | None

classmethod create(source, artifact_type, artifact_name=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Artifact resource

Parameters:

• source (ArtifactSource) – The ID, ID type, and URI of the source.

• artifact_type (str) – The artifact type.

• artifact_name (str | None) – The name of the artifact. Must be unique to your account in an Amazon Web Services Region.

• properties (Dict[str, str] | None) – A list of properties to add to the artifact.

• metadata_properties (MetadataProperties | None)

• tags (List[Tag] | None) – A list of tags to apply to the artifact.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Artifact resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Artifact | None

delete()

Delete a Artifact resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(artifact_arn, session=None, region=None)

Get a Artifact resource

Parameters:

• artifact_arn (str) – The Amazon Resource Name (ARN) of the artifact to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Artifact resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Artifact | None

classmethod get_all(source_uri=<sagemaker_core.main.utils.Unassigned object>, artifact_type=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Artifact resources

Parameters:

• source_uri (str | None) – A filter that returns only artifacts with the specified source URI.

• artifact_type (str | None) – A filter that returns only artifacts of the specified type.

• created_after (datetime | None) – A filter that returns only artifacts created on or after the specified time.

• created_before (datetime | None) – A filter that returns only artifacts created on or before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• next_token – If the previous call to ListArtifacts didn’t return the full set of artifacts, the call returns a token for getting the next set of artifacts.

• max_results – The maximum number of artifacts to return in the response. The default value is 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Artifact resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[Artifact]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Artifact resource

Returns:

The Artifact resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Artifact | None

update(artifact_name=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, properties_to_remove=<sagemaker_core.main.utils.Unassigned object>)

Update a Artifact resource

Parameters:

• properties_to_remove (List[str] | None) – A list of properties to remove.

• artifact_name (str | None)

• properties (Dict[str, str] | None)

Returns:

The Artifact resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

Artifact | None

class sagemaker_core.main.resources.Association(*, source_arn=<sagemaker_core.main.utils.Unassigned object>, destination_arn=<sagemaker_core.main.utils.Unassigned object>, source_type=<sagemaker_core.main.utils.Unassigned object>, destination_type=<sagemaker_core.main.utils.Unassigned object>, association_type=<sagemaker_core.main.utils.Unassigned object>, source_name=<sagemaker_core.main.utils.Unassigned object>, destination_name=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Association

Parameters:

• source_arn (str | None)

• destination_arn (str | None)

• source_type (str | None)

• destination_type (str | None)

• association_type (str | None)

• source_name (str | None)

• destination_name (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

source_arn

The ARN of the source.

Type:

str | None

destination_arn

The Amazon Resource Name (ARN) of the destination.

Type:

str | None

source_type

The source type.

Type:

str | None

destination_type

The destination type.

Type:

str | None

association_type

The type of the association.

Type:

str | None

source_name

The name of the source.

Type:

str | None

destination_name

The name of the destination.

Type:

str | None

creation_time

When the association was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod add(source_arn, destination_arn, association_type=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Creates an association between the source and the destination.

Parameters:

• source_arn (str) – The ARN of the source.

• destination_arn (str) – The Amazon Resource Name (ARN) of the destination.

• association_type (str | None) – The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use. ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job. AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment. DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs. Produced - The source generated the destination. For example, a training job produced a model artifact.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

None

delete()

Delete a Association resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get_all(source_arn=<sagemaker_core.main.utils.Unassigned object>, destination_arn=<sagemaker_core.main.utils.Unassigned object>, source_type=<sagemaker_core.main.utils.Unassigned object>, destination_type=<sagemaker_core.main.utils.Unassigned object>, association_type=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Association resources

Parameters:

• source_arn (str | None) – A filter that returns only associations with the specified source ARN.

• destination_arn (str | None) – A filter that returns only associations with the specified destination Amazon Resource Name (ARN).

• source_type (str | None) – A filter that returns only associations with the specified source type.

• destination_type (str | None) – A filter that returns only associations with the specified destination type.

• association_type (str | None) – A filter that returns only associations of the specified type.

• created_after (datetime | None) – A filter that returns only associations created on or after the specified time.

• created_before (datetime | None) – A filter that returns only associations created on or before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• next_token – If the previous call to ListAssociations didn’t return the full set of associations, the call returns a token for getting the next set of associations.

• max_results – The maximum number of associations to return in the response. The default value is 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Association resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[Association]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.AutoMLJob(*, auto_ml_job_name, auto_ml_job_arn=<sagemaker_core.main.utils.Unassigned object>, input_data_config=<sagemaker_core.main.utils.Unassigned object>, output_data_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_objective=<sagemaker_core.main.utils.Unassigned object>, problem_type=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, partial_failure_reasons=<sagemaker_core.main.utils.Unassigned object>, best_candidate=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_status=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_secondary_status=<sagemaker_core.main.utils.Unassigned object>, generate_candidate_definitions_only=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_artifacts=<sagemaker_core.main.utils.Unassigned object>, resolved_attributes=<sagemaker_core.main.utils.Unassigned object>, model_deploy_config=<sagemaker_core.main.utils.Unassigned object>, model_deploy_result=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource AutoMLJob

Parameters:

• auto_ml_job_name (str)

• auto_ml_job_arn (str | None)

• input_data_config (List[AutoMLChannel] | None)

• output_data_config (AutoMLOutputDataConfig | None)

• role_arn (str | None)

• auto_ml_job_objective (AutoMLJobObjective | None)

• problem_type (str | None)

• auto_ml_job_config (AutoMLJobConfig | None)

• creation_time (datetime | None)

• end_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• partial_failure_reasons (List[AutoMLPartialFailureReason] | None)

• best_candidate (AutoMLCandidate | None)

• auto_ml_job_status (str | None)

• auto_ml_job_secondary_status (str | None)

• generate_candidate_definitions_only (bool | None)

• auto_ml_job_artifacts (AutoMLJobArtifacts | None)

• resolved_attributes (ResolvedAttributes | None)

• model_deploy_config (ModelDeployConfig | None)

• model_deploy_result (ModelDeployResult | None)

auto_ml_job_name

Returns the name of the AutoML job.

Type:

str

auto_ml_job_arn

Returns the ARN of the AutoML job.

Type:

str | None

input_data_config

Returns the input data configuration for the AutoML job.

Type:

List[sagemaker_core.main.shapes.AutoMLChannel] | None

output_data_config

Returns the job’s output data config.

Type:

sagemaker_core.main.shapes.AutoMLOutputDataConfig | None

role_arn

The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.

Type:

str | None

creation_time

Returns the creation time of the AutoML job.

Type:

datetime.datetime | None

last_modified_time

Returns the job’s last modified time.

Type:

datetime.datetime | None

auto_ml_job_status

Returns the status of the AutoML job.

Type:

str | None

auto_ml_job_secondary_status

Returns the secondary status of the AutoML job.

Type:

str | None

auto_ml_job_objective

Returns the job’s objective.

Type:

sagemaker_core.main.shapes.AutoMLJobObjective | None

problem_type

Returns the job’s problem type.

Type:

str | None

auto_ml_job_config

Returns the configuration for the AutoML job.

Type:

sagemaker_core.main.shapes.AutoMLJobConfig | None

end_time

Returns the end time of the AutoML job.

Type:

datetime.datetime | None

failure_reason

Returns the failure reason for an AutoML job, when applicable.

Type:

str | None

partial_failure_reasons

Returns a list of reasons for partial failures within an AutoML job.

Type:

List[sagemaker_core.main.shapes.AutoMLPartialFailureReason] | None

best_candidate

The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

Type:

sagemaker_core.main.shapes.AutoMLCandidate | None

generate_candidate_definitions_only

Indicates whether the output for an AutoML job generates candidate definitions only.

Type:

bool | None

auto_ml_job_artifacts

Returns information on the job’s artifacts found in AutoMLJobArtifacts.

Type:

sagemaker_core.main.shapes.AutoMLJobArtifacts | None

resolved_attributes

Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.

Type:

sagemaker_core.main.shapes.ResolvedAttributes | None

model_deploy_config

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

Type:

sagemaker_core.main.shapes.ModelDeployConfig | None

model_deploy_result

Provides information about endpoint for the model deployment.

Type:

sagemaker_core.main.shapes.ModelDeployResult | None

classmethod create(auto_ml_job_name, input_data_config, output_data_config, role_arn, problem_type=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_objective=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_config=<sagemaker_core.main.utils.Unassigned object>, generate_candidate_definitions_only=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, model_deploy_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a AutoMLJob resource

Parameters:

• auto_ml_job_name (str) – Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

• input_data_config (List[AutoMLChannel]) – An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

• output_data_config (AutoMLOutputDataConfig) – Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

• role_arn (str) – The ARN of the role that is used to access the data.

• problem_type (str | None) – Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

• auto_ml_job_objective (AutoMLJobObjective | None) – Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

• auto_ml_job_config (AutoMLJobConfig | None) – A collection of settings used to configure an AutoML job.

• generate_candidate_definitions_only (bool | None) – Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

• model_deploy_config (ModelDeployConfig | None) – Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The AutoMLJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

AutoMLJob | None

classmethod get(auto_ml_job_name, session=None, region=None)

Get a AutoMLJob resource

Parameters:

• auto_ml_job_name (str) – Requests information about an AutoML job using its unique name.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The AutoMLJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

AutoMLJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all AutoMLJob resources

Parameters:

• creation_time_after (datetime | None) – Request a list of jobs, using a filter for time.

• creation_time_before (datetime | None) – Request a list of jobs, using a filter for time.

• last_modified_time_after (datetime | None) – Request a list of jobs, using a filter for time.

• last_modified_time_before (datetime | None) – Request a list of jobs, using a filter for time.

• name_contains (str | None) – Request a list of jobs, using a search filter for name.

• status_equals (str | None) – Request a list of jobs, using a filter for status.

• sort_order (str | None) – The sort order for the results. The default is Descending.

• sort_by (str | None) – The parameter by which to sort the results. The default is Name.

• max_results – Request a list of jobs up to a specified limit.

• next_token – If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed AutoMLJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[AutoMLJob]

get_all_candidates(status_equals=<sagemaker_core.main.utils.Unassigned object>, candidate_name_equals=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

List the candidates created for the job.

Parameters:

• status_equals (str | None) – List the candidates for the job and filter by status.

• candidate_name_equals (str | None) – List the candidates for the job and filter by candidate name.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• sort_by (str | None) – The parameter by which to sort the results. The default is Descending.

• max_results – List the job’s candidates up to a specified limit.

• next_token – If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed AutoMLCandidate.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[AutoMLCandidate]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a AutoMLJob resource

Returns:

The AutoMLJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

AutoMLJob | None

stop()

Stop a AutoMLJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None)

Wait for a AutoMLJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.AutoMLJobV2(*, auto_ml_job_name, auto_ml_job_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_input_data_config=<sagemaker_core.main.utils.Unassigned object>, output_data_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_objective=<sagemaker_core.main.utils.Unassigned object>, auto_ml_problem_type_config=<sagemaker_core.main.utils.Unassigned object>, auto_ml_problem_type_config_name=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, partial_failure_reasons=<sagemaker_core.main.utils.Unassigned object>, best_candidate=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_status=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_secondary_status=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_artifacts=<sagemaker_core.main.utils.Unassigned object>, resolved_attributes=<sagemaker_core.main.utils.Unassigned object>, model_deploy_config=<sagemaker_core.main.utils.Unassigned object>, model_deploy_result=<sagemaker_core.main.utils.Unassigned object>, data_split_config=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, auto_ml_compute_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource AutoMLJobV2

Parameters:

• auto_ml_job_name (str)

• auto_ml_job_arn (str | None)

• auto_ml_job_input_data_config (List[AutoMLJobChannel] | None)

• output_data_config (AutoMLOutputDataConfig | None)

• role_arn (str | None)

• auto_ml_job_objective (AutoMLJobObjective | None)

• auto_ml_problem_type_config (AutoMLProblemTypeConfig | None)

• auto_ml_problem_type_config_name (str | None)

• creation_time (datetime | None)

• end_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• partial_failure_reasons (List[AutoMLPartialFailureReason] | None)

• best_candidate (AutoMLCandidate | None)

• auto_ml_job_status (str | None)

• auto_ml_job_secondary_status (str | None)

• auto_ml_job_artifacts (AutoMLJobArtifacts | None)

• resolved_attributes (AutoMLResolvedAttributes | None)

• model_deploy_config (ModelDeployConfig | None)

• model_deploy_result (ModelDeployResult | None)

• data_split_config (AutoMLDataSplitConfig | None)

• security_config (AutoMLSecurityConfig | None)

• auto_ml_compute_config (AutoMLComputeConfig | None)

auto_ml_job_name

Returns the name of the AutoML job V2.

Type:

str

auto_ml_job_arn

Returns the Amazon Resource Name (ARN) of the AutoML job V2.

Type:

str | None

auto_ml_job_input_data_config

Returns an array of channel objects describing the input data and their location.

Type:

List[sagemaker_core.main.shapes.AutoMLJobChannel] | None

output_data_config

Returns the job’s output data config.

Type:

sagemaker_core.main.shapes.AutoMLOutputDataConfig | None

role_arn

The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.

Type:

str | None

creation_time

Returns the creation time of the AutoML job V2.

Type:

datetime.datetime | None

last_modified_time

Returns the job’s last modified time.

Type:

datetime.datetime | None

auto_ml_job_status

Returns the status of the AutoML job V2.

Type:

str | None

auto_ml_job_secondary_status

Returns the secondary status of the AutoML job V2.

Type:

str | None

auto_ml_job_objective

Returns the job’s objective.

Type:

sagemaker_core.main.shapes.AutoMLJobObjective | None

auto_ml_problem_type_config

Returns the configuration settings of the problem type set for the AutoML job V2.

Type:

sagemaker_core.main.shapes.AutoMLProblemTypeConfig | None

auto_ml_problem_type_config_name

Returns the name of the problem type configuration set for the AutoML job V2.

Type:

str | None

end_time

Returns the end time of the AutoML job V2.

Type:

datetime.datetime | None

failure_reason

Returns the reason for the failure of the AutoML job V2, when applicable.

Type:

str | None

partial_failure_reasons

Returns a list of reasons for partial failures within an AutoML job V2.

Type:

List[sagemaker_core.main.shapes.AutoMLPartialFailureReason] | None

best_candidate

Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.

Type:

sagemaker_core.main.shapes.AutoMLCandidate | None

auto_ml_job_artifacts

Type:

sagemaker_core.main.shapes.AutoMLJobArtifacts | None

resolved_attributes

Returns the resolved attributes used by the AutoML job V2.

Type:

sagemaker_core.main.shapes.AutoMLResolvedAttributes | None

model_deploy_config

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

Type:

sagemaker_core.main.shapes.ModelDeployConfig | None

model_deploy_result

Provides information about endpoint for the model deployment.

Type:

sagemaker_core.main.shapes.ModelDeployResult | None

data_split_config

Returns the configuration settings of how the data are split into train and validation datasets.

Type:

sagemaker_core.main.shapes.AutoMLDataSplitConfig | None

security_config

Returns the security configuration for traffic encryption or Amazon VPC settings.

Type:

sagemaker_core.main.shapes.AutoMLSecurityConfig | None

auto_ml_compute_config

The compute configuration used for the AutoML job V2.

Type:

sagemaker_core.main.shapes.AutoMLComputeConfig | None

classmethod create(auto_ml_job_name, auto_ml_job_input_data_config, output_data_config, auto_ml_problem_type_config, role_arn, tags=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_objective=<sagemaker_core.main.utils.Unassigned object>, model_deploy_config=<sagemaker_core.main.utils.Unassigned object>, data_split_config=<sagemaker_core.main.utils.Unassigned object>, auto_ml_compute_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a AutoMLJobV2 resource

Parameters:

• auto_ml_job_name (str) – Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

• auto_ml_job_input_data_config (List[AutoMLJobChannel]) – An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type: For tabular problem types: S3Prefix, ManifestFile. For image classification: S3Prefix, ManifestFile, AugmentedManifestFile. For text classification: S3Prefix. For time-series forecasting: S3Prefix. For text generation (LLMs fine-tuning): S3Prefix.

• output_data_config (AutoMLOutputDataConfig) – Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

• auto_ml_problem_type_config (AutoMLProblemTypeConfig) – Defines the configuration settings of one of the supported problem types.

• role_arn (str) – The ARN of the role that is used to access the data.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

• security_config (AutoMLSecurityConfig | None) – The security configuration for traffic encryption or Amazon VPC settings.

• auto_ml_job_objective (AutoMLJobObjective | None) – Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective. For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all. For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

• model_deploy_config (ModelDeployConfig | None) – Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

• data_split_config (AutoMLDataSplitConfig | None) – This structure specifies how to split the data into train and validation datasets. The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size. This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

• auto_ml_compute_config (AutoMLComputeConfig | None) – Specifies the compute configuration for the AutoML job V2.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The AutoMLJobV2 resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

AutoMLJobV2 | None

classmethod get(auto_ml_job_name, session=None, region=None)

Get a AutoMLJobV2 resource

Parameters:

• auto_ml_job_name (str) – Requests information about an AutoML job V2 using its unique name.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The AutoMLJobV2 resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

AutoMLJobV2 | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a AutoMLJobV2 resource

Returns:

The AutoMLJobV2 resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

AutoMLJobV2 | None

wait(poll=5, timeout=None)

Wait for a AutoMLJobV2 resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Base

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.Cluster(*, cluster_name, cluster_arn=<sagemaker_core.main.utils.Unassigned object>, cluster_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, failure_message=<sagemaker_core.main.utils.Unassigned object>, instance_groups=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, orchestrator=<sagemaker_core.main.utils.Unassigned object>, node_recovery=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Cluster

Parameters:

• cluster_name (str)

• cluster_arn (str | None)

• cluster_status (str | None)

• creation_time (datetime | None)

• failure_message (str | None)

• instance_groups (List[ClusterInstanceGroupDetails] | None)

• vpc_config (VpcConfig | None)

• orchestrator (ClusterOrchestrator | None)

• node_recovery (str | None)

cluster_arn

The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

Type:

str | None

cluster_status

The status of the SageMaker HyperPod cluster.

Type:

str | None

instance_groups

The instance groups of the SageMaker HyperPod cluster.

Type:

List[sagemaker_core.main.shapes.ClusterInstanceGroupDetails] | None

cluster_name

The name of the SageMaker HyperPod cluster.

Type:

str

creation_time

The time when the SageMaker Cluster is created.

Type:

datetime.datetime | None

failure_message

The failure message of the SageMaker HyperPod cluster.

Type:

str | None

vpc_config

Type:

sagemaker_core.main.shapes.VpcConfig | None

orchestrator

The type of orchestrator used for the SageMaker HyperPod cluster.

Type:

sagemaker_core.main.shapes.ClusterOrchestrator | None

node_recovery

The node recovery mode configured for the SageMaker HyperPod cluster.

Type:

str | None

batch_delete_nodes(node_ids, session=None, region=None)

Deletes specific nodes within a SageMaker HyperPod cluster.

Parameters:

• node_ids (List[str]) – A list of node IDs to be deleted from the specified cluster. For SageMaker HyperPod clusters using the Slurm workload manager, you cannot remove instances that are configured as Slurm controller nodes. If you need to delete more than 99 instances, contact Support for assistance.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

BatchDeleteClusterNodesResponse

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

BatchDeleteClusterNodesResponse | None

classmethod create(cluster_name, instance_groups, vpc_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, orchestrator=<sagemaker_core.main.utils.Unassigned object>, node_recovery=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Cluster resource

Parameters:

• cluster_name (str) – The name for the new SageMaker HyperPod cluster.

• instance_groups (List[ClusterInstanceGroupSpecification]) – The instance groups to be created in the SageMaker HyperPod cluster.

• vpc_config (VpcConfig | None) – Specifies the Amazon Virtual Private Cloud (VPC) that is associated with the Amazon SageMaker HyperPod cluster. You can control access to and from your resources by configuring your VPC. For more information, see Give SageMaker access to resources in your Amazon VPC. When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform: Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications. In Amazon EKS-orchestrated clusters, nodes receive dual-stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment. Additional resources for IPv6 configuration: For information about adding IPv6 support to your VPC, see to IPv6 Support for VPC. For information about creating a new IPv6-compatible VPC, see Amazon VPC Creation Guide. To configure SageMaker HyperPod with a custom Amazon VPC, see Custom Amazon VPC Setup for SageMaker HyperPod.

• tags (List[Tag] | None) – Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.

• orchestrator (ClusterOrchestrator | None) – The type of orchestrator to use for the SageMaker HyperPod cluster. Currently, the only supported value is “eks”, which is to use an Amazon Elastic Kubernetes Service (EKS) cluster as the orchestrator.

• node_recovery (str | None) – The node recovery mode for the SageMaker HyperPod cluster. When set to Automatic, SageMaker HyperPod will automatically reboot or replace faulty nodes when issues are detected. When set to None, cluster administrators will need to manually manage any faulty cluster instances.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Cluster resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Cluster | None

delete()

Delete a Cluster resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(cluster_name, session=None, region=None)

Get a Cluster resource

Parameters:

• cluster_name (str) – The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Cluster resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Cluster | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, training_plan_arn=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Cluster resources

Parameters:

• creation_time_after (datetime | None) – Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard. Acceptable formats include: YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00 YYYY-MM-DD, for example, 2014-10-01 Unix time in seconds, for example, 1412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

• creation_time_before (datetime | None) – Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

• max_results – Set the maximum number of SageMaker HyperPod clusters to list.

• name_contains (str | None) – Set the maximum number of instances to print in the list.

• next_token – Set the next token to retrieve the list of SageMaker HyperPod clusters.

• sort_by (str | None) – The field by which to sort results. The default value is CREATION_TIME.

• sort_order (str | None) – The sort order for results. The default value is Ascending.

• training_plan_arn (str | None) – The Amazon Resource Name (ARN); of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Cluster resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Cluster]

get_all_nodes(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, instance_group_name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.

Parameters:

• creation_time_after (datetime | None) – A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard. Acceptable formats include: YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00 YYYY-MM-DD, for example, 2014-10-01 Unix time in seconds, for example, 1412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

• creation_time_before (datetime | None) – A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

• instance_group_name_contains (str | None) – A filter that returns the instance groups whose name contain a specified string.

• max_results – The maximum number of nodes to return in the response.

• next_token – If the result of the previous ListClusterNodes request was truncated, the response includes a NextToken. To retrieve the next set of cluster nodes, use the token in the next request.

• sort_by (str | None) – The field by which to sort results. The default value is CREATION_TIME.

• sort_order (str | None) – The sort order for results. The default value is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ClusterNodeDetails.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[ClusterNodeDetails]

get_node(node_id, session=None, region=None)

Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.

Parameters:

• node_id (str) – The ID of the SageMaker HyperPod cluster node.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

ClusterNodeDetails

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ClusterNodeDetails | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Cluster resource

Returns:

The Cluster resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Cluster | None

update(instance_groups, node_recovery=<sagemaker_core.main.utils.Unassigned object>, instance_groups_to_delete=<sagemaker_core.main.utils.Unassigned object>)

Update a Cluster resource

Parameters:

• instance_groups_to_delete (List[str] | None) – Specify the names of the instance groups to delete. Use a single , as the separator between multiple names.

• instance_groups (List[ClusterInstanceGroupSpecification])

• node_recovery (str | None)

Returns:

The Cluster resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

Cluster | None

update_software(session=None, region=None)

Updates the platform software of a SageMaker HyperPod cluster for security patching.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait_for_delete(poll=5, timeout=None)

Wait for a Cluster resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Cluster resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'Deleting', 'Failed', 'InService', 'RollingBack', 'SystemUpdating', 'Updating']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ClusterSchedulerConfig(*, cluster_scheduler_config_id, cluster_scheduler_config_arn=<sagemaker_core.main.utils.Unassigned object>, name=<sagemaker_core.main.utils.Unassigned object>, cluster_scheduler_config_version=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, cluster_arn=<sagemaker_core.main.utils.Unassigned object>, scheduler_config=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ClusterSchedulerConfig

Parameters:

• cluster_scheduler_config_id (str)

• cluster_scheduler_config_arn (str | None)

• name (str | None)

• cluster_scheduler_config_version (int | None)

• status (str | None)

• failure_reason (str | None)

• cluster_arn (str | None)

• scheduler_config (SchedulerConfig | None)

• description (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

cluster_scheduler_config_arn

ARN of the cluster policy.

Type:

str | None

cluster_scheduler_config_id

ID of the cluster policy.

Type:

str

name

Name of the cluster policy.

Type:

str | None

cluster_scheduler_config_version

Version of the cluster policy.

Type:

int | None

status

Status of the cluster policy.

Type:

str | None

creation_time

Creation time of the cluster policy.

Type:

datetime.datetime | None

failure_reason

Failure reason of the cluster policy.

Type:

str | None

cluster_arn

ARN of the cluster where the cluster policy is applied.

Type:

str | None

scheduler_config

Cluster policy configuration. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.

Type:

sagemaker_core.main.shapes.SchedulerConfig | None

description

Description of the cluster policy.

Type:

str | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

Last modified time of the cluster policy.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod create(name, cluster_arn, scheduler_config, description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ClusterSchedulerConfig resource

Parameters:

• name (str) – Name for the cluster policy.

• cluster_arn (str) – ARN of the cluster.

• scheduler_config (SchedulerConfig) – Configuration about the monitoring schedule.

• description (str | None) – Description of the cluster policy.

• tags (List[Tag] | None) – Tags of the cluster policy.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ClusterSchedulerConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ClusterSchedulerConfig | None

delete()

Delete a ClusterSchedulerConfig resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(cluster_scheduler_config_id, cluster_scheduler_config_version=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a ClusterSchedulerConfig resource

Parameters:

• cluster_scheduler_config_id (str) – ID of the cluster policy.

• cluster_scheduler_config_version (int | None) – Version of the cluster policy.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ClusterSchedulerConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ClusterSchedulerConfig | None

classmethod get_all(created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, cluster_arn=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ClusterSchedulerConfig resources

Parameters:

• created_after (datetime | None) – Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

• created_before (datetime | None) – Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

• name_contains (str | None) – Filter for name containing this string.

• cluster_arn (str | None) – Filter for ARN of the cluster.

• status (str | None) – Filter for status.

• sort_by (str | None) – Filter for sorting the list by a given value. For example, sort by name, creation time, or status.

• sort_order (str | None) – The order of the list. By default, listed in Descending order according to by SortBy. To change the list order, you can specify SortOrder to be Ascending.

• next_token – If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• max_results – The maximum number of cluster policies to list.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ClusterSchedulerConfig resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ClusterSchedulerConfig]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ClusterSchedulerConfig resource

Returns:

The ClusterSchedulerConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ClusterSchedulerConfig | None

update(target_version, scheduler_config=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>)

Update a ClusterSchedulerConfig resource

Parameters:

• target_version (int) – Target version.

• scheduler_config (SchedulerConfig | None)

• description (str | None)

Returns:

The ClusterSchedulerConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

ClusterSchedulerConfig | None

wait_for_delete(poll=5, timeout=None)

Wait for a ClusterSchedulerConfig resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a ClusterSchedulerConfig resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'CreateFailed', 'CreateRollbackFailed', 'Created', 'Updating', 'UpdateFailed', 'UpdateRollbackFailed', 'Updated', 'Deleting', 'DeleteFailed', 'DeleteRollbackFailed', 'Deleted']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.CodeRepository(*, code_repository_name, code_repository_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, git_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource CodeRepository

Parameters:

• code_repository_name (str)

• code_repository_arn (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• git_config (GitConfig | None)

code_repository_name

The name of the Git repository.

Type:

str

code_repository_arn

The Amazon Resource Name (ARN) of the Git repository.

Type:

str | None

creation_time

The date and time that the repository was created.

Type:

datetime.datetime | None

last_modified_time

The date and time that the repository was last changed.

Type:

datetime.datetime | None

git_config

Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.

Type:

sagemaker_core.main.shapes.GitConfig | None

classmethod create(code_repository_name, git_config, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a CodeRepository resource

Parameters:

• code_repository_name (str) – The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

• git_config (GitConfig) – Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The CodeRepository resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

CodeRepository | None

delete()

Delete a CodeRepository resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(code_repository_name, session=None, region=None)

Get a CodeRepository resource

Parameters:

• code_repository_name (str) – The name of the Git repository to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The CodeRepository resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

CodeRepository | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Gets a list of the Git repositories in your account.

Parameters:

• creation_time_after (datetime | None) – A filter that returns only Git repositories that were created after the specified time.

• creation_time_before (datetime | None) – A filter that returns only Git repositories that were created before the specified time.

• last_modified_time_after (datetime | None) – A filter that returns only Git repositories that were last modified after the specified time.

• last_modified_time_before (datetime | None) – A filter that returns only Git repositories that were last modified before the specified time.

• max_results – The maximum number of Git repositories to return in the response.

• name_contains (str | None) – A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

• next_token – If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

• sort_by (str | None) – The field to sort results by. The default is Name.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed CodeRepository.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[CodeRepository]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a CodeRepository resource

Returns:

The CodeRepository resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

CodeRepository | None

update(git_config=<sagemaker_core.main.utils.Unassigned object>)

Update a CodeRepository resource

Returns:

The CodeRepository resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Parameters:

git_config (GitConfigForUpdate | None)

Return type:

CodeRepository | None

class sagemaker_core.main.resources.CompilationJob(*, compilation_job_name, compilation_job_arn=<sagemaker_core.main.utils.Unassigned object>, compilation_job_status=<sagemaker_core.main.utils.Unassigned object>, compilation_start_time=<sagemaker_core.main.utils.Unassigned object>, compilation_end_time=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, inference_image=<sagemaker_core.main.utils.Unassigned object>, model_package_version_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, model_artifacts=<sagemaker_core.main.utils.Unassigned object>, model_digests=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, input_config=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, derived_information=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource CompilationJob

Parameters:

• compilation_job_name (str)

• compilation_job_arn (str | None)

• compilation_job_status (str | None)

• compilation_start_time (datetime | None)

• compilation_end_time (datetime | None)

• stopping_condition (StoppingCondition | None)

• inference_image (str | None)

• model_package_version_arn (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• model_artifacts (ModelArtifacts | None)

• model_digests (ModelDigests | None)

• role_arn (str | None)

• input_config (InputConfig | None)

• output_config (OutputConfig | None)

• vpc_config (NeoVpcConfig | None)

• derived_information (DerivedInformation | None)

compilation_job_name

The name of the model compilation job.

Type:

str

compilation_job_arn

The Amazon Resource Name (ARN) of the model compilation job.

Type:

str | None

compilation_job_status

The status of the model compilation job.

Type:

str | None

stopping_condition

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.

Type:

sagemaker_core.main.shapes.StoppingCondition | None

creation_time

The time that the model compilation job was created.

Type:

datetime.datetime | None

last_modified_time

The time that the status of the model compilation job was last modified.

Type:

datetime.datetime | None

failure_reason

If a model compilation job failed, the reason it failed.

Type:

str | None

model_artifacts

Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

Type:

sagemaker_core.main.shapes.ModelArtifacts | None

role_arn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI assumes to perform the model compilation job.

Type:

str | None

input_config

Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

Type:

sagemaker_core.main.shapes.InputConfig | None

output_config

Information about the output location for the compiled model and the target device that the model runs on.

Type:

sagemaker_core.main.shapes.OutputConfig | None

compilation_start_time

The time when the model compilation job started the CompilationJob instances. You are billed for the time between this timestamp and the timestamp in the CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That’s because it takes time to download the compilation job, which depends on the size of the compilation job container.

Type:

datetime.datetime | None

compilation_end_time

The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job’s model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker AI detected that the job failed.

Type:

datetime.datetime | None

inference_image

The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.

Type:

str | None

model_package_version_arn

The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.

Type:

str | None

model_digests

Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.

Type:

sagemaker_core.main.shapes.ModelDigests | None

vpc_config

A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

Type:

sagemaker_core.main.shapes.NeoVpcConfig | None

derived_information

Information that SageMaker Neo automatically derived about the model.

Type:

sagemaker_core.main.shapes.DerivedInformation | None

classmethod create(compilation_job_name, role_arn, output_config, stopping_condition, model_package_version_arn=<sagemaker_core.main.utils.Unassigned object>, input_config=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a CompilationJob resource

Parameters:

• compilation_job_name (str) – A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf. During model compilation, Amazon SageMaker AI needs your permission to: Read input data from an S3 bucket Write model artifacts to an S3 bucket Write logs to Amazon CloudWatch Logs Publish metrics to Amazon CloudWatch You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

• output_config (OutputConfig) – Provides information about the output location for the compiled model and the target device the model runs on.

• stopping_condition (StoppingCondition) – Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.

• model_package_version_arn (str | None) – The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

• input_config (InputConfig | None) – Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

• vpc_config (NeoVpcConfig | None) – A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The CompilationJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

CompilationJob | None

delete()

Delete a CompilationJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(compilation_job_name, session=None, region=None)

Get a CompilationJob resource

Parameters:

• compilation_job_name (str) – The name of the model compilation job that you want information about.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The CompilationJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

CompilationJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all CompilationJob resources

Parameters:

• next_token – If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.

• max_results – The maximum number of model compilation jobs to return in the response.

• creation_time_after (datetime | None) – A filter that returns the model compilation jobs that were created after a specified time.

• creation_time_before (datetime | None) – A filter that returns the model compilation jobs that were created before a specified time.

• last_modified_time_after (datetime | None) – A filter that returns the model compilation jobs that were modified after a specified time.

• last_modified_time_before (datetime | None) – A filter that returns the model compilation jobs that were modified before a specified time.

• name_contains (str | None) – A filter that returns the model compilation jobs whose name contains a specified string.

• status_equals (str | None) – A filter that retrieves model compilation jobs with a specific CompilationJobStatus status.

• sort_by (str | None) – The field by which to sort results. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed CompilationJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[CompilationJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a CompilationJob resource

Returns:

The CompilationJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

CompilationJob | None

stop()

Stop a CompilationJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None)

Wait for a CompilationJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ComputeQuota(*, compute_quota_id, compute_quota_arn=<sagemaker_core.main.utils.Unassigned object>, name=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, compute_quota_version=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, cluster_arn=<sagemaker_core.main.utils.Unassigned object>, compute_quota_config=<sagemaker_core.main.utils.Unassigned object>, compute_quota_target=<sagemaker_core.main.utils.Unassigned object>, activation_state=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ComputeQuota

Parameters:

• compute_quota_id (str)

• compute_quota_arn (str | None)

• name (str | None)

• description (str | None)

• compute_quota_version (int | None)

• status (str | None)

• failure_reason (str | None)

• cluster_arn (str | None)

• compute_quota_config (ComputeQuotaConfig | None)

• compute_quota_target (ComputeQuotaTarget | None)

• activation_state (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

compute_quota_arn

ARN of the compute allocation definition.

Type:

str | None

compute_quota_id

ID of the compute allocation definition.

Type:

str

name

Name of the compute allocation definition.

Type:

str | None

compute_quota_version

Version of the compute allocation definition.

Type:

int | None

status

Status of the compute allocation definition.

Type:

str | None

compute_quota_target

The target entity to allocate compute resources to.

Type:

sagemaker_core.main.shapes.ComputeQuotaTarget | None

creation_time

Creation time of the compute allocation configuration.

Type:

datetime.datetime | None

description

Description of the compute allocation definition.

Type:

str | None

failure_reason

Failure reason of the compute allocation definition.

Type:

str | None

cluster_arn

ARN of the cluster.

Type:

str | None

compute_quota_config

Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

Type:

sagemaker_core.main.shapes.ComputeQuotaConfig | None

activation_state

The state of the compute allocation being described. Use to enable or disable compute allocation. Default is Enabled.

Type:

str | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

Last modified time of the compute allocation configuration.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod create(name, cluster_arn, compute_quota_config, compute_quota_target, description=<sagemaker_core.main.utils.Unassigned object>, activation_state=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ComputeQuota resource

Parameters:

• name (str) – Name to the compute allocation definition.

• cluster_arn (str) – ARN of the cluster.

• compute_quota_config (ComputeQuotaConfig) – Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

• compute_quota_target (ComputeQuotaTarget) – The target entity to allocate compute resources to.

• description (str | None) – Description of the compute allocation definition.

• activation_state (str | None) – The state of the compute allocation being described. Use to enable or disable compute allocation. Default is Enabled.

• tags (List[Tag] | None) – Tags of the compute allocation definition.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ComputeQuota resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ComputeQuota | None

delete()

Delete a ComputeQuota resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(compute_quota_id, compute_quota_version=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a ComputeQuota resource

Parameters:

• compute_quota_id (str) – ID of the compute allocation definition.

• compute_quota_version (int | None) – Version of the compute allocation definition.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ComputeQuota resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ComputeQuota | None

classmethod get_all(created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, cluster_arn=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ComputeQuota resources

Parameters:

• created_after (datetime | None) – Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

• created_before (datetime | None) – Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

• name_contains (str | None) – Filter for name containing this string.

• status (str | None) – Filter for status.

• cluster_arn (str | None) – Filter for ARN of the cluster.

• sort_by (str | None) – Filter for sorting the list by a given value. For example, sort by name, creation time, or status.

• sort_order (str | None) – The order of the list. By default, listed in Descending order according to by SortBy. To change the list order, you can specify SortOrder to be Ascending.

• next_token – If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• max_results – The maximum number of compute allocation definitions to list.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ComputeQuota resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ComputeQuota]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ComputeQuota resource

Returns:

The ComputeQuota resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ComputeQuota | None

update(target_version, compute_quota_config=<sagemaker_core.main.utils.Unassigned object>, compute_quota_target=<sagemaker_core.main.utils.Unassigned object>, activation_state=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>)

Update a ComputeQuota resource

Parameters:

• target_version (int) – Target version.

• compute_quota_config (ComputeQuotaConfig | None)

• compute_quota_target (ComputeQuotaTarget | None)

• activation_state (str | None)

• description (str | None)

Returns:

The ComputeQuota resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

ComputeQuota | None

wait_for_delete(poll=5, timeout=None)

Wait for a ComputeQuota resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a ComputeQuota resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'CreateFailed', 'CreateRollbackFailed', 'Created', 'Updating', 'UpdateFailed', 'UpdateRollbackFailed', 'Updated', 'Deleting', 'DeleteFailed', 'DeleteRollbackFailed', 'Deleted']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Context(*, context_name, context_arn=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, context_type=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Context

Parameters:

• context_name (str)

• context_arn (str | None)

• source (ContextSource | None)

• context_type (str | None)

• description (str | None)

• properties (Dict[str, str] | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• lineage_group_arn (str | None)

context_name

The name of the context.

Type:

str

context_arn

The Amazon Resource Name (ARN) of the context.

Type:

str | None

source

The source of the context.

Type:

sagemaker_core.main.shapes.ContextSource | None

context_type

The type of the context.

Type:

str | None

description

The description of the context.

Type:

str | None

properties

A list of the context’s properties.

Type:

Dict[str, str] | None

creation_time

When the context was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

When the context was last modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

lineage_group_arn

The Amazon Resource Name (ARN) of the lineage group.

Type:

str | None

classmethod create(context_name, source, context_type, description=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Context resource

Parameters:

• context_name (str) – The name of the context. Must be unique to your account in an Amazon Web Services Region.

• source (ContextSource) – The source type, ID, and URI.

• context_type (str) – The context type.

• description (str | None) – The description of the context.

• properties (Dict[str, str] | None) – A list of properties to add to the context.

• tags (List[Tag] | None) – A list of tags to apply to the context.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Context resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Context | None

delete()

Delete a Context resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(context_name, session=None, region=None)

Get a Context resource

Parameters:

• context_name (str) – The name of the context to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Context resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Context | None

classmethod get_all(source_uri=<sagemaker_core.main.utils.Unassigned object>, context_type=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Context resources

Parameters:

• source_uri (str | None) – A filter that returns only contexts with the specified source URI.

• context_type (str | None) – A filter that returns only contexts of the specified type.

• created_after (datetime | None) – A filter that returns only contexts created on or after the specified time.

• created_before (datetime | None) – A filter that returns only contexts created on or before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• next_token – If the previous call to ListContexts didn’t return the full set of contexts, the call returns a token for getting the next set of contexts.

• max_results – The maximum number of contexts to return in the response. The default value is 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Context resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[Context]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Context resource

Returns:

The Context resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Context | None

update(description=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>, properties_to_remove=<sagemaker_core.main.utils.Unassigned object>)

Update a Context resource

Parameters:

• properties_to_remove (List[str] | None) – A list of properties to remove.

• description (str | None)

• properties (Dict[str, str] | None)

Returns:

The Context resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

Context | None

class sagemaker_core.main.resources.DataQualityJobDefinition(*, job_definition_name, job_definition_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, data_quality_baseline_config=<sagemaker_core.main.utils.Unassigned object>, data_quality_app_specification=<sagemaker_core.main.utils.Unassigned object>, data_quality_job_input=<sagemaker_core.main.utils.Unassigned object>, data_quality_job_output_config=<sagemaker_core.main.utils.Unassigned object>, job_resources=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource DataQualityJobDefinition

Parameters:

• job_definition_name (str)

• job_definition_arn (str | None)

• creation_time (datetime | None)

• data_quality_baseline_config (DataQualityBaselineConfig | None)

• data_quality_app_specification (DataQualityAppSpecification | None)

• data_quality_job_input (DataQualityJobInput | None)

• data_quality_job_output_config (MonitoringOutputConfig | None)

• job_resources (MonitoringResources | None)

• network_config (MonitoringNetworkConfig | None)

• role_arn (str | None)

• stopping_condition (MonitoringStoppingCondition | None)

job_definition_arn

The Amazon Resource Name (ARN) of the data quality monitoring job definition.

Type:

str | None

job_definition_name

The name of the data quality monitoring job definition.

Type:

str

creation_time

The time that the data quality monitoring job definition was created.

Type:

datetime.datetime | None

data_quality_app_specification

Information about the container that runs the data quality monitoring job.

Type:

sagemaker_core.main.shapes.DataQualityAppSpecification | None

data_quality_job_input

The list of inputs for the data quality monitoring job. Currently endpoints are supported.

Type:

sagemaker_core.main.shapes.DataQualityJobInput | None

data_quality_job_output_config

Type:

sagemaker_core.main.shapes.MonitoringOutputConfig | None

job_resources

Type:

sagemaker_core.main.shapes.MonitoringResources | None

role_arn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

Type:

str | None

data_quality_baseline_config

The constraints and baselines for the data quality monitoring job definition.

Type:

sagemaker_core.main.shapes.DataQualityBaselineConfig | None

network_config

The networking configuration for the data quality monitoring job.

Type:

sagemaker_core.main.shapes.MonitoringNetworkConfig | None

stopping_condition

Type:

sagemaker_core.main.shapes.MonitoringStoppingCondition | None

classmethod create(job_definition_name, data_quality_app_specification, data_quality_job_input, data_quality_job_output_config, job_resources, role_arn, data_quality_baseline_config=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a DataQualityJobDefinition resource

Parameters:

• job_definition_name (str) – The name for the monitoring job definition.

• data_quality_app_specification (DataQualityAppSpecification) – Specifies the container that runs the monitoring job.

• data_quality_job_input (DataQualityJobInput) – A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.

• data_quality_job_output_config (MonitoringOutputConfig)

• job_resources (MonitoringResources)

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

• data_quality_baseline_config (DataQualityBaselineConfig | None) – Configures the constraints and baselines for the monitoring job.

• network_config (MonitoringNetworkConfig | None) – Specifies networking configuration for the monitoring job.

• stopping_condition (MonitoringStoppingCondition | None)

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The DataQualityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

DataQualityJobDefinition | None

delete()

Delete a DataQualityJobDefinition resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(job_definition_name, session=None, region=None)

Get a DataQualityJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the data quality monitoring job definition to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The DataQualityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

DataQualityJobDefinition | None

classmethod get_all(endpoint_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all DataQualityJobDefinition resources

Parameters:

• endpoint_name (str | None) – A filter that lists the data quality job definitions associated with the specified endpoint.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – Whether to sort the results in Ascending or Descending order. The default is Descending.

• next_token – If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.&gt;

• max_results – The maximum number of data quality monitoring job definitions to return in the response.

• name_contains (str | None) – A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only data quality monitoring job definitions created before the specified time.

• creation_time_after (datetime | None) – A filter that returns only data quality monitoring job definitions created after the specified time.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed DataQualityJobDefinition resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[DataQualityJobDefinition]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a DataQualityJobDefinition resource

Returns:

The DataQualityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

DataQualityJobDefinition | None

class sagemaker_core.main.resources.Device(*, device_name, device_fleet_name, device_arn=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, iot_thing_name=<sagemaker_core.main.utils.Unassigned object>, registration_time=<sagemaker_core.main.utils.Unassigned object>, latest_heartbeat=<sagemaker_core.main.utils.Unassigned object>, models=<sagemaker_core.main.utils.Unassigned object>, max_models=<sagemaker_core.main.utils.Unassigned object>, next_token=<sagemaker_core.main.utils.Unassigned object>, agent_version=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Device

Parameters:

• device_name (str)

• device_fleet_name (str)

• device_arn (str | None)

• description (str | None)

• iot_thing_name (str | None)

• registration_time (datetime | None)

• latest_heartbeat (datetime | None)

• models (List[EdgeModel] | None)

• max_models (int | None)

• next_token (str | None)

• agent_version (str | None)

device_name

The unique identifier of the device.

Type:

str

device_fleet_name

The name of the fleet the device belongs to.

Type:

str

registration_time

The timestamp of the last registration or de-reregistration.

Type:

datetime.datetime | None

device_arn

The Amazon Resource Name (ARN) of the device.

Type:

str | None

description

A description of the device.

Type:

str | None

iot_thing_name

The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.

Type:

str | None

latest_heartbeat

The last heartbeat received from the device.

Type:

datetime.datetime | None

models

Models on the device.

Type:

List[sagemaker_core.main.shapes.EdgeModel] | None

max_models

The maximum number of models.

Type:

int | None

next_token

The response from the last list when returning a list large enough to need tokening.

Type:

str | None

agent_version

Edge Manager agent version.

Type:

str | None

classmethod get(device_name, device_fleet_name, next_token=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a Device resource

Parameters:

• device_name (str) – The unique ID of the device.

• device_fleet_name (str) – The name of the fleet the devices belong to.

• next_token (str | None) – Next token of device description.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Device resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Device | None

classmethod get_all(latest_heartbeat_after=<sagemaker_core.main.utils.Unassigned object>, model_name=<sagemaker_core.main.utils.Unassigned object>, device_fleet_name=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Device resources

Parameters:

• next_token – The response from the last list when returning a list large enough to need tokening.

• max_results – Maximum number of results to select.

• latest_heartbeat_after (datetime | None) – Select fleets where the job was updated after X

• model_name (str | None) – A filter that searches devices that contains this name in any of their models.

• device_fleet_name (str | None) – Filter for fleets containing this name in their device fleet name.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Device resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Device]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Device resource

Returns:

The Device resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Device | None

class sagemaker_core.main.resources.DeviceFleet(*, device_fleet_name, device_fleet_arn=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, iot_role_alias=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource DeviceFleet

Parameters:

• device_fleet_name (str)

• device_fleet_arn (str | None)

• output_config (EdgeOutputConfig | None)

• description (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• role_arn (str | None)

• iot_role_alias (str | None)

device_fleet_name

The name of the fleet.

Type:

str

device_fleet_arn

The The Amazon Resource Name (ARN) of the fleet.

Type:

str | None

output_config

The output configuration for storing sampled data.

Type:

sagemaker_core.main.shapes.EdgeOutputConfig | None

creation_time

Timestamp of when the device fleet was created.

Type:

datetime.datetime | None

last_modified_time

Timestamp of when the device fleet was last updated.

Type:

datetime.datetime | None

description

A description of the fleet.

Type:

str | None

role_arn

The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).

Type:

str | None

iot_role_alias

The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).

Type:

str | None

classmethod create(device_fleet_name, output_config, role_arn=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, enable_iot_role_alias=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a DeviceFleet resource

Parameters:

• device_fleet_name (str) – The name of the fleet that the device belongs to.

• output_config (EdgeOutputConfig) – The output configuration for storing sample data collected by the fleet.

• role_arn (str | None) – The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).

• description (str | None) – A description of the fleet.

• tags (List[Tag] | None) – Creates tags for the specified fleet.

• enable_iot_role_alias (bool | None) – Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: “SageMakerEdge-{DeviceFleetName}”. For example, if your device fleet is called “demo-fleet”, the name of the role alias will be “SageMakerEdge-demo-fleet”.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The DeviceFleet resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

DeviceFleet | None

delete()

Delete a DeviceFleet resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

Return type:

None

deregister_devices(device_names, session=None, region=None)

Deregisters the specified devices.

Parameters:

• device_names (List[str]) – The unique IDs of the devices.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(device_fleet_name, session=None, region=None)

Get a DeviceFleet resource

Parameters:

• device_fleet_name (str) – The name of the fleet.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The DeviceFleet resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

DeviceFleet | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all DeviceFleet resources

Parameters:

• next_token – The response from the last list when returning a list large enough to need tokening.

• max_results – The maximum number of results to select.

• creation_time_after (datetime | None) – Filter fleets where packaging job was created after specified time.

• creation_time_before (datetime | None) – Filter fleets where the edge packaging job was created before specified time.

• last_modified_time_after (datetime | None) – Select fleets where the job was updated after X

• last_modified_time_before (datetime | None) – Select fleets where the job was updated before X

• name_contains (str | None) – Filter for fleets containing this name in their fleet device name.

• sort_by (str | None) – The column to sort by.

• sort_order (str | None) – What direction to sort in.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed DeviceFleet resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[DeviceFleet]

get_report(session=None, region=None)

Describes a fleet.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

GetDeviceFleetReportResponse

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

GetDeviceFleetReportResponse | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a DeviceFleet resource

Returns:

The DeviceFleet resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

DeviceFleet | None

register_devices(devices, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Register devices.

Parameters:

• devices (List[Device]) – A list of devices to register with SageMaker Edge Manager.

• tags (List[Tag] | None) – The tags associated with devices.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

None

update(output_config, role_arn=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, enable_iot_role_alias=<sagemaker_core.main.utils.Unassigned object>)

Update a DeviceFleet resource

Parameters:

• enable_iot_role_alias (bool | None) – Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: “SageMakerEdge-{DeviceFleetName}”. For example, if your device fleet is called “demo-fleet”, the name of the role alias will be “SageMakerEdge-demo-fleet”.

• output_config (EdgeOutputConfig)

• role_arn (str | None)

• description (str | None)

Returns:

The DeviceFleet resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

Return type:

DeviceFleet | None

update_devices(devices, session=None, region=None)

Updates one or more devices in a fleet.

Parameters:

• devices (List[Device]) – List of devices to register with Edge Manager agent.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

class sagemaker_core.main.resources.Domain(*, domain_id, domain_arn=<sagemaker_core.main.utils.Unassigned object>, domain_name=<sagemaker_core.main.utils.Unassigned object>, home_efs_file_system_id=<sagemaker_core.main.utils.Unassigned object>, single_sign_on_managed_application_instance_id=<sagemaker_core.main.utils.Unassigned object>, single_sign_on_application_arn=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, security_group_id_for_domain_boundary=<sagemaker_core.main.utils.Unassigned object>, auth_mode=<sagemaker_core.main.utils.Unassigned object>, default_user_settings=<sagemaker_core.main.utils.Unassigned object>, domain_settings=<sagemaker_core.main.utils.Unassigned object>, app_network_access_type=<sagemaker_core.main.utils.Unassigned object>, home_efs_file_system_kms_key_id=<sagemaker_core.main.utils.Unassigned object>, subnet_ids=<sagemaker_core.main.utils.Unassigned object>, url=<sagemaker_core.main.utils.Unassigned object>, vpc_id=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, app_security_group_management=<sagemaker_core.main.utils.Unassigned object>, tag_propagation=<sagemaker_core.main.utils.Unassigned object>, default_space_settings=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Domain

Parameters:

• domain_id (str)

• domain_arn (str | None)

• domain_name (str | None)

• home_efs_file_system_id (str | None)

• single_sign_on_managed_application_instance_id (str | None)

• single_sign_on_application_arn (str | None)

• status (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• security_group_id_for_domain_boundary (str | None)

• auth_mode (str | None)

• default_user_settings (UserSettings | None)

• domain_settings (DomainSettings | None)

• app_network_access_type (str | None)

• home_efs_file_system_kms_key_id (str | None)

• subnet_ids (List[str] | None)

• url (str | None)

• vpc_id (str | None)

• kms_key_id (str | None)

• app_security_group_management (str | None)

• tag_propagation (str | None)

• default_space_settings (DefaultSpaceSettings | None)

domain_arn

The domain’s Amazon Resource Name (ARN).

Type:

str | None

domain_id

The domain ID.

Type:

str

domain_name

The domain name.

Type:

str | None

home_efs_file_system_id

The ID of the Amazon Elastic File System managed by this Domain.

Type:

str | None

single_sign_on_managed_application_instance_id

The IAM Identity Center managed application instance ID.

Type:

str | None

single_sign_on_application_arn

The ARN of the application managed by SageMaker AI in IAM Identity Center. This value is only returned for domains created after October 1, 2023.

Type:

str | None

status

The status.

Type:

str | None

creation_time

The creation time.

Type:

datetime.datetime | None

last_modified_time

The last modified time.

Type:

datetime.datetime | None

failure_reason

The failure reason.

Type:

str | None

security_group_id_for_domain_boundary

The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.

Type:

str | None

auth_mode

The domain’s authentication mode.

Type:

str | None

default_user_settings

Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.

Type:

sagemaker_core.main.shapes.UserSettings | None

domain_settings

A collection of Domain settings.

Type:

sagemaker_core.main.shapes.DomainSettings | None

app_network_access_type

Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly. PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access VpcOnly - All traffic is through the specified VPC and subnets

Type:

str | None

home_efs_file_system_kms_key_id

Use KmsKeyId.

Type:

str | None

subnet_ids

The VPC subnets that the domain uses for communication.

Type:

List[str] | None

url

The domain’s URL.

Type:

str | None

vpc_id

The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

Type:

str | None

kms_key_id

The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.

Type:

str | None

app_security_group_management

The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.

Type:

str | None

tag_propagation

Indicates whether custom tag propagation is supported for the domain.

Type:

str | None

default_space_settings

The default settings for shared spaces that users create in the domain.

Type:

sagemaker_core.main.shapes.DefaultSpaceSettings | None

classmethod create(domain_name, auth_mode, default_user_settings, subnet_ids, vpc_id, domain_settings=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, app_network_access_type=<sagemaker_core.main.utils.Unassigned object>, home_efs_file_system_kms_key_id=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, app_security_group_management=<sagemaker_core.main.utils.Unassigned object>, tag_propagation=<sagemaker_core.main.utils.Unassigned object>, default_space_settings=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Domain resource

Parameters:

• domain_name (str) – A name for the domain.

• auth_mode (str) – The mode of authentication that members use to access the domain.

• default_user_settings (UserSettings) – The default settings to use to create a user profile when UserSettings isn’t specified in the call to the CreateUserProfile API. SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.

• subnet_ids (List[str]) – The VPC subnets that the domain uses for communication.

• vpc_id (str) – The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

• domain_settings (DomainSettings | None) – A collection of Domain settings.

• tags (List[Tag] | None) – Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API. Tags that you specify for the Domain are also added to all Apps that the Domain launches.

• app_network_access_type (str | None) – Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly. PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access VpcOnly - All traffic is through the specified VPC and subnets

• home_efs_file_system_kms_key_id (str | None) – Use KmsKeyId.

• kms_key_id (str | None) – SageMaker AI uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.

• app_security_group_management (str | None) – The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.

• tag_propagation (str | None) – Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.

• default_space_settings (DefaultSpaceSettings | None) – The default settings for shared spaces that users create in the domain.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Domain resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Domain | None

delete(retention_policy=<sagemaker_core.main.utils.Unassigned object>)

Delete a Domain resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Parameters:

retention_policy (RetentionPolicy | None)

Return type:

None

classmethod get(domain_id, session=None, region=None)

Get a Domain resource

Parameters:

• domain_id (str) – The domain ID.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Domain resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Domain | None

classmethod get_all(session=None, region=None)

Get all Domain resources.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Domain resources.

Return type:

ResourceIterator[Domain]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Domain resource

Returns:

The Domain resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Domain | None

update(default_user_settings=<sagemaker_core.main.utils.Unassigned object>, domain_settings_for_update=<sagemaker_core.main.utils.Unassigned object>, app_security_group_management=<sagemaker_core.main.utils.Unassigned object>, default_space_settings=<sagemaker_core.main.utils.Unassigned object>, subnet_ids=<sagemaker_core.main.utils.Unassigned object>, app_network_access_type=<sagemaker_core.main.utils.Unassigned object>, tag_propagation=<sagemaker_core.main.utils.Unassigned object>)

Update a Domain resource

Parameters:

• domain_settings_for_update (DomainSettingsForUpdate | None) – A collection of DomainSettings configuration values to update.

• default_user_settings (UserSettings | None)

• app_security_group_management (str | None)

• default_space_settings (DefaultSpaceSettings | None)

• subnet_ids (List[str] | None)

• app_network_access_type (str | None)

• tag_propagation (str | None)

Returns:

The Domain resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

Domain | None

wait_for_delete(poll=5, timeout=None)

Wait for a Domain resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Domain resource to reach certain status.

Parameters:

• target_status (Literal['Deleting', 'Failed', 'InService', 'Pending', 'Updating', 'Update_Failed', 'Delete_Failed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.EdgeDeploymentPlan(*, edge_deployment_plan_name, edge_deployment_plan_arn=<sagemaker_core.main.utils.Unassigned object>, model_configs=<sagemaker_core.main.utils.Unassigned object>, device_fleet_name=<sagemaker_core.main.utils.Unassigned object>, edge_deployment_success=<sagemaker_core.main.utils.Unassigned object>, edge_deployment_pending=<sagemaker_core.main.utils.Unassigned object>, edge_deployment_failed=<sagemaker_core.main.utils.Unassigned object>, stages=<sagemaker_core.main.utils.Unassigned object>, next_token=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource EdgeDeploymentPlan

Parameters:

• edge_deployment_plan_name (str)

• edge_deployment_plan_arn (str | None)

• model_configs (List[EdgeDeploymentModelConfig] | None)

• device_fleet_name (str | None)

• edge_deployment_success (int | None)

• edge_deployment_pending (int | None)

• edge_deployment_failed (int | None)

• stages (List[DeploymentStageStatusSummary] | None)

• next_token (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

edge_deployment_plan_arn

The ARN of edge deployment plan.

Type:

str | None

edge_deployment_plan_name

The name of the edge deployment plan.

Type:

str

model_configs

List of models associated with the edge deployment plan.

Type:

List[sagemaker_core.main.shapes.EdgeDeploymentModelConfig] | None

device_fleet_name

The device fleet used for this edge deployment plan.

Type:

str | None

stages

List of stages in the edge deployment plan.

Type:

List[sagemaker_core.main.shapes.DeploymentStageStatusSummary] | None

edge_deployment_success

The number of edge devices with the successful deployment.

Type:

int | None

edge_deployment_pending

The number of edge devices yet to pick up deployment, or in progress.

Type:

int | None

edge_deployment_failed

The number of edge devices that failed the deployment.

Type:

int | None

next_token

Token to use when calling the next set of stages in the edge deployment plan.

Type:

str | None

creation_time

The time when the edge deployment plan was created.

Type:

datetime.datetime | None

last_modified_time

The time when the edge deployment plan was last updated.

Type:

datetime.datetime | None

classmethod create(edge_deployment_plan_name, model_configs, device_fleet_name, stages=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a EdgeDeploymentPlan resource

Parameters:

• edge_deployment_plan_name (str) – The name of the edge deployment plan.

• model_configs (List[EdgeDeploymentModelConfig]) – List of models associated with the edge deployment plan.

• device_fleet_name (str | object) – The device fleet used for this edge deployment plan.

• stages (List[DeploymentStage] | None) – List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.

• tags (List[Tag] | None) – List of tags with which to tag the edge deployment plan.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The EdgeDeploymentPlan resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

EdgeDeploymentPlan | None

create_stage(session=None, region=None)

Creates a new stage in an existing edge deployment plan.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

None

delete()

Delete a EdgeDeploymentPlan resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

Return type:

None

delete_stage(stage_name, session=None, region=None)

Delete a stage in an edge deployment plan if (and only if) the stage is inactive.

Parameters:

• stage_name (str) – The name of the stage.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

Return type:

None

classmethod get(edge_deployment_plan_name, next_token=<sagemaker_core.main.utils.Unassigned object>, max_results=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a EdgeDeploymentPlan resource

Parameters:

• edge_deployment_plan_name (str) – The name of the deployment plan to describe.

• next_token (str | None) – If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.

• max_results (int | None) – The maximum number of results to select (50 by default).

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The EdgeDeploymentPlan resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

EdgeDeploymentPlan | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, device_fleet_name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all EdgeDeploymentPlan resources

Parameters:

• next_token – The response from the last list when returning a list large enough to need tokening.

• max_results – The maximum number of results to select (50 by default).

• creation_time_after (datetime | None) – Selects edge deployment plans created after this time.

• creation_time_before (datetime | None) – Selects edge deployment plans created before this time.

• last_modified_time_after (datetime | None) – Selects edge deployment plans that were last updated after this time.

• last_modified_time_before (datetime | None) – Selects edge deployment plans that were last updated before this time.

• name_contains (str | None) – Selects edge deployment plans with names containing this name.

• device_fleet_name_contains (str | None) – Selects edge deployment plans with a device fleet name containing this name.

• sort_by (str | None) – The column by which to sort the edge deployment plans. Can be one of NAME, DEVICEFLEETNAME, CREATIONTIME, LASTMODIFIEDTIME.

• sort_order (str | None) – The direction of the sorting (ascending or descending).

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed EdgeDeploymentPlan resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[EdgeDeploymentPlan]

get_all_stage_devices(stage_name, exclude_devices_deployed_in_other_stage=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Lists devices allocated to the stage, containing detailed device information and deployment status.

Parameters:

• stage_name (str) – The name of the stage in the deployment.

• max_results – The maximum number of requests to select.

• exclude_devices_deployed_in_other_stage (bool | None) – Toggle for excluding devices deployed in other stages.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed DeviceDeploymentSummary.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[DeviceDeploymentSummary]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh(max_results=<sagemaker_core.main.utils.Unassigned object>)

Refresh a EdgeDeploymentPlan resource

Returns:

The EdgeDeploymentPlan resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Parameters:

max_results (int | None)

Return type:

EdgeDeploymentPlan | None

start_stage(stage_name, session=None, region=None)

Starts a stage in an edge deployment plan.

Parameters:

• stage_name (str) – The name of the stage to start.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

stop_stage(stage_name, session=None, region=None)

Stops a stage in an edge deployment plan.

Parameters:

• stage_name (str) – The name of the stage to stop.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

class sagemaker_core.main.resources.EdgePackagingJob(*, edge_packaging_job_name, edge_packaging_job_arn=<sagemaker_core.main.utils.Unassigned object>, compilation_job_name=<sagemaker_core.main.utils.Unassigned object>, model_name=<sagemaker_core.main.utils.Unassigned object>, model_version=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, resource_key=<sagemaker_core.main.utils.Unassigned object>, edge_packaging_job_status=<sagemaker_core.main.utils.Unassigned object>, edge_packaging_job_status_message=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, model_artifact=<sagemaker_core.main.utils.Unassigned object>, model_signature=<sagemaker_core.main.utils.Unassigned object>, preset_deployment_output=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource EdgePackagingJob

Parameters:

• edge_packaging_job_name (str)

• edge_packaging_job_arn (str | None)

• compilation_job_name (str | None)

• model_name (str | None)

• model_version (str | None)

• role_arn (str | None)

• output_config (EdgeOutputConfig | None)

• resource_key (str | None)

• edge_packaging_job_status (str | None)

• edge_packaging_job_status_message (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• model_artifact (str | None)

• model_signature (str | None)

• preset_deployment_output (EdgePresetDeploymentOutput | None)

edge_packaging_job_arn

The Amazon Resource Name (ARN) of the edge packaging job.

Type:

str | None

edge_packaging_job_name

The name of the edge packaging job.

Type:

str

edge_packaging_job_status

The current status of the packaging job.

Type:

str | None

compilation_job_name

The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.

Type:

str | None

model_name

The name of the model.

Type:

str | None

model_version

The version of the model.

Type:

str | None

role_arn

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.

Type:

str | None

output_config

The output configuration for the edge packaging job.

Type:

sagemaker_core.main.shapes.EdgeOutputConfig | None

resource_key

The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.

Type:

str | None

edge_packaging_job_status_message

Returns a message describing the job status and error messages.

Type:

str | None

creation_time

The timestamp of when the packaging job was created.

Type:

datetime.datetime | None

last_modified_time

The timestamp of when the job was last updated.

Type:

datetime.datetime | None

model_artifact

The Amazon Simple Storage (S3) URI where model artifacts ares stored.

Type:

str | None

model_signature

The signature document of files in the model artifact.

Type:

str | None

preset_deployment_output

The output of a SageMaker Edge Manager deployable resource.

Type:

sagemaker_core.main.shapes.EdgePresetDeploymentOutput | None

classmethod create(edge_packaging_job_name, compilation_job_name, model_name, model_version, role_arn, output_config, resource_key=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a EdgePackagingJob resource

Parameters:

• edge_packaging_job_name (str) – The name of the edge packaging job.

• compilation_job_name (str | object) – The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.

• model_name (str | object) – The name of the model.

• model_version (str) – The version of the model.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.

• output_config (EdgeOutputConfig) – Provides information about the output location for the packaged model.

• resource_key (str | None) – The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.

• tags (List[Tag] | None) – Creates tags for the packaging job.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The EdgePackagingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

EdgePackagingJob | None

classmethod get(edge_packaging_job_name, session=None, region=None)

Get a EdgePackagingJob resource

Parameters:

• edge_packaging_job_name (str) – The name of the edge packaging job.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The EdgePackagingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

EdgePackagingJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, model_name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all EdgePackagingJob resources

Parameters:

• next_token – The response from the last list when returning a list large enough to need tokening.

• max_results – Maximum number of results to select.

• creation_time_after (datetime | None) – Select jobs where the job was created after specified time.

• creation_time_before (datetime | None) – Select jobs where the job was created before specified time.

• last_modified_time_after (datetime | None) – Select jobs where the job was updated after specified time.

• last_modified_time_before (datetime | None) – Select jobs where the job was updated before specified time.

• name_contains (str | None) – Filter for jobs containing this name in their packaging job name.

• model_name_contains (str | None) – Filter for jobs where the model name contains this string.

• status_equals (str | None) – The job status to filter for.

• sort_by (str | None) – Use to specify what column to sort by.

• sort_order (str | None) – What direction to sort by.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed EdgePackagingJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[EdgePackagingJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a EdgePackagingJob resource

Returns:

The EdgePackagingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

EdgePackagingJob | None

stop()

Stop a EdgePackagingJob resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

wait(poll=5, timeout=None)

Wait for a EdgePackagingJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Endpoint(*, endpoint_name, endpoint_arn=<sagemaker_core.main.utils.Unassigned object>, endpoint_config_name=<sagemaker_core.main.utils.Unassigned object>, production_variants=<sagemaker_core.main.utils.Unassigned object>, data_capture_config=<sagemaker_core.main.utils.Unassigned object>, endpoint_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_deployment_config=<sagemaker_core.main.utils.Unassigned object>, async_inference_config=<sagemaker_core.main.utils.Unassigned object>, pending_deployment_summary=<sagemaker_core.main.utils.Unassigned object>, explainer_config=<sagemaker_core.main.utils.Unassigned object>, shadow_production_variants=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Endpoint

Parameters:

• endpoint_name (str)

• endpoint_arn (str | None)

• endpoint_config_name (str | None)

• production_variants (List[ProductionVariantSummary] | None)

• data_capture_config (DataCaptureConfigSummary | None)

• endpoint_status (str | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• last_deployment_config (DeploymentConfig | None)

• async_inference_config (AsyncInferenceConfig | None)

• pending_deployment_summary (PendingDeploymentSummary | None)

• explainer_config (ExplainerConfig | None)

• shadow_production_variants (List[ProductionVariantSummary] | None)

endpoint_name

Name of the endpoint.

Type:

str

endpoint_arn

The Amazon Resource Name (ARN) of the endpoint.

Type:

str | None

endpoint_status

The status of the endpoint. OutOfService: Endpoint is not available to take incoming requests. Creating: CreateEndpoint is executing. Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. InService: Endpoint is available to process incoming requests. Deleting: DeleteEndpoint is executing. Failed: Endpoint could not be created, updated, or re-scaled. Use the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. UpdateRollbackFailed: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint’s status to InService, see Rolling Deployments.

Type:

str | None

creation_time

A timestamp that shows when the endpoint was created.

Type:

datetime.datetime | None

last_modified_time

A timestamp that shows when the endpoint was last modified.

Type:

datetime.datetime | None

endpoint_config_name

The name of the endpoint configuration associated with this endpoint.

Type:

str | None

production_variants

An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

Type:

List[sagemaker_core.main.shapes.ProductionVariantSummary] | None

data_capture_config

Type:

sagemaker_core.main.shapes.DataCaptureConfigSummary | None

failure_reason

If the status of the endpoint is Failed, the reason why it failed.

Type:

str | None

last_deployment_config

The most recent deployment configuration for the endpoint.

Type:

sagemaker_core.main.shapes.DeploymentConfig | None

async_inference_config

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Type:

sagemaker_core.main.shapes.AsyncInferenceConfig | None

pending_deployment_summary

Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.

Type:

sagemaker_core.main.shapes.PendingDeploymentSummary | None

explainer_config

The configuration parameters for an explainer.

Type:

sagemaker_core.main.shapes.ExplainerConfig | None

shadow_production_variants

An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.

Type:

List[sagemaker_core.main.shapes.ProductionVariantSummary] | None

classmethod create(endpoint_name, endpoint_config_name, deployment_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Endpoint resource

Parameters:

• endpoint_name (str) – The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

• endpoint_config_name (str | object) – The name of an endpoint configuration. For more information, see CreateEndpointConfig.

• deployment_config (DeploymentConfig | None)

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Endpoint resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Endpoint | None

delete()

Delete a Endpoint resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(endpoint_name, session=None, region=None)

Get a Endpoint resource

Parameters:

• endpoint_name (str) – The name of the endpoint.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Endpoint resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Endpoint | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Endpoint resources

Parameters:

• sort_by (str | None) – Sorts the list of results. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Descending.

• next_token – If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.

• max_results – The maximum number of endpoints to return in the response. This value defaults to 10.

• name_contains (str | None) – A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only endpoints that were created before the specified time (timestamp).

• creation_time_after (datetime | None) – A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

• last_modified_time_before (datetime | None) – A filter that returns only endpoints that were modified before the specified timestamp.

• last_modified_time_after (datetime | None) – A filter that returns only endpoints that were modified after the specified timestamp.

• status_equals (str | None) – A filter that returns only endpoints with the specified status.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Endpoint resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Endpoint]

invoke(body, content_type=<sagemaker_core.main.utils.Unassigned object>, accept=<sagemaker_core.main.utils.Unassigned object>, custom_attributes=<sagemaker_core.main.utils.Unassigned object>, target_model=<sagemaker_core.main.utils.Unassigned object>, target_variant=<sagemaker_core.main.utils.Unassigned object>, target_container_hostname=<sagemaker_core.main.utils.Unassigned object>, inference_id=<sagemaker_core.main.utils.Unassigned object>, enable_explanations=<sagemaker_core.main.utils.Unassigned object>, inference_component_name=<sagemaker_core.main.utils.Unassigned object>, session_id=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint.

Parameters:

• body (Any) – Provides input data, in the format specified in the ContentType request header. Amazon SageMaker passes all of the data in the body to the model. For information about the format of the request body, see Common Data Formats-Inference.

• content_type (str | None) – The MIME type of the input data in the request body.

• accept (str | None) – The desired MIME type of the inference response from the model container.

• custom_attributes (str | None) – Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1). The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function. This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

• target_model (str | None) – The model to request for inference when invoking a multi-model endpoint.

• target_variant (str | None) – Specify the production variant to send the inference request to when invoking an endpoint that is running two or more variants. Note that this parameter overrides the default behavior for the endpoint, which is to distribute the invocation traffic based on the variant weights. For information about how to use variant targeting to perform a/b testing, see Test models in production

• target_container_hostname (str | None) – If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke.

• inference_id (str | None) – If you provide a value, it is added to the captured data when you enable data capture on the endpoint. For information about data capture, see Capture Data.

• enable_explanations (str | None) – An optional JMESPath expression used to override the EnableExplanations parameter of the ClarifyExplainerConfig API. See the EnableExplanations section in the developer guide for more information.

• inference_component_name (str | None) – If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke.

• session_id (str | None) – Creates a stateful session or identifies an existing one. You can do one of the following: Create a stateful session by specifying the value NEW_SESSION. Send your request to an existing stateful session by specifying the ID of that session. With a stateful session, you can send multiple requests to a stateful model. When you create a session with a stateful model, the model must create the session ID and set the expiration time. The model must also provide that information in the response to your request. You can get the ID and timestamp from the NewSessionId response parameter. For any subsequent request where you specify that session ID, SageMaker routes the request to the same instance that supports the session.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

InvokeEndpointOutput

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• InternalDependencyException – Your request caused an exception with an internal dependency. Contact customer support.

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• ModelError – Model (owned by the customer in the container) returned 4xx or 5xx error code.

• ModelNotReadyException – Either a serverless endpoint variant’s resources are still being provisioned, or a multi-model endpoint is still downloading or loading the target model. Wait and try your request again.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

InvokeEndpointOutput | None

invoke_async(input_location, content_type=<sagemaker_core.main.utils.Unassigned object>, accept=<sagemaker_core.main.utils.Unassigned object>, custom_attributes=<sagemaker_core.main.utils.Unassigned object>, inference_id=<sagemaker_core.main.utils.Unassigned object>, request_ttl_seconds=<sagemaker_core.main.utils.Unassigned object>, invocation_timeout_seconds=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint in an asynchronous manner.

Parameters:

• input_location (str) – The Amazon S3 URI where the inference request payload is stored.

• content_type (str | None) – The MIME type of the input data in the request body.

• accept (str | None) – The desired MIME type of the inference response from the model container.

• custom_attributes (str | None) – Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1). The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function. This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

• inference_id (str | None) – The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified.

• request_ttl_seconds (int | None) – Maximum age in seconds a request can be in the queue before it is marked as expired. The default is 6 hours, or 21,600 seconds.

• invocation_timeout_seconds (int | None) – Maximum amount of time in seconds a request can be processed before it is marked as expired. The default is 15 minutes, or 900 seconds.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

InvokeEndpointAsyncOutput

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

InvokeEndpointAsyncOutput | None

invoke_with_response_stream(body, content_type=<sagemaker_core.main.utils.Unassigned object>, accept=<sagemaker_core.main.utils.Unassigned object>, custom_attributes=<sagemaker_core.main.utils.Unassigned object>, target_variant=<sagemaker_core.main.utils.Unassigned object>, target_container_hostname=<sagemaker_core.main.utils.Unassigned object>, inference_id=<sagemaker_core.main.utils.Unassigned object>, inference_component_name=<sagemaker_core.main.utils.Unassigned object>, session_id=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Invokes a model at the specified endpoint to return the inference response as a stream.

Parameters:

• body (Any) – Provides input data, in the format specified in the ContentType request header. Amazon SageMaker passes all of the data in the body to the model. For information about the format of the request body, see Common Data Formats-Inference.

• content_type (str | None) – The MIME type of the input data in the request body.

• accept (str | None) – The desired MIME type of the inference response from the model container.

• custom_attributes (str | None) – Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1). The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function. This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

• target_variant (str | None) – Specify the production variant to send the inference request to when invoking an endpoint that is running two or more variants. Note that this parameter overrides the default behavior for the endpoint, which is to distribute the invocation traffic based on the variant weights. For information about how to use variant targeting to perform a/b testing, see Test models in production

• target_container_hostname (str | None) – If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke.

• inference_id (str | None) – An identifier that you assign to your request.

• inference_component_name (str | None) – If the endpoint hosts one or more inference components, this parameter specifies the name of inference component to invoke for a streaming response.

• session_id (str | None) – The ID of a stateful session to handle your request. You can’t create a stateful session by using the InvokeEndpointWithResponseStream action. Instead, you can create one by using the InvokeEndpoint action. In your request, you specify NEW_SESSION for the SessionId request parameter. The response to that request provides the session ID for the NewSessionId response parameter.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

InvokeEndpointWithResponseStreamOutput

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• InternalStreamFailure – The stream processing failed because of an unknown error, exception or failure. Try your request again.

• ModelError – Model (owned by the customer in the container) returned 4xx or 5xx error code.

• ModelStreamError – An error occurred while streaming the response body. This error can have the following error codes: ModelInvocationTimeExceeded The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

InvokeEndpointWithResponseStreamOutput | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Endpoint resource

Returns:

The Endpoint resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Endpoint | None

update(retain_all_variant_properties=<sagemaker_core.main.utils.Unassigned object>, exclude_retained_variant_properties=<sagemaker_core.main.utils.Unassigned object>, deployment_config=<sagemaker_core.main.utils.Unassigned object>, retain_deployment_config=<sagemaker_core.main.utils.Unassigned object>)

Update a Endpoint resource

Parameters:

• retain_all_variant_properties (bool | None) – When updating endpoint resources, enables or disables the retention of variant properties, such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set RetainAllVariantProperties to true. To use the variant properties specified in a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to false. The default is false.

• exclude_retained_variant_properties (List[VariantProperty] | None) – When you are updating endpoint resources with RetainAllVariantProperties, whose value is set to true, ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig. If you don’t specify a value for ExcludeRetainedVariantProperties, no variant properties are overridden.

• deployment_config (DeploymentConfig | None) – The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

• retain_deployment_config (bool | None) – Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).

Returns:

The Endpoint resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

Endpoint | None

update_weights_and_capacities(desired_weights_and_capacities, session=None, region=None)

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.

Parameters:

• desired_weights_and_capacities (List[DesiredWeightAndCapacity]) – An object that provides new capacity and weight values for a variant.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

None

wait_for_delete(poll=5, timeout=None)

Wait for a Endpoint resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Endpoint resource to reach certain status.

Parameters:

• target_status (Literal['OutOfService', 'Creating', 'Updating', 'SystemUpdating', 'RollingBack', 'InService', 'Deleting', 'Failed', 'UpdateRollbackFailed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.EndpointConfig(*, endpoint_config_name, endpoint_config_arn=<sagemaker_core.main.utils.Unassigned object>, production_variants=<sagemaker_core.main.utils.Unassigned object>, data_capture_config=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, async_inference_config=<sagemaker_core.main.utils.Unassigned object>, explainer_config=<sagemaker_core.main.utils.Unassigned object>, shadow_production_variants=<sagemaker_core.main.utils.Unassigned object>, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource EndpointConfig

Parameters:

• endpoint_config_name (str)

• endpoint_config_arn (str | None)

• production_variants (List[ProductionVariant] | None)

• data_capture_config (DataCaptureConfig | None)

• kms_key_id (str | None)

• creation_time (datetime | None)

• async_inference_config (AsyncInferenceConfig | None)

• explainer_config (ExplainerConfig | None)

• shadow_production_variants (List[ProductionVariant] | None)

• execution_role_arn (str | None)

• vpc_config (VpcConfig | None)

• enable_network_isolation (bool | None)

endpoint_config_name

Name of the SageMaker endpoint configuration.

Type:

str

endpoint_config_arn

The Amazon Resource Name (ARN) of the endpoint configuration.

Type:

str | None

production_variants

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

Type:

List[sagemaker_core.main.shapes.ProductionVariant] | None

creation_time

A timestamp that shows when the endpoint configuration was created.

Type:

datetime.datetime | None

data_capture_config

Type:

sagemaker_core.main.shapes.DataCaptureConfig | None

kms_key_id

Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

Type:

str | None

async_inference_config

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Type:

sagemaker_core.main.shapes.AsyncInferenceConfig | None

explainer_config

The configuration parameters for an explainer.

Type:

sagemaker_core.main.shapes.ExplainerConfig | None

shadow_production_variants

An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.

Type:

List[sagemaker_core.main.shapes.ProductionVariant] | None

execution_role_arn

The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.

Type:

str | None

vpc_config

Type:

sagemaker_core.main.shapes.VpcConfig | None

enable_network_isolation

Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

Type:

bool | None

classmethod create(endpoint_config_name, production_variants, data_capture_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, async_inference_config=<sagemaker_core.main.utils.Unassigned object>, explainer_config=<sagemaker_core.main.utils.Unassigned object>, shadow_production_variants=<sagemaker_core.main.utils.Unassigned object>, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a EndpointConfig resource

Parameters:

• endpoint_config_name (str) – The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

• production_variants (List[ProductionVariant]) – An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

• data_capture_config (DataCaptureConfig | None)

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• kms_key_id (str | None) – The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint, UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section Using Key Policies in Amazon Web Services KMS Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can’t request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.

• async_inference_config (AsyncInferenceConfig | None) – Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.

• explainer_config (ExplainerConfig | None) – A member of CreateEndpointConfig that enables explainers.

• shadow_production_variants (List[ProductionVariant] | None) – An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants. If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants.

• execution_role_arn (str | None) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform actions on your behalf. For more information, see SageMaker AI Roles. To be able to pass this role to Amazon SageMaker AI, the caller of this action must have the iam:PassRole permission.

• vpc_config (VpcConfig | None)

• enable_network_isolation (bool | None) – Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The EndpointConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

EndpointConfig | None

delete()

Delete a EndpointConfig resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(endpoint_config_name, session=None, region=None)

Get a EndpointConfig resource

Parameters:

• endpoint_config_name (str) – The name of the endpoint configuration.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The EndpointConfig resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

EndpointConfig | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all EndpointConfig resources

Parameters:

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Descending.

• next_token – If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.

• max_results – The maximum number of training jobs to return in the response.

• name_contains (str | None) – A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only endpoint configurations created before the specified time (timestamp).

• creation_time_after (datetime | None) – A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed EndpointConfig resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[EndpointConfig]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a EndpointConfig resource

Returns:

The EndpointConfig resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

EndpointConfig | None

class sagemaker_core.main.resources.Experiment(*, experiment_name, experiment_arn=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Experiment

Parameters:

• experiment_name (str)

• experiment_arn (str | None)

• display_name (str | None)

• source (ExperimentSource | None)

• description (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

experiment_name

The name of the experiment.

Type:

str

experiment_arn

The Amazon Resource Name (ARN) of the experiment.

Type:

str | None

display_name

The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

Type:

str | None

source

The Amazon Resource Name (ARN) of the source and, optionally, the type.

Type:

sagemaker_core.main.shapes.ExperimentSource | None

description

The description of the experiment.

Type:

str | None

creation_time

When the experiment was created.

Type:

datetime.datetime | None

created_by

Who created the experiment.

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

When the experiment was last modified.

Type:

datetime.datetime | None

last_modified_by

Who last modified the experiment.

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod create(experiment_name, display_name=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Experiment resource

Parameters:

• experiment_name (str) – The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.

• display_name (str | None) – The name of the experiment as displayed. The name doesn’t need to be unique. If you don’t specify DisplayName, the value in ExperimentName is displayed.

• description (str | None) – The description of the experiment.

• tags (List[Tag] | None) – A list of tags to associate with the experiment. You can use Search API to search on the tags.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Experiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Experiment | None

delete()

Delete a Experiment resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(experiment_name, session=None, region=None)

Get a Experiment resource

Parameters:

• experiment_name (str) – The name of the experiment to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Experiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Experiment | None

classmethod get_all(created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Experiment resources

Parameters:

• created_after (datetime | None) – A filter that returns only experiments created after the specified time.

• created_before (datetime | None) – A filter that returns only experiments created before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• next_token – If the previous call to ListExperiments didn’t return the full set of experiments, the call returns a token for getting the next set of experiments.

• max_results – The maximum number of experiments to return in the response. The default value is 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Experiment resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Experiment]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Experiment resource

Returns:

The Experiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Experiment | None

update(display_name=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>)

Update a Experiment resource

Returns:

The Experiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Parameters:

• display_name (str | None)

• description (str | None)

Return type:

Experiment | None

class sagemaker_core.main.resources.FeatureGroup(*, feature_group_name, feature_group_arn=<sagemaker_core.main.utils.Unassigned object>, record_identifier_feature_name=<sagemaker_core.main.utils.Unassigned object>, event_time_feature_name=<sagemaker_core.main.utils.Unassigned object>, feature_definitions=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, online_store_config=<sagemaker_core.main.utils.Unassigned object>, offline_store_config=<sagemaker_core.main.utils.Unassigned object>, throughput_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, feature_group_status=<sagemaker_core.main.utils.Unassigned object>, offline_store_status=<sagemaker_core.main.utils.Unassigned object>, last_update_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, next_token=<sagemaker_core.main.utils.Unassigned object>, online_store_total_size_bytes=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource FeatureGroup

Parameters:

• feature_group_name (str)

• feature_group_arn (str | None)

• record_identifier_feature_name (str | None)

• event_time_feature_name (str | None)

• feature_definitions (List[FeatureDefinition] | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• online_store_config (OnlineStoreConfig | None)

• offline_store_config (OfflineStoreConfig | None)

• throughput_config (ThroughputConfigDescription | None)

• role_arn (str | None)

• feature_group_status (str | None)

• offline_store_status (OfflineStoreStatus | None)

• last_update_status (LastUpdateStatus | None)

• failure_reason (str | None)

• description (str | None)

• next_token (str | None)

• online_store_total_size_bytes (int | None)

feature_group_arn

The Amazon Resource Name (ARN) of the FeatureGroup.

Type:

str | None

feature_group_name

he name of the FeatureGroup.

Type:

str

record_identifier_feature_name

The name of the Feature used for RecordIdentifier, whose value uniquely identifies a record stored in the feature store.

Type:

str | None

event_time_feature_name

The name of the feature that stores the EventTime of a Record in a FeatureGroup. An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup have a corresponding EventTime.

Type:

str | None

feature_definitions

A list of the Features in the FeatureGroup. Each feature is defined by a FeatureName and FeatureType.

Type:

List[sagemaker_core.main.shapes.FeatureDefinition] | None

creation_time

A timestamp indicating when SageMaker created the FeatureGroup.

Type:

datetime.datetime | None

next_token

A token to resume pagination of the list of Features (FeatureDefinitions).

Type:

str | None

last_modified_time

A timestamp indicating when the feature group was last updated.

Type:

datetime.datetime | None

online_store_config

The configuration for the OnlineStore.

Type:

sagemaker_core.main.shapes.OnlineStoreConfig | None

offline_store_config

The configuration of the offline store. It includes the following configurations: Amazon S3 location of the offline store. Configuration of the Glue data catalog. Table format of the offline store. Option to disable the automatic creation of a Glue table for the offline store. Encryption configuration.

Type:

sagemaker_core.main.shapes.OfflineStoreConfig | None

throughput_config

Type:

sagemaker_core.main.shapes.ThroughputConfigDescription | None

role_arn

The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.

Type:

str | None

feature_group_status

The status of the feature group.

Type:

str | None

offline_store_status

The status of the OfflineStore. Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked

Type:

sagemaker_core.main.shapes.OfflineStoreStatus | None

last_update_status

A value indicating whether the update made to the feature group was successful.

Type:

sagemaker_core.main.shapes.LastUpdateStatus | None

failure_reason

The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure can occur because: The FeatureGroup could not be created in the OfflineStore. The FeatureGroup could not be deleted from the OfflineStore.

Type:

str | None

description

A free form description of the feature group.

Type:

str | None

online_store_total_size_bytes

The size of the OnlineStore in bytes.

Type:

int | None

batch_get_record(identifiers, expiration_time_response=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Retrieves a batch of Records from a FeatureGroup.

Parameters:

• identifiers (List[BatchGetRecordIdentifier]) – A list containing the name or Amazon Resource Name (ARN) of the FeatureGroup, the list of names of Features to be retrieved, and the corresponding RecordIdentifier values as strings.

• expiration_time_response (str | None) – Parameter to request ExpiresAt in response. If Enabled, BatchGetRecord will return the value of ExpiresAt, if it is not null. If Disabled and null, BatchGetRecord will return null.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

BatchGetRecordResponse

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• AccessForbidden – You do not have permission to perform an action.

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

BatchGetRecordResponse | None

classmethod create(feature_group_name, record_identifier_feature_name, event_time_feature_name, feature_definitions, online_store_config=<sagemaker_core.main.utils.Unassigned object>, offline_store_config=<sagemaker_core.main.utils.Unassigned object>, throughput_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a FeatureGroup resource

Parameters:

• feature_group_name (str) – The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name: Must start with an alphanumeric character. Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.

• record_identifier_feature_name (str) – The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore. Only the latest record per identifier value will be stored in the OnlineStore. RecordIdentifierFeatureName must be one of feature definitions’ names. You use the RecordIdentifierFeatureName to access data in a FeatureStore. This name: Must start with an alphanumeric character. Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.

• event_time_feature_name (str) – The name of the feature that stores the EventTime of a Record in a FeatureGroup. An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime. An EventTime can be a String or Fractional. Fractional: EventTime feature values must be a Unix timestamp in seconds. String: EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd’T’HH:mm:ssZ and yyyy-MM-dd’T’HH:mm:ss.SSSZ where yyyy, MM, and dd represent the year, month, and day respectively and HH, mm, ss, and if applicable, SSS represent the hour, month, second and milliseconds respsectively. ‘T’ and Z are constants.

• feature_definitions (List[FeatureDefinition]) – A list of Feature names and types. Name and Type is compulsory per Feature. Valid feature FeatureTypes are Integral, Fractional and String. FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time You can create up to 2,500 FeatureDefinitions per FeatureGroup.

• online_store_config (OnlineStoreConfig | None) – You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig. You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of the OnlineStore. The default value is False.

• offline_store_config (OfflineStoreConfig | None) – Use this to configure an OfflineFeatureStore. This parameter allows you to specify: The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore. A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog. An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent. Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg. To learn more about this parameter, see OfflineStoreConfig.

• throughput_config (ThroughputConfig | None)

• role_arn (str | None) – The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.

• description (str | None) – A free-form description of a FeatureGroup.

• tags (List[Tag] | None) – Tags used to identify Features in each FeatureGroup.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The FeatureGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

FeatureGroup | None

delete()

Delete a FeatureGroup resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

delete_record(record_identifier_value_as_string, event_time, target_stores=<sagemaker_core.main.utils.Unassigned object>, deletion_mode=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Deletes a Record from a FeatureGroup in the OnlineStore.

Parameters:

• record_identifier_value_as_string (str) – The value for the RecordIdentifier that uniquely identifies the record, in string format.

• event_time (str) – Timestamp indicating when the deletion event occurred. EventTime can be used to query data at a certain point in time.

• target_stores (List[str] | None) – A list of stores from which you’re deleting the record. By default, Feature Store deletes the record from all of the stores that you’re using for the FeatureGroup.

• deletion_mode (str | None) – The name of the deletion mode for deleting the record. By default, the deletion mode is set to SoftDelete.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• AccessForbidden – You do not have permission to perform an action.

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

None

classmethod get(feature_group_name, next_token=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a FeatureGroup resource

Parameters:

• feature_group_name (str) – The name or Amazon Resource Name (ARN) of the FeatureGroup you want described.

• next_token (str | None) – A token to resume pagination of the list of Features (FeatureDefinitions). 2,500 Features are returned by default.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The FeatureGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FeatureGroup | None

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, feature_group_status_equals=<sagemaker_core.main.utils.Unassigned object>, offline_store_status_equals=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all FeatureGroup resources

Parameters:

• name_contains (str | None) – A string that partially matches one or more FeatureGroups names. Filters FeatureGroups by name.

• feature_group_status_equals (str | None) – A FeatureGroup status. Filters by FeatureGroup status.

• offline_store_status_equals (str | None) – An OfflineStore status. Filters by OfflineStore status.

• creation_time_after (datetime | None) – Use this parameter to search for FeatureGroupss created after a specific date and time.

• creation_time_before (datetime | None) – Use this parameter to search for FeatureGroupss created before a specific date and time.

• sort_order (str | None) – The order in which feature groups are listed.

• sort_by (str | None) – The value on which the feature group list is sorted.

• max_results – The maximum number of results returned by ListFeatureGroups.

• next_token – A token to resume pagination of ListFeatureGroups results.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed FeatureGroup resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[FeatureGroup]

get_record(record_identifier_value_as_string, feature_names=<sagemaker_core.main.utils.Unassigned object>, expiration_time_response=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Use for OnlineStore serving from a FeatureStore.

Parameters:

• record_identifier_value_as_string (str) – The value that corresponds to RecordIdentifier type and uniquely identifies the record in the FeatureGroup.

• feature_names (List[str] | None) – List of names of Features to be retrieved. If not specified, the latest value for all the Features are returned.

• expiration_time_response (str | None) – Parameter to request ExpiresAt in response. If Enabled, GetRecord will return the value of ExpiresAt, if it is not null. If Disabled and null, GetRecord will return null.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

GetRecordResponse

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• AccessForbidden – You do not have permission to perform an action.

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• ResourceNotFound – Resource being access is not found.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

GetRecordResponse | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

put_record(record, target_stores=<sagemaker_core.main.utils.Unassigned object>, ttl_duration=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

The PutRecord API is used to ingest a list of Records into your feature group.

Parameters:

• record (List[FeatureValue]) – List of FeatureValues to be inserted. This will be a full over-write. If you only want to update few of the feature values, do the following: Use GetRecord to retrieve the latest record. Update the record returned from GetRecord. Use PutRecord to update feature values.

• target_stores (List[str] | None) – A list of stores to which you’re adding the record. By default, Feature Store adds the record to all of the stores that you’re using for the FeatureGroup.

• ttl_duration (TtlDuration | None) – Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• AccessForbidden – You do not have permission to perform an action.

• InternalFailure – An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

• ServiceUnavailable – The service is currently unavailable.

• ValidationError – There was an error validating your request.

Return type:

None

refresh()

Refresh a FeatureGroup resource

Returns:

The FeatureGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FeatureGroup | None

update(feature_additions=<sagemaker_core.main.utils.Unassigned object>, online_store_config=<sagemaker_core.main.utils.Unassigned object>, throughput_config=<sagemaker_core.main.utils.Unassigned object>)

Update a FeatureGroup resource

Parameters:

• feature_additions (List[FeatureDefinition] | None) – Updates the feature group. Updating a feature group is an asynchronous operation. When you get an HTTP 200 response, you’ve made a valid request. It takes some time after you’ve made a valid request for Feature Store to update the feature group.

• online_store_config (OnlineStoreConfigUpdate | None)

• throughput_config (ThroughputConfigUpdate | None)

Returns:

The FeatureGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

FeatureGroup | None

wait_for_delete(poll=5, timeout=None)

Wait for a FeatureGroup resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a FeatureGroup resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'Created', 'CreateFailed', 'Deleting', 'DeleteFailed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.FeatureMetadata(*, feature_group_name, feature_name, feature_group_arn=<sagemaker_core.main.utils.Unassigned object>, feature_type=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, parameters=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource FeatureMetadata

Parameters:

• feature_group_name (str)

• feature_name (str)

• feature_group_arn (str | None)

• feature_type (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• description (str | None)

• parameters (List[FeatureParameter] | None)

feature_group_arn

The Amazon Resource Number (ARN) of the feature group that contains the feature.

Type:

str | None

feature_group_name

The name of the feature group that you’ve specified.

Type:

str

feature_name

The name of the feature that you’ve specified.

Type:

str

feature_type

The data type of the feature.

Type:

str | None

creation_time

A timestamp indicating when the feature was created.

Type:

datetime.datetime | None

last_modified_time

A timestamp indicating when the metadata for the feature group was modified. For example, if you add a parameter describing the feature, the timestamp changes to reflect the last time you

Type:

datetime.datetime | None

description

The description you added to describe the feature.

Type:

str | None

parameters

The key-value pairs that you added to describe the feature.

Type:

List[sagemaker_core.main.shapes.FeatureParameter] | None

classmethod get(feature_group_name, feature_name, session=None, region=None)

Get a FeatureMetadata resource

Parameters:

• feature_group_name (str) – The name or Amazon Resource Name (ARN) of the feature group containing the feature.

• feature_name (str) – The name of the feature.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The FeatureMetadata resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FeatureMetadata | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a FeatureMetadata resource

Returns:

The FeatureMetadata resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FeatureMetadata | None

update(description=<sagemaker_core.main.utils.Unassigned object>, parameter_additions=<sagemaker_core.main.utils.Unassigned object>, parameter_removals=<sagemaker_core.main.utils.Unassigned object>)

Update a FeatureMetadata resource

Parameters:

• parameter_additions (List[FeatureParameter] | None) – A list of key-value pairs that you can add to better describe the feature.

• parameter_removals (List[str] | None) – A list of parameter keys that you can specify to remove parameters that describe your feature.

• description (str | None)

Returns:

The FeatureMetadata resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FeatureMetadata | None

class sagemaker_core.main.resources.FlowDefinition(*, flow_definition_name, flow_definition_arn=<sagemaker_core.main.utils.Unassigned object>, flow_definition_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, human_loop_request_source=<sagemaker_core.main.utils.Unassigned object>, human_loop_activation_config=<sagemaker_core.main.utils.Unassigned object>, human_loop_config=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource FlowDefinition

Parameters:

• flow_definition_name (str)

• flow_definition_arn (str | None)

• flow_definition_status (str | None)

• creation_time (datetime | None)

• human_loop_request_source (HumanLoopRequestSource | None)

• human_loop_activation_config (HumanLoopActivationConfig | None)

• human_loop_config (HumanLoopConfig | None)

• output_config (FlowDefinitionOutputConfig | None)

• role_arn (str | None)

• failure_reason (str | None)

flow_definition_arn

The Amazon Resource Name (ARN) of the flow defintion.

Type:

str | None

flow_definition_name

The Amazon Resource Name (ARN) of the flow definition.

Type:

str

flow_definition_status

The status of the flow definition. Valid values are listed below.

Type:

str | None

creation_time

The timestamp when the flow definition was created.

Type:

datetime.datetime | None

output_config

An object containing information about the output file.

Type:

sagemaker_core.main.shapes.FlowDefinitionOutputConfig | None

role_arn

The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.

Type:

str | None

human_loop_request_source

Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

Type:

sagemaker_core.main.shapes.HumanLoopRequestSource | None

human_loop_activation_config

An object containing information about what triggers a human review workflow.

Type:

sagemaker_core.main.shapes.HumanLoopActivationConfig | None

human_loop_config

An object containing information about who works on the task, the workforce task price, and other task details.

Type:

sagemaker_core.main.shapes.HumanLoopConfig | None

failure_reason

The reason your flow definition failed.

Type:

str | None

classmethod create(flow_definition_name, output_config, role_arn, human_loop_request_source=<sagemaker_core.main.utils.Unassigned object>, human_loop_activation_config=<sagemaker_core.main.utils.Unassigned object>, human_loop_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a FlowDefinition resource

Parameters:

• flow_definition_name (str) – The name of your flow definition.

• output_config (FlowDefinitionOutputConfig) – An object containing information about where the human review results will be uploaded.

• role_arn (str) – The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.

• human_loop_request_source (HumanLoopRequestSource | None) – Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

• human_loop_activation_config (HumanLoopActivationConfig | None) – An object containing information about the events that trigger a human workflow.

• human_loop_config (HumanLoopConfig | None) – An object containing information about the tasks the human reviewers will perform.

• tags (List[Tag] | None) – An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The FlowDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

FlowDefinition | None

delete()

Delete a FlowDefinition resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(flow_definition_name, session=None, region=None)

Get a FlowDefinition resource

Parameters:

• flow_definition_name (str) – The name of the flow definition.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The FlowDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FlowDefinition | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all FlowDefinition resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.

• creation_time_before (datetime | None) – A filter that returns only flow definitions that were created before the specified timestamp.

• sort_order (str | None) – An optional value that specifies whether you want the results sorted in Ascending or Descending order.

• next_token – A token to resume pagination.

• max_results – The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed FlowDefinition resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[FlowDefinition]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a FlowDefinition resource

Returns:

The FlowDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

FlowDefinition | None

wait_for_delete(poll=5, timeout=None)

Wait for a FlowDefinition resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a FlowDefinition resource to reach certain status.

Parameters:

• target_status (Literal['Initializing', 'Active', 'Failed', 'Deleting']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Hub(*, hub_name, hub_arn=<sagemaker_core.main.utils.Unassigned object>, hub_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_description=<sagemaker_core.main.utils.Unassigned object>, hub_search_keywords=<sagemaker_core.main.utils.Unassigned object>, s3_storage_config=<sagemaker_core.main.utils.Unassigned object>, hub_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Hub

Parameters:

• hub_name (str)

• hub_arn (str | None)

• hub_display_name (str | None)

• hub_description (str | None)

• hub_search_keywords (List[str] | None)

• s3_storage_config (HubS3StorageConfig | None)

• hub_status (str | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

hub_name

The name of the hub.

Type:

str

hub_arn

The Amazon Resource Name (ARN) of the hub.

Type:

str | None

hub_status

The status of the hub.

Type:

str | None

creation_time

The date and time that the hub was created.

Type:

datetime.datetime | None

last_modified_time

The date and time that the hub was last modified.

Type:

datetime.datetime | None

hub_display_name

The display name of the hub.

Type:

str | None

hub_description

A description of the hub.

Type:

str | None

hub_search_keywords

The searchable keywords for the hub.

Type:

List[str] | None

s3_storage_config

The Amazon S3 storage configuration for the hub.

Type:

sagemaker_core.main.shapes.HubS3StorageConfig | None

failure_reason

The failure reason if importing hub content failed.

Type:

str | None

classmethod create(hub_name, hub_description, hub_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_search_keywords=<sagemaker_core.main.utils.Unassigned object>, s3_storage_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Hub resource

Parameters:

• hub_name (str) – The name of the hub to create.

• hub_description (str) – A description of the hub.

• hub_display_name (str | None) – The display name of the hub.

• hub_search_keywords (List[str] | None) – The searchable keywords for the hub.

• s3_storage_config (HubS3StorageConfig | None) – The Amazon S3 storage configuration for the hub.

• tags (List[Tag] | None) – Any tags to associate with the hub.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Hub resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Hub | None

delete()

Delete a Hub resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(hub_name, session=None, region=None)

Get a Hub resource

Parameters:

• hub_name (str) – The name of the hub to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Hub resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Hub | None

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Hub resources

Parameters:

• name_contains (str | None) – Only list hubs with names that contain the specified string.

• creation_time_before (datetime | None) – Only list hubs that were created before the time specified.

• creation_time_after (datetime | None) – Only list hubs that were created after the time specified.

• last_modified_time_before (datetime | None) – Only list hubs that were last modified before the time specified.

• last_modified_time_after (datetime | None) – Only list hubs that were last modified after the time specified.

• sort_by (str | None) – Sort hubs by either name or creation time.

• sort_order (str | None) – Sort hubs by ascending or descending order.

• max_results – The maximum number of hubs to list.

• next_token – If the response to a previous ListHubs request was truncated, the response includes a NextToken. To retrieve the next set of hubs, use the token in the next request.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Hub resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Hub]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Hub resource

Returns:

The Hub resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Hub | None

update(hub_description=<sagemaker_core.main.utils.Unassigned object>, hub_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_search_keywords=<sagemaker_core.main.utils.Unassigned object>)

Update a Hub resource

Returns:

The Hub resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Parameters:

• hub_description (str | None)

• hub_display_name (str | None)

• hub_search_keywords (List[str] | None)

Return type:

Hub | None

wait_for_delete(poll=5, timeout=None)

Wait for a Hub resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Hub resource to reach certain status.

Parameters:

• target_status (Literal['InService', 'Creating', 'Updating', 'Deleting', 'CreateFailed', 'UpdateFailed', 'DeleteFailed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.HubContent(*, hub_content_type, hub_content_name, hub_content_arn=<sagemaker_core.main.utils.Unassigned object>, hub_content_version=<sagemaker_core.main.utils.Unassigned object>, document_schema_version=<sagemaker_core.main.utils.Unassigned object>, hub_arn=<sagemaker_core.main.utils.Unassigned object>, hub_content_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_content_description=<sagemaker_core.main.utils.Unassigned object>, hub_content_markdown=<sagemaker_core.main.utils.Unassigned object>, hub_content_document=<sagemaker_core.main.utils.Unassigned object>, sage_maker_public_hub_content_arn=<sagemaker_core.main.utils.Unassigned object>, reference_min_version=<sagemaker_core.main.utils.Unassigned object>, support_status=<sagemaker_core.main.utils.Unassigned object>, hub_content_search_keywords=<sagemaker_core.main.utils.Unassigned object>, hub_content_dependencies=<sagemaker_core.main.utils.Unassigned object>, hub_content_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, hub_name=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource HubContent

Parameters:

• hub_content_type (str)

• hub_content_name (str)

• hub_content_arn (str | None)

• hub_content_version (str | None)

• document_schema_version (str | None)

• hub_arn (str | None)

• hub_content_display_name (str | None)

• hub_content_description (str | None)

• hub_content_markdown (str | None)

• hub_content_document (str | None)

• sage_maker_public_hub_content_arn (str | None)

• reference_min_version (str | None)

• support_status (str | None)

• hub_content_search_keywords (List[str] | None)

• hub_content_dependencies (List[HubContentDependency] | None)

• hub_content_status (str | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• hub_name (str | None)

hub_content_name

The name of the hub content.

Type:

str

hub_content_arn

The Amazon Resource Name (ARN) of the hub content.

Type:

str | None

hub_content_version

The version of the hub content.

Type:

str | None

hub_content_type

The type of hub content.

Type:

str

document_schema_version

The document schema version for the hub content.

Type:

str | None

hub_name

The name of the hub that contains the content.

Type:

str | None

hub_arn

The Amazon Resource Name (ARN) of the hub that contains the content.

Type:

str | None

hub_content_document

The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.

Type:

str | None

hub_content_status

The status of the hub content.

Type:

str | None

creation_time

The date and time that hub content was created.

Type:

datetime.datetime | None

hub_content_display_name

The display name of the hub content.

Type:

str | None

hub_content_description

A description of the hub content.

Type:

str | None

hub_content_markdown

A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.

Type:

str | None

sage_maker_public_hub_content_arn

The ARN of the public hub content.

Type:

str | None

reference_min_version

The minimum version of the hub content.

Type:

str | None

support_status

The support status of the hub content.

Type:

str | None

hub_content_search_keywords

The searchable keywords for the hub content.

Type:

List[str] | None

hub_content_dependencies

The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks.

Type:

List[sagemaker_core.main.shapes.HubContentDependency] | None

failure_reason

The failure reason if importing hub content failed.

Type:

str | None

last_modified_time

The last modified time of the hub content.

Type:

datetime.datetime | None

delete()

Delete a HubContent resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(hub_name, hub_content_type, hub_content_name, hub_content_version=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a HubContent resource

Parameters:

• hub_name (str) – The name of the hub that contains the content to describe.

• hub_content_type (str) – The type of content in the hub.

• hub_content_name (str) – The name of the content to describe.

• hub_content_version (str | None) – The version of the content to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The HubContent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

HubContent | None

get_all_versions(min_version=<sagemaker_core.main.utils.Unassigned object>, max_schema_version=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

List hub content versions.

Parameters:

• min_version (str | None) – The lower bound of the hub content versions to list.

• max_schema_version (str | None) – The upper bound of the hub content schema version.

• creation_time_before (datetime | None) – Only list hub content versions that were created before the time specified.

• creation_time_after (datetime | None) – Only list hub content versions that were created after the time specified.

• sort_by (str | None) – Sort hub content versions by either name or creation time.

• sort_order (str | None) – Sort hub content versions by ascending or descending order.

• max_results – The maximum number of hub content versions to list.

• next_token – If the response to a previous ListHubContentVersions request was truncated, the response includes a NextToken. To retrieve the next set of hub content versions, use the token in the next request.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed HubContent.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[HubContent]

classmethod load(hub_content_name, hub_content_type, document_schema_version, hub_name, hub_content_document, hub_content_version=<sagemaker_core.main.utils.Unassigned object>, hub_content_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_content_description=<sagemaker_core.main.utils.Unassigned object>, hub_content_markdown=<sagemaker_core.main.utils.Unassigned object>, support_status=<sagemaker_core.main.utils.Unassigned object>, hub_content_search_keywords=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Import a HubContent resource

Parameters:

• hub_content_name (str) – The name of the hub content to import.

• hub_content_type (str) – The type of hub content to import.

• document_schema_version (str) – The version of the hub content schema to import.

• hub_name (str) – The name of the hub to import content into.

• hub_content_document (str) – The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.

• hub_content_version (str | None) – The version of the hub content to import.

• hub_content_display_name (str | None) – The display name of the hub content to import.

• hub_content_description (str | None) – A description of the hub content to import.

• hub_content_markdown (str | None) – A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.

• support_status (str | None) – The status of the hub content resource.

• hub_content_search_keywords (List[str] | None) – The searchable keywords of the hub content.

• tags (List[Tag] | None) – Any tags associated with the hub content.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The HubContent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

HubContent | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a HubContent resource

Returns:

The HubContent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

HubContent | None

update(hub_content_type, hub_content_version, hub_content_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_content_description=<sagemaker_core.main.utils.Unassigned object>, hub_content_markdown=<sagemaker_core.main.utils.Unassigned object>, hub_content_search_keywords=<sagemaker_core.main.utils.Unassigned object>, support_status=<sagemaker_core.main.utils.Unassigned object>)

Update a HubContent resource

Returns:

The HubContent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Parameters:

• hub_content_type (str)

• hub_content_version (str)

• hub_content_display_name (str | None)

• hub_content_description (str | None)

• hub_content_markdown (str | None)

• hub_content_search_keywords (List[str] | None)

• support_status (str | None)

Return type:

HubContent | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a HubContent resource to reach certain status.

Parameters:

• target_status (Literal['Supported', 'Deprecated', 'Restricted']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.HubContentReference(*, hub_name, sage_maker_public_hub_content_arn, hub_arn, hub_content_arn, hub_content_name=<sagemaker_core.main.utils.Unassigned object>, min_version=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource HubContentReference

Parameters:

• hub_name (str | object)

• sage_maker_public_hub_content_arn (str)

• hub_arn (str)

• hub_content_arn (str)

• hub_content_name (str | object | None)

• min_version (str | None)

• tags (List[Tag] | None)

hub_name

The name of the hub to add the hub content reference to.

Type:

str | object

sage_maker_public_hub_content_arn

The ARN of the public hub content to reference.

Type:

str

hub_arn

The ARN of the hub that the hub content reference was added to.

Type:

str

hub_content_arn

The ARN of the hub content.

Type:

str

hub_content_name

The name of the hub content to reference.

Type:

str | object | None

min_version

The minimum version of the hub content to reference.

Type:

str | None

tags

Any tags associated with the hub content to reference.

Type:

List[sagemaker_core.main.shapes.Tag] | None

classmethod create(hub_name, sage_maker_public_hub_content_arn, hub_content_name=<sagemaker_core.main.utils.Unassigned object>, min_version=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

Create a HubContentReference resource

Parameters:

• hub_name (str | object) – The name of the hub to add the hub content reference to.

• sage_maker_public_hub_content_arn (str) – The ARN of the public hub content to reference.

• hub_content_name (str | object | None) – The name of the hub content to reference.

• min_version (str | None) – The minimum version of the hub content to reference.

• tags (List[Tag] | None) – Any tags associated with the hub content to reference.

• session – Boto3 session.

• region – Region name.

Returns:

The HubContentReference resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

HubContentReference | None

delete(hub_content_type)

Delete a HubContentReference resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Parameters:

hub_content_type (str)

Return type:

None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

update(hub_content_type, min_version=<sagemaker_core.main.utils.Unassigned object>)

Update a HubContentReference resource

Parameters:

• hub_content_type (str) – The content type of the resource that you want to update. Only specify a ModelReference resource for this API. To update a Model or Notebook resource, use the UpdateHubContent API instead.

• min_version (str | None)

Returns:

The HubContentReference resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

HubContentReference | None

class sagemaker_core.main.resources.HumanTaskUi(*, human_task_ui_name, human_task_ui_arn=<sagemaker_core.main.utils.Unassigned object>, human_task_ui_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, ui_template=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource HumanTaskUi

Parameters:

• human_task_ui_name (str)

• human_task_ui_arn (str | None)

• human_task_ui_status (str | None)

• creation_time (datetime | None)

• ui_template (UiTemplateInfo | None)

human_task_ui_arn

The Amazon Resource Name (ARN) of the human task user interface (worker task template).

Type:

str | None

human_task_ui_name

The name of the human task user interface (worker task template).

Type:

str

creation_time

The timestamp when the human task user interface was created.

Type:

datetime.datetime | None

ui_template

Type:

sagemaker_core.main.shapes.UiTemplateInfo | None

human_task_ui_status

The status of the human task user interface (worker task template). Valid values are listed below.

Type:

str | None

classmethod create(human_task_ui_name, ui_template, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a HumanTaskUi resource

Parameters:

• human_task_ui_name (str) – The name of the user interface you are creating.

• ui_template (UiTemplate)

• tags (List[Tag] | None) – An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The HumanTaskUi resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

HumanTaskUi | None

delete()

Delete a HumanTaskUi resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(human_task_ui_name, session=None, region=None)

Get a HumanTaskUi resource

Parameters:

• human_task_ui_name (str) – The name of the human task user interface (worker task template) you want information about.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The HumanTaskUi resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

HumanTaskUi | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all HumanTaskUi resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.

• creation_time_before (datetime | None) – A filter that returns only human task user interfaces that were created before the specified timestamp.

• sort_order (str | None) – An optional value that specifies whether you want the results sorted in Ascending or Descending order.

• next_token – A token to resume pagination.

• max_results – The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed HumanTaskUi resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[HumanTaskUi]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a HumanTaskUi resource

Returns:

The HumanTaskUi resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

HumanTaskUi | None

wait_for_delete(poll=5, timeout=None)

Wait for a HumanTaskUi resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a HumanTaskUi resource to reach certain status.

Parameters:

• target_status (Literal['Active', 'Deleting']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.HyperParameterTuningJob(*, hyper_parameter_tuning_job_name, hyper_parameter_tuning_job_arn=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_job_config=<sagemaker_core.main.utils.Unassigned object>, training_job_definition=<sagemaker_core.main.utils.Unassigned object>, training_job_definitions=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_job_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, training_job_status_counters=<sagemaker_core.main.utils.Unassigned object>, objective_status_counters=<sagemaker_core.main.utils.Unassigned object>, best_training_job=<sagemaker_core.main.utils.Unassigned object>, overall_best_training_job=<sagemaker_core.main.utils.Unassigned object>, warm_start_config=<sagemaker_core.main.utils.Unassigned object>, autotune=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, tuning_job_completion_details=<sagemaker_core.main.utils.Unassigned object>, consumed_resources=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource HyperParameterTuningJob

Parameters:

• hyper_parameter_tuning_job_name (str)

• hyper_parameter_tuning_job_arn (str | None)

• hyper_parameter_tuning_job_config (HyperParameterTuningJobConfig | None)

• training_job_definition (HyperParameterTrainingJobDefinition | None)

• training_job_definitions (List[HyperParameterTrainingJobDefinition] | None)

• hyper_parameter_tuning_job_status (str | None)

• creation_time (datetime | None)

• hyper_parameter_tuning_end_time (datetime | None)

• last_modified_time (datetime | None)

• training_job_status_counters (TrainingJobStatusCounters | None)

• objective_status_counters (ObjectiveStatusCounters | None)

• best_training_job (HyperParameterTrainingJobSummary | None)

• overall_best_training_job (HyperParameterTrainingJobSummary | None)

• warm_start_config (HyperParameterTuningJobWarmStartConfig | None)

• autotune (Autotune | None)

• failure_reason (str | None)

• tuning_job_completion_details (HyperParameterTuningJobCompletionDetails | None)

• consumed_resources (HyperParameterTuningJobConsumedResources | None)

hyper_parameter_tuning_job_name

The name of the hyperparameter tuning job.

Type:

str

hyper_parameter_tuning_job_arn

The Amazon Resource Name (ARN) of the tuning job.

Type:

str | None

hyper_parameter_tuning_job_config

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobConfig | None

hyper_parameter_tuning_job_status

The status of the tuning job.

Type:

str | None

creation_time

The date and time that the tuning job started.

Type:

datetime.datetime | None

training_job_status_counters

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

Type:

sagemaker_core.main.shapes.TrainingJobStatusCounters | None

objective_status_counters

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

Type:

sagemaker_core.main.shapes.ObjectiveStatusCounters | None

training_job_definition

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

Type:

sagemaker_core.main.shapes.HyperParameterTrainingJobDefinition | None

training_job_definitions

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

Type:

List[sagemaker_core.main.shapes.HyperParameterTrainingJobDefinition] | None

hyper_parameter_tuning_end_time

The date and time that the tuning job ended.

Type:

datetime.datetime | None

last_modified_time

The date and time that the status of the tuning job was modified.

Type:

datetime.datetime | None

best_training_job

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

Type:

sagemaker_core.main.shapes.HyperParameterTrainingJobSummary | None

overall_best_training_job

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

Type:

sagemaker_core.main.shapes.HyperParameterTrainingJobSummary | None

warm_start_config

The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobWarmStartConfig | None

autotune

A flag to indicate if autotune is enabled for the hyperparameter tuning job.

Type:

sagemaker_core.main.shapes.Autotune | None

failure_reason

If the tuning job failed, the reason it failed.

Type:

str | None

tuning_job_completion_details

Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobCompletionDetails | None

consumed_resources

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobConsumedResources | None

classmethod create(hyper_parameter_tuning_job_name, hyper_parameter_tuning_job_config, training_job_definition=<sagemaker_core.main.utils.Unassigned object>, training_job_definitions=<sagemaker_core.main.utils.Unassigned object>, warm_start_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, autotune=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a HyperParameterTuningJob resource

Parameters:

• hyper_parameter_tuning_job_name (str) – The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

• hyper_parameter_tuning_job_config (HyperParameterTuningJobConfig) – The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

• training_job_definition (HyperParameterTrainingJobDefinition | None) – The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

• training_job_definitions (List[HyperParameterTrainingJobDefinition] | None) – A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

• warm_start_config (HyperParameterTuningJobWarmStartConfig | None) – Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job. All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

• autotune (Autotune | None) – Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields: ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job. RetryStrategy: The number of times to retry a training job. Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches. ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The HyperParameterTuningJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

HyperParameterTuningJob | None

delete()

Delete a HyperParameterTuningJob resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(hyper_parameter_tuning_job_name, session=None, region=None)

Get a HyperParameterTuningJob resource

Parameters:

• hyper_parameter_tuning_job_name (str) – The name of the tuning job.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The HyperParameterTuningJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

HyperParameterTuningJob | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all HyperParameterTuningJob resources

Parameters:

• next_token – If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

• max_results – The maximum number of tuning jobs to return. The default value is 10.

• sort_by (str | None) – The field to sort results by. The default is Name.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• name_contains (str | None) – A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

• creation_time_after (datetime | None) – A filter that returns only tuning jobs that were created after the specified time.

• creation_time_before (datetime | None) – A filter that returns only tuning jobs that were created before the specified time.

• last_modified_time_after (datetime | None) – A filter that returns only tuning jobs that were modified after the specified time.

• last_modified_time_before (datetime | None) – A filter that returns only tuning jobs that were modified before the specified time.

• status_equals (str | None) – A filter that returns only tuning jobs with the specified status.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed HyperParameterTuningJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[HyperParameterTuningJob]

get_all_training_jobs(status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Parameters:

• next_token – If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

• max_results – The maximum number of training jobs to return. The default value is 10.

• status_equals (str | None) – A filter that returns only training jobs with the specified status.

• sort_by (str | None) – The field to sort results by. The default is Name. If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed HyperParameterTrainingJobSummary.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[HyperParameterTrainingJobSummary]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a HyperParameterTuningJob resource

Returns:

The HyperParameterTuningJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

HyperParameterTuningJob | None

stop()

Stop a HyperParameterTuningJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None)

Wait for a HyperParameterTuningJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_delete(poll=5, timeout=None)

Wait for a HyperParameterTuningJob resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Image(*, image_name, creation_time=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, image_arn=<sagemaker_core.main.utils.Unassigned object>, image_status=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Image

Parameters:

• image_name (str)

• creation_time (datetime | None)

• description (str | None)

• display_name (str | None)

• failure_reason (str | None)

• image_arn (str | None)

• image_status (str | None)

• last_modified_time (datetime | None)

• role_arn (str | None)

creation_time

When the image was created.

Type:

datetime.datetime | None

description

The description of the image.

Type:

str | None

display_name

The name of the image as displayed.

Type:

str | None

failure_reason

When a create, update, or delete operation fails, the reason for the failure.

Type:

str | None

image_arn

The ARN of the image.

Type:

str | None

image_name

The name of the image.

Type:

str

image_status

The status of the image.

Type:

str | None

last_modified_time

When the image was last modified.

Type:

datetime.datetime | None

role_arn

The ARN of the IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

Type:

str | None

classmethod create(image_name, role_arn, description=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Image resource

Parameters:

• image_name (str) – The name of the image. Must be unique to your account.

• role_arn (str) – The ARN of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

• description (str | None) – The description of the image.

• display_name (str | None) – The display name of the image. If not provided, ImageName is displayed.

• tags (List[Tag] | None) – A list of tags to apply to the image.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Image resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Image | None

delete()

Delete a Image resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(image_name, session=None, region=None)

Get a Image resource

Parameters:

• image_name (str) – The name of the image to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Image resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Image | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Image resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only images created on or after the specified time.

• creation_time_before (datetime | None) – A filter that returns only images created on or before the specified time.

• last_modified_time_after (datetime | None) – A filter that returns only images modified on or after the specified time.

• last_modified_time_before (datetime | None) – A filter that returns only images modified on or before the specified time.

• max_results – The maximum number of images to return in the response. The default value is 10.

• name_contains (str | None) – A filter that returns only images whose name contains the specified string.

• next_token – If the previous call to ListImages didn’t return the full set of images, the call returns a token for getting the next set of images.

• sort_by (str | None) – The property used to sort results. The default value is CREATION_TIME.

• sort_order (str | None) – The sort order. The default value is DESCENDING.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Image resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Image]

get_all_aliases(alias=<sagemaker_core.main.utils.Unassigned object>, version=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Lists the aliases of a specified image or image version.

Parameters:

• alias (str | None) – The alias of the image version.

• version (int | None) – The version of the image. If image version is not specified, the aliases of all versions of the image are listed.

• max_results – The maximum number of aliases to return.

• next_token – If the previous call to ListAliases didn’t return the full set of aliases, the call returns a token for retrieving the next set of aliases.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed str.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[str]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Image resource

Returns:

The Image resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Image | None

update(delete_properties=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>)

Update a Image resource

Parameters:

• delete_properties (List[str] | None) – A list of properties to delete. Only the Description and DisplayName properties can be deleted.

• description (str | None)

• display_name (str | None)

• role_arn (str | None)

Returns:

The Image resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

Image | None

wait_for_delete(poll=5, timeout=None)

Wait for a Image resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Image resource to reach certain status.

Parameters:

• target_status (Literal['CREATING', 'CREATED', 'CREATE_FAILED', 'UPDATING', 'UPDATE_FAILED', 'DELETING', 'DELETE_FAILED']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ImageVersion(*, image_name, base_image=<sagemaker_core.main.utils.Unassigned object>, container_image=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, image_arn=<sagemaker_core.main.utils.Unassigned object>, image_version_arn=<sagemaker_core.main.utils.Unassigned object>, image_version_status=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, version=<sagemaker_core.main.utils.Unassigned object>, vendor_guidance=<sagemaker_core.main.utils.Unassigned object>, job_type=<sagemaker_core.main.utils.Unassigned object>, ml_framework=<sagemaker_core.main.utils.Unassigned object>, programming_lang=<sagemaker_core.main.utils.Unassigned object>, processor=<sagemaker_core.main.utils.Unassigned object>, horovod=<sagemaker_core.main.utils.Unassigned object>, release_notes=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ImageVersion

Parameters:

• image_name (str)

• base_image (str | None)

• container_image (str | None)

• creation_time (datetime | None)

• failure_reason (str | None)

• image_arn (str | None)

• image_version_arn (str | None)

• image_version_status (str | None)

• last_modified_time (datetime | None)

• version (int | None)

• vendor_guidance (str | None)

• job_type (str | None)

• ml_framework (str | None)

• programming_lang (str | None)

• processor (str | None)

• horovod (bool | None)

• release_notes (str | None)

base_image

The registry path of the container image on which this image version is based.

Type:

str | None

container_image

The registry path of the container image that contains this image version.

Type:

str | None

creation_time

When the version was created.

Type:

datetime.datetime | None

failure_reason

When a create or delete operation fails, the reason for the failure.

Type:

str | None

image_arn

The ARN of the image the version is based on.

Type:

str | None

image_version_arn

The ARN of the version.

Type:

str | None

image_version_status

The status of the version.

Type:

str | None

last_modified_time

When the version was last modified.

Type:

datetime.datetime | None

version

The version number.

Type:

int | None

vendor_guidance

The stability of the image version specified by the maintainer. NOT_PROVIDED: The maintainers did not provide a status for image version stability. STABLE: The image version is stable. TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

Type:

str | None

job_type

Indicates SageMaker AI job type compatibility. TRAINING: The image version is compatible with SageMaker AI training jobs. INFERENCE: The image version is compatible with SageMaker AI inference jobs. NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.

Type:

str | None

ml_framework

The machine learning framework vended in the image version.

Type:

str | None

programming_lang

The supported programming language and its version.

Type:

str | None

processor

Indicates CPU or GPU compatibility. CPU: The image version is compatible with CPU. GPU: The image version is compatible with GPU.

Type:

str | None

horovod

Indicates Horovod compatibility.

Type:

bool | None

release_notes

The maintainer description of the image version.

Type:

str | None

classmethod create(base_image, client_token, image_name, aliases=<sagemaker_core.main.utils.Unassigned object>, vendor_guidance=<sagemaker_core.main.utils.Unassigned object>, job_type=<sagemaker_core.main.utils.Unassigned object>, ml_framework=<sagemaker_core.main.utils.Unassigned object>, programming_lang=<sagemaker_core.main.utils.Unassigned object>, processor=<sagemaker_core.main.utils.Unassigned object>, horovod=<sagemaker_core.main.utils.Unassigned object>, release_notes=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ImageVersion resource

Parameters:

• base_image (str) – The registry path of the container image to use as the starting point for this version. The path is an Amazon ECR URI in the following format: &lt;acct-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;repo-name[:tag] or [@digest]&gt;

• client_token (str) – A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.

• image_name (str | object) – The ImageName of the Image to create a version of.

• aliases (List[str] | None) – A list of aliases created with the image version.

• vendor_guidance (str | None) – The stability of the image version, specified by the maintainer. NOT_PROVIDED: The maintainers did not provide a status for image version stability. STABLE: The image version is stable. TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

• job_type (str | None) – Indicates SageMaker AI job type compatibility. TRAINING: The image version is compatible with SageMaker AI training jobs. INFERENCE: The image version is compatible with SageMaker AI inference jobs. NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.

• ml_framework (str | None) – The machine learning framework vended in the image version.

• programming_lang (str | None) – The supported programming language and its version.

• processor (str | None) – Indicates CPU or GPU compatibility. CPU: The image version is compatible with CPU. GPU: The image version is compatible with GPU.

• horovod (bool | None) – Indicates Horovod compatibility.

• release_notes (str | None) – The maintainer description of the image version.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ImageVersion resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ImageVersion | None

delete(alias=<sagemaker_core.main.utils.Unassigned object>)

Delete a ImageVersion resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Parameters:

alias (str | None)

Return type:

None

classmethod get(image_name, version=<sagemaker_core.main.utils.Unassigned object>, alias=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a ImageVersion resource

Parameters:

• image_name (str) – The name of the image.

• version (int | None) – The version of the image. If not specified, the latest version is described.

• alias (str | None) – The alias of the image version.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ImageVersion resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ImageVersion | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh(alias=<sagemaker_core.main.utils.Unassigned object>)

Refresh a ImageVersion resource

Returns:

The ImageVersion resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Parameters:

alias (str | None)

Return type:

ImageVersion | None

update(alias=<sagemaker_core.main.utils.Unassigned object>, version=<sagemaker_core.main.utils.Unassigned object>, aliases_to_add=<sagemaker_core.main.utils.Unassigned object>, aliases_to_delete=<sagemaker_core.main.utils.Unassigned object>, vendor_guidance=<sagemaker_core.main.utils.Unassigned object>, job_type=<sagemaker_core.main.utils.Unassigned object>, ml_framework=<sagemaker_core.main.utils.Unassigned object>, programming_lang=<sagemaker_core.main.utils.Unassigned object>, processor=<sagemaker_core.main.utils.Unassigned object>, horovod=<sagemaker_core.main.utils.Unassigned object>, release_notes=<sagemaker_core.main.utils.Unassigned object>)

Update a ImageVersion resource

Parameters:

• alias (str | None) – The alias of the image version.

• aliases_to_add (List[str] | None) – A list of aliases to add.

• aliases_to_delete (List[str] | None) – A list of aliases to delete.

• version (int | None)

• vendor_guidance (str | None)

• job_type (str | None)

• ml_framework (str | None)

• programming_lang (str | None)

• processor (str | None)

• horovod (bool | None)

• release_notes (str | None)

Returns:

The ImageVersion resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

ImageVersion | None

wait_for_delete(poll=5, timeout=None)

Wait for a ImageVersion resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a ImageVersion resource to reach certain status.

Parameters:

• target_status (Literal['CREATING', 'CREATED', 'CREATE_FAILED', 'DELETING', 'DELETE_FAILED']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.InferenceComponent(*, inference_component_name, inference_component_arn=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, endpoint_arn=<sagemaker_core.main.utils.Unassigned object>, variant_name=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, specification=<sagemaker_core.main.utils.Unassigned object>, runtime_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, inference_component_status=<sagemaker_core.main.utils.Unassigned object>, last_deployment_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource InferenceComponent

Parameters:

• inference_component_name (str)

• inference_component_arn (str | None)

• endpoint_name (str | None)

• endpoint_arn (str | None)

• variant_name (str | None)

• failure_reason (str | None)

• specification (InferenceComponentSpecificationSummary | None)

• runtime_config (InferenceComponentRuntimeConfigSummary | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• inference_component_status (str | None)

• last_deployment_config (InferenceComponentDeploymentConfig | None)

inference_component_name

The name of the inference component.

Type:

str

inference_component_arn

The Amazon Resource Name (ARN) of the inference component.

Type:

str | None

endpoint_name

The name of the endpoint that hosts the inference component.

Type:

str | None

endpoint_arn

The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.

Type:

str | None

creation_time

The time when the inference component was created.

Type:

datetime.datetime | None

last_modified_time

The time when the inference component was last updated.

Type:

datetime.datetime | None

variant_name

The name of the production variant that hosts the inference component.

Type:

str | None

failure_reason

If the inference component status is Failed, the reason for the failure.

Type:

str | None

specification

Details about the resources that are deployed with this inference component.

Type:

sagemaker_core.main.shapes.InferenceComponentSpecificationSummary | None

runtime_config

Details about the runtime settings for the model that is deployed with the inference component.

Type:

sagemaker_core.main.shapes.InferenceComponentRuntimeConfigSummary | None

inference_component_status

The status of the inference component.

Type:

str | None

last_deployment_config

The deployment and rollback settings that you assigned to the inference component.

Type:

sagemaker_core.main.shapes.InferenceComponentDeploymentConfig | None

classmethod create(inference_component_name, endpoint_name, specification, variant_name=<sagemaker_core.main.utils.Unassigned object>, runtime_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a InferenceComponent resource

Parameters:

• inference_component_name (str) – A unique name to assign to the inference component.

• endpoint_name (str | object) – The name of an existing endpoint where you host the inference component.

• specification (InferenceComponentSpecification) – Details about the resources to deploy with this inference component, including the model, container, and compute resources.

• variant_name (str | None) – The name of an existing production variant where you host the inference component.

• runtime_config (InferenceComponentRuntimeConfig | None) – Runtime settings for a model that is deployed with an inference component.

• tags (List[Tag] | None) – A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The InferenceComponent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

InferenceComponent | None

delete()

Delete a InferenceComponent resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(inference_component_name, session=None, region=None)

Get a InferenceComponent resource

Parameters:

• inference_component_name (str) – The name of the inference component.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The InferenceComponent resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

InferenceComponent | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, endpoint_name_equals=<sagemaker_core.main.utils.Unassigned object>, variant_name_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all InferenceComponent resources

Parameters:

• sort_by (str | None) – The field by which to sort the inference components in the response. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Descending.

• next_token – A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

• max_results – The maximum number of inference components to return in the response. This value defaults to 10.

• name_contains (str | None) – Filters the results to only those inference components with a name that contains the specified string.

• creation_time_before (datetime | None) – Filters the results to only those inference components that were created before the specified time.

• creation_time_after (datetime | None) – Filters the results to only those inference components that were created after the specified time.

• last_modified_time_before (datetime | None) – Filters the results to only those inference components that were updated before the specified time.

• last_modified_time_after (datetime | None) – Filters the results to only those inference components that were updated after the specified time.

• status_equals (str | None) – Filters the results to only those inference components with the specified status.

• endpoint_name_equals (str | None) – An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.

• variant_name_equals (str | None) – A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed InferenceComponent resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[InferenceComponent]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a InferenceComponent resource

Returns:

The InferenceComponent resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

InferenceComponent | None

update(specification=<sagemaker_core.main.utils.Unassigned object>, runtime_config=<sagemaker_core.main.utils.Unassigned object>, deployment_config=<sagemaker_core.main.utils.Unassigned object>)

Update a InferenceComponent resource

Parameters:

• deployment_config (InferenceComponentDeploymentConfig | None) – The deployment configuration for the inference component. The configuration contains the desired deployment strategy and rollback settings.

• specification (InferenceComponentSpecification | None)

• runtime_config (InferenceComponentRuntimeConfig | None)

Returns:

The InferenceComponent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

InferenceComponent | None

update_runtime_configs(desired_runtime_config, session=None, region=None)

Runtime settings for a model that is deployed with an inference component.

Parameters:

• desired_runtime_config (InferenceComponentRuntimeConfig) – Runtime settings for a model that is deployed with an inference component.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

None

wait_for_delete(poll=5, timeout=None)

Wait for a InferenceComponent resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a InferenceComponent resource to reach certain status.

Parameters:

• target_status (Literal['InService', 'Creating', 'Updating', 'Failed', 'Deleting']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.InferenceExperiment(*, name, arn=<sagemaker_core.main.utils.Unassigned object>, type=<sagemaker_core.main.utils.Unassigned object>, schedule=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, status_reason=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, completion_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, endpoint_metadata=<sagemaker_core.main.utils.Unassigned object>, model_variants=<sagemaker_core.main.utils.Unassigned object>, data_storage_config=<sagemaker_core.main.utils.Unassigned object>, shadow_mode_config=<sagemaker_core.main.utils.Unassigned object>, kms_key=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource InferenceExperiment

Parameters:

• name (str)

• arn (str | None)

• type (str | None)

• schedule (InferenceExperimentSchedule | None)

• status (str | None)

• status_reason (str | None)

• description (str | None)

• creation_time (datetime | None)

• completion_time (datetime | None)

• last_modified_time (datetime | None)

• role_arn (str | None)

• endpoint_metadata (EndpointMetadata | None)

• model_variants (List[ModelVariantConfigSummary] | None)

• data_storage_config (InferenceExperimentDataStorageConfig | None)

• shadow_mode_config (ShadowModeConfig | None)

• kms_key (str | None)

arn

The ARN of the inference experiment being described.

Type:

str | None

name

The name of the inference experiment.

Type:

str

type

The type of the inference experiment.

Type:

str | None

status

The status of the inference experiment. The following are the possible statuses for an inference experiment: Creating - Amazon SageMaker is creating your experiment. Created - Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time. Updating - When you make changes to your experiment, your experiment shows as updating. Starting - Amazon SageMaker is beginning your experiment. Running - Your experiment is in progress. Stopping - Amazon SageMaker is stopping your experiment. Completed - Your experiment has completed. Cancelled - When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.

Type:

str | None

endpoint_metadata

The metadata of the endpoint on which the inference experiment ran.

Type:

sagemaker_core.main.shapes.EndpointMetadata | None

model_variants

An array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant.

Type:

List[sagemaker_core.main.shapes.ModelVariantConfigSummary] | None

schedule

The duration for which the inference experiment ran or will run.

Type:

sagemaker_core.main.shapes.InferenceExperimentSchedule | None

status_reason

The error message or client-specified Reason from the StopInferenceExperiment API, that explains the status of the inference experiment.

Type:

str | None

description

The description of the inference experiment.

Type:

str | None

creation_time

The timestamp at which you created the inference experiment.

Type:

datetime.datetime | None

completion_time

The timestamp at which the inference experiment was completed.

Type:

datetime.datetime | None

last_modified_time

The timestamp at which you last modified the inference experiment.

Type:

datetime.datetime | None

role_arn

The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

Type:

str | None

data_storage_config

The Amazon S3 location and configuration for storing inference request and response data.

Type:

sagemaker_core.main.shapes.InferenceExperimentDataStorageConfig | None

shadow_mode_config

The configuration of ShadowMode inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates.

Type:

sagemaker_core.main.shapes.ShadowModeConfig | None

kms_key

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment.

Type:

str | None

classmethod create(name, type, role_arn, endpoint_name, model_variants, shadow_mode_config, schedule=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, data_storage_config=<sagemaker_core.main.utils.Unassigned object>, kms_key=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a InferenceExperiment resource

Parameters:

• name (str) – The name for the inference experiment.

• type (str) – The type of the inference experiment that you want to run. The following types of experiments are possible: ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.

• role_arn (str) – The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

• endpoint_name (str | object) – The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.

• model_variants (List[ModelVariantConfig]) – An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant.

• shadow_mode_config (ShadowModeConfig) – The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

• schedule (InferenceExperimentSchedule | None) – The duration for which you want the inference experiment to run. If you don’t specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.

• description (str | None) – A description for the inference experiment.

• data_storage_config (InferenceExperimentDataStorageConfig | None) – The Amazon S3 location and configuration for storing inference request and response data. This is an optional parameter that you can use for data capture. For more information, see Capture data.

• kms_key (str | None) – The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats: KMS key ID “1234abcd-12ab-34cd-56ef-1234567890ab” Amazon Resource Name (ARN) of a KMS key “arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab” KMS key Alias “alias/ExampleAlias” Amazon Resource Name (ARN) of a KMS key Alias “arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias” If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to “aws:kms”. For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

• tags (List[Tag] | None) – Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The InferenceExperiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

InferenceExperiment | None

delete()

Delete a InferenceExperiment resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(name, session=None, region=None)

Get a InferenceExperiment resource

Parameters:

• name (str) – The name of the inference experiment to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The InferenceExperiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

InferenceExperiment | None

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, type=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all InferenceExperiment resources

Parameters:

• name_contains (str | None) – Selects inference experiments whose names contain this name.

• type (str | None) – Selects inference experiments of this type. For the possible types of inference experiments, see CreateInferenceExperiment.

• status_equals (str | None) – Selects inference experiments which are in this status. For the possible statuses, see DescribeInferenceExperiment.

• creation_time_after (datetime | None) – Selects inference experiments which were created after this timestamp.

• creation_time_before (datetime | None) – Selects inference experiments which were created before this timestamp.

• last_modified_time_after (datetime | None) – Selects inference experiments which were last modified after this timestamp.

• last_modified_time_before (datetime | None) – Selects inference experiments which were last modified before this timestamp.

• sort_by (str | None) – The column by which to sort the listed inference experiments.

• sort_order (str | None) – The direction of sorting (ascending or descending).

• next_token – The response from the last list when returning a list large enough to need tokening.

• max_results – The maximum number of results to select.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed InferenceExperiment resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[InferenceExperiment]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a InferenceExperiment resource

Returns:

The InferenceExperiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

InferenceExperiment | None

stop()

Stop a InferenceExperiment resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

update(schedule=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, model_variants=<sagemaker_core.main.utils.Unassigned object>, data_storage_config=<sagemaker_core.main.utils.Unassigned object>, shadow_mode_config=<sagemaker_core.main.utils.Unassigned object>)

Update a InferenceExperiment resource

Returns:

The InferenceExperiment resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Parameters:

• schedule (InferenceExperimentSchedule | None)

• description (str | None)

• model_variants (List[ModelVariantConfig] | None)

• data_storage_config (InferenceExperimentDataStorageConfig | None)

• shadow_mode_config (ShadowModeConfig | None)

Return type:

InferenceExperiment | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a InferenceExperiment resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'Created', 'Updating', 'Running', 'Starting', 'Stopping', 'Completed', 'Cancelled']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.InferenceRecommendationsJob(*, job_name, job_description=<sagemaker_core.main.utils.Unassigned object>, job_type=<sagemaker_core.main.utils.Unassigned object>, job_arn=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, completion_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, input_config=<sagemaker_core.main.utils.Unassigned object>, stopping_conditions=<sagemaker_core.main.utils.Unassigned object>, inference_recommendations=<sagemaker_core.main.utils.Unassigned object>, endpoint_performances=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource InferenceRecommendationsJob

Parameters:

• job_name (str)

• job_description (str | None)

• job_type (str | None)

• job_arn (str | None)

• role_arn (str | None)

• status (str | None)

• creation_time (datetime | None)

• completion_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• input_config (RecommendationJobInputConfig | None)

• stopping_conditions (RecommendationJobStoppingConditions | None)

• inference_recommendations (List[InferenceRecommendation] | None)

• endpoint_performances (List[EndpointPerformance] | None)

job_name

The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Type:

str

job_type

The job type that you provided when you initiated the job.

Type:

str | None

job_arn

The Amazon Resource Name (ARN) of the job.

Type:

str | None

role_arn

The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.

Type:

str | None

status

The status of the job.

Type:

str | None

creation_time

A timestamp that shows when the job was created.

Type:

datetime.datetime | None

last_modified_time

A timestamp that shows when the job was last modified.

Type:

datetime.datetime | None

input_config

Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.

Type:

sagemaker_core.main.shapes.RecommendationJobInputConfig | None

job_description

The job description that you provided when you initiated the job.

Type:

str | None

completion_time

A timestamp that shows when the job completed.

Type:

datetime.datetime | None

failure_reason

If the job fails, provides information why the job failed.

Type:

str | None

stopping_conditions

The stopping conditions that you provided when you initiated the job.

Type:

sagemaker_core.main.shapes.RecommendationJobStoppingConditions | None

inference_recommendations

The recommendations made by Inference Recommender.

Type:

List[sagemaker_core.main.shapes.InferenceRecommendation] | None

endpoint_performances

The performance results from running an Inference Recommender job on an existing endpoint.

Type:

List[sagemaker_core.main.shapes.EndpointPerformance] | None

classmethod create(job_name, job_type, role_arn, input_config, job_description=<sagemaker_core.main.utils.Unassigned object>, stopping_conditions=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a InferenceRecommendationsJob resource

Parameters:

• job_name (str) – A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.

• job_type (str) – Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT) job.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

• input_config (RecommendationJobInputConfig) – Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.

• job_description (str | None) – Description of the recommendation job.

• stopping_conditions (RecommendationJobStoppingConditions | None) – A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.

• output_config (RecommendationJobOutputConfig | None) – Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.

• tags (List[Tag] | None) – The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The InferenceRecommendationsJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

InferenceRecommendationsJob | None

classmethod get(job_name, session=None, region=None)

Get a InferenceRecommendationsJob resource

Parameters:

• job_name (str) – The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The InferenceRecommendationsJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

InferenceRecommendationsJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, model_name_equals=<sagemaker_core.main.utils.Unassigned object>, model_package_version_arn_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all InferenceRecommendationsJob resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only jobs created after the specified time (timestamp).

• creation_time_before (datetime | None) – A filter that returns only jobs created before the specified time (timestamp).

• last_modified_time_after (datetime | None) – A filter that returns only jobs that were last modified after the specified time (timestamp).

• last_modified_time_before (datetime | None) – A filter that returns only jobs that were last modified before the specified time (timestamp).

• name_contains (str | None) – A string in the job name. This filter returns only recommendations whose name contains the specified string.

• status_equals (str | None) – A filter that retrieves only inference recommendations jobs with a specific status.

• sort_by (str | None) – The parameter by which to sort the results.

• sort_order (str | None) – The sort order for the results.

• next_token – If the response to a previous ListInferenceRecommendationsJobsRequest request was truncated, the response includes a NextToken. To retrieve the next set of recommendations, use the token in the next request.

• max_results – The maximum number of recommendations to return in the response.

• model_name_equals (str | None) – A filter that returns only jobs that were created for this model.

• model_package_version_arn_equals (str | None) – A filter that returns only jobs that were created for this versioned model package.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed InferenceRecommendationsJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[InferenceRecommendationsJob]

get_all_steps(step_type=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Returns a list of the subtasks for an Inference Recommender job.

Parameters:

• step_type (str | None) – A filter to return details about the specified type of subtask. BENCHMARK: Evaluate the performance of your model on different instance types.

• max_results – The maximum number of results to return.

• next_token – A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed InferenceRecommendationsJobStep.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[InferenceRecommendationsJobStep]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a InferenceRecommendationsJob resource

Returns:

The InferenceRecommendationsJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

InferenceRecommendationsJob | None

stop()

Stop a InferenceRecommendationsJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None)

Wait for a InferenceRecommendationsJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_delete(poll=5, timeout=None)

Wait for a InferenceRecommendationsJob resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.LabelingJob(*, labeling_job_name, labeling_job_status=<sagemaker_core.main.utils.Unassigned object>, label_counters=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, job_reference_code=<sagemaker_core.main.utils.Unassigned object>, labeling_job_arn=<sagemaker_core.main.utils.Unassigned object>, label_attribute_name=<sagemaker_core.main.utils.Unassigned object>, input_config=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, label_category_config_s3_uri=<sagemaker_core.main.utils.Unassigned object>, stopping_conditions=<sagemaker_core.main.utils.Unassigned object>, labeling_job_algorithms_config=<sagemaker_core.main.utils.Unassigned object>, human_task_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, labeling_job_output=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource LabelingJob

Parameters:

• labeling_job_name (str)

• labeling_job_status (str | None)

• label_counters (LabelCounters | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• job_reference_code (str | None)

• labeling_job_arn (str | None)

• label_attribute_name (str | None)

• input_config (LabelingJobInputConfig | None)

• output_config (LabelingJobOutputConfig | None)

• role_arn (str | None)

• label_category_config_s3_uri (str | None)

• stopping_conditions (LabelingJobStoppingConditions | None)

• labeling_job_algorithms_config (LabelingJobAlgorithmsConfig | None)

• human_task_config (HumanTaskConfig | None)

• tags (List[Tag] | None)

• labeling_job_output (LabelingJobOutput | None)

labeling_job_status

The processing status of the labeling job.

Type:

str | None

label_counters

Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn’t be labeled, and the total number of objects labeled.

Type:

sagemaker_core.main.shapes.LabelCounters | None

creation_time

The date and time that the labeling job was created.

Type:

datetime.datetime | None

last_modified_time

The date and time that the labeling job was last updated.

Type:

datetime.datetime | None

job_reference_code

A unique identifier for work done as part of a labeling job.

Type:

str | None

labeling_job_name

The name assigned to the labeling job when it was created.

Type:

str

labeling_job_arn

The Amazon Resource Name (ARN) of the labeling job.

Type:

str | None

input_config

Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

Type:

sagemaker_core.main.shapes.LabelingJobInputConfig | None

output_config

The location of the job’s output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.

Type:

sagemaker_core.main.shapes.LabelingJobOutputConfig | None

role_arn

The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during data labeling.

Type:

str | None

human_task_config

Configuration information required for human workers to complete a labeling task.

Type:

sagemaker_core.main.shapes.HumanTaskConfig | None

failure_reason

If the job failed, the reason that it failed.

Type:

str | None

label_attribute_name

The attribute used as the label in the output manifest file.

Type:

str | None

label_category_config_s3_uri

The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits: Semantic segmentation labeling jobs using automated labeling: 20 labels Box bounding labeling jobs (all): 10 labels The file is a JSON structure in the following format: { “document-version”: “2018-11-28” “labels”: [ { “label”: “label 1” }, { “label”: “label 2” }, … { “label”: “label n” } ] }

Type:

str | None

stopping_conditions

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.

Type:

sagemaker_core.main.shapes.LabelingJobStoppingConditions | None

labeling_job_algorithms_config

Configuration information for automated data labeling.

Type:

sagemaker_core.main.shapes.LabelingJobAlgorithmsConfig | None

tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Type:

List[sagemaker_core.main.shapes.Tag] | None

labeling_job_output

The location of the output produced by the labeling job.

Type:

sagemaker_core.main.shapes.LabelingJobOutput | None

classmethod create(labeling_job_name, label_attribute_name, input_config, output_config, role_arn, human_task_config, label_category_config_s3_uri=<sagemaker_core.main.utils.Unassigned object>, stopping_conditions=<sagemaker_core.main.utils.Unassigned object>, labeling_job_algorithms_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a LabelingJob resource

Parameters:

• labeling_job_name (str) – The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.

• label_attribute_name (str) – The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements. The name can’t end with “-metadata”. If you are using one of the following built-in task types, the attribute name must end with “-ref”. If the task type you are using is not listed below, the attribute name must not end with “-ref”. Image semantic segmentation (SemanticSegmentation), and adjustment (AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation) labeling jobs for this task type. Video frame object detection (VideoObjectDetection), and adjustment and verification (AdjustmentVideoObjectDetection) labeling jobs for this task type. Video frame object tracking (VideoObjectTracking), and adjustment and verification (AdjustmentVideoObjectTracking) labeling jobs for this task type. 3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type. 3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (Adjustment3DPointCloudObjectTracking) labeling jobs for this task type. If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.

• input_config (LabelingJobInputConfig) – Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects. You must specify at least one of the following: S3DataSource or SnsDataSource. Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled. Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job. If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.

• output_config (LabelingJobOutputConfig) – The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.

• role_arn (str) – The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

• human_task_config (HumanTaskConfig) – Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

• label_category_config_s3_uri (str | None) – The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects. For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs. For named entity recognition jobs, in addition to “labels”, you must provide worker instructions in the label category configuration file using the “instructions” parameter: “instructions”: {“shortInstruction”:”&lt;h1&gt;Add header&lt;/h1&gt;&lt;p&gt;Add Instructions&lt;/p&gt;”, “fullInstruction”:”&lt;p&gt;Add additional instructions.&lt;/p&gt;”}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) . For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1, label_2,…,label_n with your label categories. { “document-version”: “2018-11-28”, “labels”: [{“label”: “label_1”},{“label”: “label_2”},…{“label”: “label_n”}] } Note the following about the label category configuration file: For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one. Each label category must be unique, you cannot specify duplicate label categories. If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.

• stopping_conditions (LabelingJobStoppingConditions | None) – A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

• labeling_job_algorithms_config (LabelingJobAlgorithmsConfig | None) – Configures the information required to perform automated data labeling.

• tags (List[Tag] | None) – An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The LabelingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

LabelingJob | None

classmethod get(labeling_job_name, session=None, region=None)

Get a LabelingJob resource

Parameters:

• labeling_job_name (str) – The name of the labeling job to return information for.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The LabelingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

LabelingJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all LabelingJob resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only labeling jobs created after the specified time (timestamp).

• creation_time_before (datetime | None) – A filter that returns only labeling jobs created before the specified time (timestamp).

• last_modified_time_after (datetime | None) – A filter that returns only labeling jobs modified after the specified time (timestamp).

• last_modified_time_before (datetime | None) – A filter that returns only labeling jobs modified before the specified time (timestamp).

• max_results – The maximum number of labeling jobs to return in each page of the response.

• next_token – If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

• name_contains (str | None) – A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• status_equals (str | None) – A filter that retrieves only labeling jobs with a specific status.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed LabelingJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[LabelingJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a LabelingJob resource

Returns:

The LabelingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

LabelingJob | None

stop()

Stop a LabelingJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None)

Wait for a LabelingJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.LineageGroup(*, lineage_group_name, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource LineageGroup

Parameters:

• lineage_group_name (str)

• lineage_group_arn (str | None)

• display_name (str | None)

• description (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

lineage_group_name

The name of the lineage group.

Type:

str

lineage_group_arn

The Amazon Resource Name (ARN) of the lineage group.

Type:

str | None

display_name

The display name of the lineage group.

Type:

str | None

description

The description of the lineage group.

Type:

str | None

creation_time

The creation time of lineage group.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

The last modified time of the lineage group.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod get(lineage_group_name, session=None, region=None)

Get a LineageGroup resource

Parameters:

• lineage_group_name (str) – The name of the lineage group.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The LineageGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

LineageGroup | None

classmethod get_all(created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all LineageGroup resources

Parameters:

• created_after (datetime | None) – A timestamp to filter against lineage groups created after a certain point in time.

• created_before (datetime | None) – A timestamp to filter against lineage groups created before a certain point in time.

• sort_by (str | None) – The parameter by which to sort the results. The default is CreationTime.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• next_token – If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

• max_results – The maximum number of endpoints to return in the response. This value defaults to 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed LineageGroup resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[LineageGroup]

get_policy(session=None, region=None)

The resource policy for the lineage group.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

GetLineageGroupPolicyResponse

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

GetLineageGroupPolicyResponse | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a LineageGroup resource

Returns:

The LineageGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

LineageGroup | None

class sagemaker_core.main.resources.MlflowTrackingServer(*, tracking_server_name, tracking_server_arn=<sagemaker_core.main.utils.Unassigned object>, artifact_store_uri=<sagemaker_core.main.utils.Unassigned object>, tracking_server_size=<sagemaker_core.main.utils.Unassigned object>, mlflow_version=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, tracking_server_status=<sagemaker_core.main.utils.Unassigned object>, is_active=<sagemaker_core.main.utils.Unassigned object>, tracking_server_url=<sagemaker_core.main.utils.Unassigned object>, weekly_maintenance_window_start=<sagemaker_core.main.utils.Unassigned object>, automatic_model_registration=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource MlflowTrackingServer

Parameters:

• tracking_server_name (str)

• tracking_server_arn (str | None)

• artifact_store_uri (str | None)

• tracking_server_size (str | None)

• mlflow_version (str | None)

• role_arn (str | None)

• tracking_server_status (str | None)

• is_active (str | None)

• tracking_server_url (str | None)

• weekly_maintenance_window_start (str | None)

• automatic_model_registration (bool | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

tracking_server_arn

The ARN of the described tracking server.

Type:

str | None

tracking_server_name

The name of the described tracking server.

Type:

str

artifact_store_uri

The S3 URI of the general purpose bucket used as the MLflow Tracking Server artifact store.

Type:

str | None

tracking_server_size

The size of the described tracking server.

Type:

str | None

mlflow_version

The MLflow version used for the described tracking server.

Type:

str | None

role_arn

The Amazon Resource Name (ARN) for an IAM role in your account that the described MLflow Tracking Server uses to access the artifact store in Amazon S3.

Type:

str | None

tracking_server_status

The current creation status of the described MLflow Tracking Server.

Type:

str | None

is_active

Whether the described MLflow Tracking Server is currently active.

Type:

str | None

tracking_server_url

The URL to connect to the MLflow user interface for the described tracking server.

Type:

str | None

weekly_maintenance_window_start

The day and time of the week when weekly maintenance occurs on the described tracking server.

Type:

str | None

automatic_model_registration

Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.

Type:

bool | None

creation_time

The timestamp of when the described MLflow Tracking Server was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

The timestamp of when the described MLflow Tracking Server was last modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod create(tracking_server_name, artifact_store_uri, role_arn, tracking_server_size=<sagemaker_core.main.utils.Unassigned object>, mlflow_version=<sagemaker_core.main.utils.Unassigned object>, automatic_model_registration=<sagemaker_core.main.utils.Unassigned object>, weekly_maintenance_window_start=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a MlflowTrackingServer resource

Parameters:

• tracking_server_name (str) – A unique string identifying the tracking server name. This string is part of the tracking server ARN.

• artifact_store_uri (str) – The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

• role_arn (str) – The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.

• tracking_server_size (str | None) – The size of the tracking server you want to create. You can choose between “Small”, “Medium”, and “Large”. The default MLflow Tracking Server configuration size is “Small”. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use. We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.

• mlflow_version (str | None) – The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.

• automatic_model_registration (bool | None) – Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False.

• weekly_maintenance_window_start (str | None) – The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.

• tags (List[Tag] | None) – Tags consisting of key-value pairs used to manage metadata for the tracking server.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The MlflowTrackingServer resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

MlflowTrackingServer | None

delete()

Delete a MlflowTrackingServer resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(tracking_server_name, session=None, region=None)

Get a MlflowTrackingServer resource

Parameters:

• tracking_server_name (str) – The name of the MLflow Tracking Server to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The MlflowTrackingServer resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

MlflowTrackingServer | None

classmethod get_all(created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, tracking_server_status=<sagemaker_core.main.utils.Unassigned object>, mlflow_version=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all MlflowTrackingServer resources

Parameters:

• created_after (datetime | None) – Use the CreatedAfter filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as “2024-03-16T01:46:56+00:00”. The CreatedAfter parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

• created_before (datetime | None) – Use the CreatedBefore filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as “2024-03-16T01:46:56+00:00”. The CreatedBefore parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

• tracking_server_status (str | None) – Filter for tracking servers with a specified creation status.

• mlflow_version (str | None) – Filter for tracking servers using the specified MLflow version.

• sort_by (str | None) – Filter for trackings servers sorting by name, creation time, or creation status.

• sort_order (str | None) – Change the order of the listed tracking servers. By default, tracking servers are listed in Descending order by creation time. To change the list order, you can specify SortOrder to be Ascending.

• next_token – If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• max_results – The maximum number of tracking servers to list.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed MlflowTrackingServer resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[MlflowTrackingServer]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a MlflowTrackingServer resource

Returns:

The MlflowTrackingServer resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

MlflowTrackingServer | None

stop()

Stop a MlflowTrackingServer resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

update(artifact_store_uri=<sagemaker_core.main.utils.Unassigned object>, tracking_server_size=<sagemaker_core.main.utils.Unassigned object>, automatic_model_registration=<sagemaker_core.main.utils.Unassigned object>, weekly_maintenance_window_start=<sagemaker_core.main.utils.Unassigned object>)

Update a MlflowTrackingServer resource

Returns:

The MlflowTrackingServer resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Parameters:

• artifact_store_uri (str | None)

• tracking_server_size (str | None)

• automatic_model_registration (bool | None)

• weekly_maintenance_window_start (str | None)

Return type:

MlflowTrackingServer | None

wait_for_delete(poll=5, timeout=None)

Wait for a MlflowTrackingServer resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a MlflowTrackingServer resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'Created', 'CreateFailed', 'Updating', 'Updated', 'UpdateFailed', 'Deleting', 'DeleteFailed', 'Stopping', 'Stopped', 'StopFailed', 'Starting', 'Started', 'StartFailed', 'MaintenanceInProgress', 'MaintenanceComplete', 'MaintenanceFailed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Model(*, model_name, primary_container=<sagemaker_core.main.utils.Unassigned object>, containers=<sagemaker_core.main.utils.Unassigned object>, inference_execution_config=<sagemaker_core.main.utils.Unassigned object>, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, model_arn=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, deployment_recommendation=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Model

Parameters:

• model_name (str)

• primary_container (ContainerDefinition | None)

• containers (List[ContainerDefinition] | None)

• inference_execution_config (InferenceExecutionConfig | None)

• execution_role_arn (str | None)

• vpc_config (VpcConfig | None)

• creation_time (datetime | None)

• model_arn (str | None)

• enable_network_isolation (bool | None)

• deployment_recommendation (DeploymentRecommendation | None)

model_name

Name of the SageMaker model.

Type:

str

creation_time

A timestamp that shows when the model was created.

Type:

datetime.datetime | None

model_arn

The Amazon Resource Name (ARN) of the model.

Type:

str | None

primary_container

The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

Type:

sagemaker_core.main.shapes.ContainerDefinition | None

containers

The containers in the inference pipeline.

Type:

List[sagemaker_core.main.shapes.ContainerDefinition] | None

inference_execution_config

Specifies details of how containers in a multi-container endpoint are called.

Type:

sagemaker_core.main.shapes.InferenceExecutionConfig | None

execution_role_arn

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

Type:

str | None

vpc_config

A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud

Type:

sagemaker_core.main.shapes.VpcConfig | None

enable_network_isolation

If True, no inbound or outbound network calls can be made to or from the model container.

Type:

bool | None

deployment_recommendation

A set of recommended deployment configurations for the model.

Type:

sagemaker_core.main.shapes.DeploymentRecommendation | None

classmethod create(model_name, primary_container=<sagemaker_core.main.utils.Unassigned object>, containers=<sagemaker_core.main.utils.Unassigned object>, inference_execution_config=<sagemaker_core.main.utils.Unassigned object>, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Model resource

Parameters:

• model_name (str) – The name of the new model.

• primary_container (ContainerDefinition | None) – The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

• containers (List[ContainerDefinition] | None) – Specifies the containers in the inference pipeline.

• inference_execution_config (InferenceExecutionConfig | None) – Specifies details of how containers in a multi-container endpoint are called.

• execution_role_arn (str | None) – The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles. To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• vpc_config (VpcConfig | None) – A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

• enable_network_isolation (bool | None) – Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Model resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Model | None

delete()

Delete a Model resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(model_name, session=None, region=None)

Get a Model resource

Parameters:

• model_name (str) – The name of the model.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Model resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Model | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Model resources

Parameters:

• sort_by (str | None) – Sorts the list of results. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Descending.

• next_token – If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.

• max_results – The maximum number of models to return in the response.

• name_contains (str | None) – A string in the model name. This filter returns only models whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only models created before the specified time (timestamp).

• creation_time_after (datetime | None) – A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Model resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Model]

get_all_metadata(search_expression=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

Parameters:

• search_expression (ModelMetadataSearchExpression | None) – One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression’s condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.

• next_token – If the response to a previous ListModelMetadataResponse request was truncated, the response includes a NextToken. To retrieve the next set of model metadata, use the token in the next request.

• max_results – The maximum number of models to return in the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelMetadataSummary.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelMetadataSummary]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Model resource

Returns:

The Model resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Model | None

class sagemaker_core.main.resources.ModelBiasJobDefinition(*, job_definition_name, job_definition_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, model_bias_baseline_config=<sagemaker_core.main.utils.Unassigned object>, model_bias_app_specification=<sagemaker_core.main.utils.Unassigned object>, model_bias_job_input=<sagemaker_core.main.utils.Unassigned object>, model_bias_job_output_config=<sagemaker_core.main.utils.Unassigned object>, job_resources=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelBiasJobDefinition

Parameters:

• job_definition_name (str)

• job_definition_arn (str | None)

• creation_time (datetime | None)

• model_bias_baseline_config (ModelBiasBaselineConfig | None)

• model_bias_app_specification (ModelBiasAppSpecification | None)

• model_bias_job_input (ModelBiasJobInput | None)

• model_bias_job_output_config (MonitoringOutputConfig | None)

• job_resources (MonitoringResources | None)

• network_config (MonitoringNetworkConfig | None)

• role_arn (str | None)

• stopping_condition (MonitoringStoppingCondition | None)

job_definition_arn

The Amazon Resource Name (ARN) of the model bias job.

Type:

str | None

job_definition_name

The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Type:

str

creation_time

The time at which the model bias job was created.

Type:

datetime.datetime | None

model_bias_app_specification

Configures the model bias job to run a specified Docker container image.

Type:

sagemaker_core.main.shapes.ModelBiasAppSpecification | None

model_bias_job_input

Inputs for the model bias job.

Type:

sagemaker_core.main.shapes.ModelBiasJobInput | None

model_bias_job_output_config

Type:

sagemaker_core.main.shapes.MonitoringOutputConfig | None

job_resources

Type:

sagemaker_core.main.shapes.MonitoringResources | None

role_arn

The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.

Type:

str | None

model_bias_baseline_config

The baseline configuration for a model bias job.

Type:

sagemaker_core.main.shapes.ModelBiasBaselineConfig | None

network_config

Networking options for a model bias job.

Type:

sagemaker_core.main.shapes.MonitoringNetworkConfig | None

stopping_condition

Type:

sagemaker_core.main.shapes.MonitoringStoppingCondition | None

classmethod create(job_definition_name, model_bias_app_specification, model_bias_job_input, model_bias_job_output_config, job_resources, role_arn, model_bias_baseline_config=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelBiasJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• model_bias_app_specification (ModelBiasAppSpecification) – Configures the model bias job to run a specified Docker container image.

• model_bias_job_input (ModelBiasJobInput) – Inputs for the model bias job.

• model_bias_job_output_config (MonitoringOutputConfig)

• job_resources (MonitoringResources)

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

• model_bias_baseline_config (ModelBiasBaselineConfig | None) – The baseline configuration for a model bias job.

• network_config (MonitoringNetworkConfig | None) – Networking options for a model bias job.

• stopping_condition (MonitoringStoppingCondition | None)

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelBiasJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelBiasJobDefinition | None

delete()

Delete a ModelBiasJobDefinition resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(job_definition_name, session=None, region=None)

Get a ModelBiasJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelBiasJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelBiasJobDefinition | None

classmethod get_all(endpoint_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelBiasJobDefinition resources

Parameters:

• endpoint_name (str | None) – Name of the endpoint to monitor for model bias.

• sort_by (str | None) – Whether to sort results by the Name or CreationTime field. The default is CreationTime.

• sort_order (str | None) – Whether to sort the results in Ascending or Descending order. The default is Descending.

• next_token – The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• max_results – The maximum number of model bias jobs to return in the response. The default value is 10.

• name_contains (str | None) – Filter for model bias jobs whose name contains a specified string.

• creation_time_before (datetime | None) – A filter that returns only model bias jobs created before a specified time.

• creation_time_after (datetime | None) – A filter that returns only model bias jobs created after a specified time.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelBiasJobDefinition resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelBiasJobDefinition]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ModelBiasJobDefinition resource

Returns:

The ModelBiasJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelBiasJobDefinition | None

class sagemaker_core.main.resources.ModelCard(*, model_card_name, model_card_arn=<sagemaker_core.main.utils.Unassigned object>, model_card_version=<sagemaker_core.main.utils.Unassigned object>, content=<sagemaker_core.main.utils.Unassigned object>, model_card_status=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, model_card_processing_status=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelCard

Parameters:

• model_card_name (str)

• model_card_arn (str | None)

• model_card_version (int | None)

• content (str | None)

• model_card_status (str | None)

• security_config (ModelCardSecurityConfig | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• model_card_processing_status (str | None)

model_card_arn

The Amazon Resource Name (ARN) of the model card.

Type:

str | None

model_card_name

The name of the model card.

Type:

str

model_card_version

The version of the model card.

Type:

int | None

content

The content of the model card.

Type:

str | None

model_card_status

The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. Draft: The model card is a work in progress. PendingReview: The model card is pending review. Approved: The model card is approved. Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

Type:

str | None

creation_time

The date and time the model card was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

security_config

The security configuration used to protect model card content.

Type:

sagemaker_core.main.shapes.ModelCardSecurityConfig | None

last_modified_time

The date and time the model card was last modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

model_card_processing_status

The processing status of model card deletion. The ModelCardProcessingStatus updates throughout the different deletion steps. DeletePending: Model card deletion request received. DeleteInProgress: Model card deletion is in progress. ContentDeleted: Deleted model card content. ExportJobsDeleted: Deleted all export jobs associated with the model card. DeleteCompleted: Successfully deleted the model card. DeleteFailed: The model card failed to delete.

Type:

str | None

classmethod create(model_card_name, content, model_card_status, security_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelCard resource

Parameters:

• model_card_name (str) – The unique name of the model card.

• content (str) – The content of the model card. Content must be in model card JSON schema and provided as a string.

• model_card_status (str) – The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. Draft: The model card is a work in progress. PendingReview: The model card is pending review. Approved: The model card is approved. Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

• security_config (ModelCardSecurityConfig | None) – An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.

• tags (List[Tag] | None) – Key-value pairs used to manage metadata for model cards.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelCard resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelCard | None

delete()

Delete a ModelCard resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(model_card_name, model_card_version=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get a ModelCard resource

Parameters:

• model_card_name (str) – The name or Amazon Resource Name (ARN) of the model card to describe.

• model_card_version (int | None) – The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelCard resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelCard | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, model_card_status=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelCard resources

Parameters:

• creation_time_after (datetime | None) – Only list model cards that were created after the time specified.

• creation_time_before (datetime | None) – Only list model cards that were created before the time specified.

• max_results – The maximum number of model cards to list.

• name_contains (str | None) – Only list model cards with names that contain the specified string.

• model_card_status (str | None) – Only list model cards with the specified approval status.

• next_token – If the response to a previous ListModelCards request was truncated, the response includes a NextToken. To retrieve the next set of model cards, use the token in the next request.

• sort_by (str | None) – Sort model cards by either name or creation time. Sorts by creation time by default.

• sort_order (str | None) – Sort model cards by ascending or descending order.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelCard resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelCard]

get_all_versions(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

List existing versions of an Amazon SageMaker Model Card.

Parameters:

• creation_time_after (datetime | None) – Only list model card versions that were created after the time specified.

• creation_time_before (datetime | None) – Only list model card versions that were created before the time specified.

• max_results – The maximum number of model card versions to list.

• next_token – If the response to a previous ListModelCardVersions request was truncated, the response includes a NextToken. To retrieve the next set of model card versions, use the token in the next request.

• sort_by (str | None) – Sort listed model card versions by version. Sorts by version by default.

• sort_order (str | None) – Sort model card versions by ascending or descending order.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelCardVersionSummary.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[ModelCardVersionSummary]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ModelCard resource

Returns:

The ModelCard resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelCard | None

update(content=<sagemaker_core.main.utils.Unassigned object>, model_card_status=<sagemaker_core.main.utils.Unassigned object>)

Update a ModelCard resource

Returns:

The ModelCard resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Parameters:

• content (str | None)

• model_card_status (str | None)

Return type:

ModelCard | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a ModelCard resource to reach certain status.

Parameters:

• target_status (Literal['Draft', 'PendingReview', 'Approved', 'Archived']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ModelCardExportJob(*, model_card_export_job_arn, model_card_export_job_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, model_card_name=<sagemaker_core.main.utils.Unassigned object>, model_card_version=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, created_at=<sagemaker_core.main.utils.Unassigned object>, last_modified_at=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, export_artifacts=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelCardExportJob

Parameters:

• model_card_export_job_arn (str)

• model_card_export_job_name (str | None)

• status (str | None)

• model_card_name (str | None)

• model_card_version (int | None)

• output_config (ModelCardExportOutputConfig | None)

• created_at (datetime | None)

• last_modified_at (datetime | None)

• failure_reason (str | None)

• export_artifacts (ModelCardExportArtifacts | None)

model_card_export_job_name

The name of the model card export job to describe.

Type:

str | None

model_card_export_job_arn

The Amazon Resource Name (ARN) of the model card export job.

Type:

str

status

The completion status of the model card export job. InProgress: The model card export job is in progress. Completed: The model card export job is complete. Failed: The model card export job failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeModelCardExportJob call.

Type:

str | None

model_card_name

The name or Amazon Resource Name (ARN) of the model card that the model export job exports.

Type:

str | None

model_card_version

The version of the model card that the model export job exports.

Type:

int | None

output_config

The export output details for the model card.

Type:

sagemaker_core.main.shapes.ModelCardExportOutputConfig | None

created_at

The date and time that the model export job was created.

Type:

datetime.datetime | None

last_modified_at

The date and time that the model export job was last modified.

Type:

datetime.datetime | None

failure_reason

The failure reason if the model export job fails.

Type:

str | None

export_artifacts

The exported model card artifacts.

Type:

sagemaker_core.main.shapes.ModelCardExportArtifacts | None

classmethod create(model_card_name, model_card_export_job_name, output_config, model_card_version=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelCardExportJob resource

Parameters:

• model_card_name (str | object) – The name or Amazon Resource Name (ARN) of the model card to export.

• model_card_export_job_name (str) – The name of the model card export job.

• output_config (ModelCardExportOutputConfig) – The model card output configuration that specifies the Amazon S3 path for exporting.

• model_card_version (int | None) – The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelCardExportJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelCardExportJob | None

classmethod get(model_card_export_job_arn, session=None, region=None)

Get a ModelCardExportJob resource

Parameters:

• model_card_export_job_arn (str) – The Amazon Resource Name (ARN) of the model card export job to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelCardExportJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelCardExportJob | None

classmethod get_all(model_card_name, model_card_version=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, model_card_export_job_name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelCardExportJob resources

Parameters:

• model_card_name (str) – List export jobs for the model card with the specified name.

• model_card_version (int | None) – List export jobs for the model card with the specified version.

• creation_time_after (datetime | None) – Only list model card export jobs that were created after the time specified.

• creation_time_before (datetime | None) – Only list model card export jobs that were created before the time specified.

• model_card_export_job_name_contains (str | None) – Only list model card export jobs with names that contain the specified string.

• status_equals (str | None) – Only list model card export jobs with the specified status.

• sort_by (str | None) – Sort model card export jobs by either name or creation time. Sorts by creation time by default.

• sort_order (str | None) – Sort model card export jobs by ascending or descending order.

• next_token – If the response to a previous ListModelCardExportJobs request was truncated, the response includes a NextToken. To retrieve the next set of model card export jobs, use the token in the next request.

• max_results – The maximum number of model card export jobs to list.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelCardExportJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelCardExportJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ModelCardExportJob resource

Returns:

The ModelCardExportJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelCardExportJob | None

wait(poll=5, timeout=None)

Wait for a ModelCardExportJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ModelExplainabilityJobDefinition(*, job_definition_name, job_definition_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, model_explainability_baseline_config=<sagemaker_core.main.utils.Unassigned object>, model_explainability_app_specification=<sagemaker_core.main.utils.Unassigned object>, model_explainability_job_input=<sagemaker_core.main.utils.Unassigned object>, model_explainability_job_output_config=<sagemaker_core.main.utils.Unassigned object>, job_resources=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelExplainabilityJobDefinition

Parameters:

• job_definition_name (str)

• job_definition_arn (str | None)

• creation_time (datetime | None)

• model_explainability_baseline_config (ModelExplainabilityBaselineConfig | None)

• model_explainability_app_specification (ModelExplainabilityAppSpecification | None)

• model_explainability_job_input (ModelExplainabilityJobInput | None)

• model_explainability_job_output_config (MonitoringOutputConfig | None)

• job_resources (MonitoringResources | None)

• network_config (MonitoringNetworkConfig | None)

• role_arn (str | None)

• stopping_condition (MonitoringStoppingCondition | None)

job_definition_arn

The Amazon Resource Name (ARN) of the model explainability job.

Type:

str | None

job_definition_name

The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Type:

str

creation_time

The time at which the model explainability job was created.

Type:

datetime.datetime | None

model_explainability_app_specification

Configures the model explainability job to run a specified Docker container image.

Type:

sagemaker_core.main.shapes.ModelExplainabilityAppSpecification | None

model_explainability_job_input

Inputs for the model explainability job.

Type:

sagemaker_core.main.shapes.ModelExplainabilityJobInput | None

model_explainability_job_output_config

Type:

sagemaker_core.main.shapes.MonitoringOutputConfig | None

job_resources

Type:

sagemaker_core.main.shapes.MonitoringResources | None

role_arn

The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.

Type:

str | None

model_explainability_baseline_config

The baseline configuration for a model explainability job.

Type:

sagemaker_core.main.shapes.ModelExplainabilityBaselineConfig | None

network_config

Networking options for a model explainability job.

Type:

sagemaker_core.main.shapes.MonitoringNetworkConfig | None

stopping_condition

Type:

sagemaker_core.main.shapes.MonitoringStoppingCondition | None

classmethod create(job_definition_name, model_explainability_app_specification, model_explainability_job_input, model_explainability_job_output_config, job_resources, role_arn, model_explainability_baseline_config=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelExplainabilityJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• model_explainability_app_specification (ModelExplainabilityAppSpecification) – Configures the model explainability job to run a specified Docker container image.

• model_explainability_job_input (ModelExplainabilityJobInput) – Inputs for the model explainability job.

• model_explainability_job_output_config (MonitoringOutputConfig)

• job_resources (MonitoringResources)

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

• model_explainability_baseline_config (ModelExplainabilityBaselineConfig | None) – The baseline configuration for a model explainability job.

• network_config (MonitoringNetworkConfig | None) – Networking options for a model explainability job.

• stopping_condition (MonitoringStoppingCondition | None)

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelExplainabilityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelExplainabilityJobDefinition | None

delete()

Delete a ModelExplainabilityJobDefinition resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(job_definition_name, session=None, region=None)

Get a ModelExplainabilityJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelExplainabilityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelExplainabilityJobDefinition | None

classmethod get_all(endpoint_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelExplainabilityJobDefinition resources

Parameters:

• endpoint_name (str | None) – Name of the endpoint to monitor for model explainability.

• sort_by (str | None) – Whether to sort results by the Name or CreationTime field. The default is CreationTime.

• sort_order (str | None) – Whether to sort the results in Ascending or Descending order. The default is Descending.

• next_token – The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• max_results – The maximum number of jobs to return in the response. The default value is 10.

• name_contains (str | None) – Filter for model explainability jobs whose name contains a specified string.

• creation_time_before (datetime | None) – A filter that returns only model explainability jobs created before a specified time.

• creation_time_after (datetime | None) – A filter that returns only model explainability jobs created after a specified time.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelExplainabilityJobDefinition resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelExplainabilityJobDefinition]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ModelExplainabilityJobDefinition resource

Returns:

The ModelExplainabilityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelExplainabilityJobDefinition | None

class sagemaker_core.main.resources.ModelPackage(*, model_package_name, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, model_package_version=<sagemaker_core.main.utils.Unassigned object>, model_package_arn=<sagemaker_core.main.utils.Unassigned object>, model_package_description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, inference_specification=<sagemaker_core.main.utils.Unassigned object>, source_algorithm_specification=<sagemaker_core.main.utils.Unassigned object>, validation_specification=<sagemaker_core.main.utils.Unassigned object>, model_package_status=<sagemaker_core.main.utils.Unassigned object>, model_package_status_details=<sagemaker_core.main.utils.Unassigned object>, certify_for_marketplace=<sagemaker_core.main.utils.Unassigned object>, model_approval_status=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, model_metrics=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, approval_description=<sagemaker_core.main.utils.Unassigned object>, domain=<sagemaker_core.main.utils.Unassigned object>, task=<sagemaker_core.main.utils.Unassigned object>, sample_payload_url=<sagemaker_core.main.utils.Unassigned object>, customer_metadata_properties=<sagemaker_core.main.utils.Unassigned object>, drift_check_baselines=<sagemaker_core.main.utils.Unassigned object>, additional_inference_specifications=<sagemaker_core.main.utils.Unassigned object>, skip_model_validation=<sagemaker_core.main.utils.Unassigned object>, source_uri=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, model_card=<sagemaker_core.main.utils.Unassigned object>, model_life_cycle=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelPackage

Parameters:

• model_package_name (str)

• model_package_group_name (str | None)

• model_package_version (int | None)

• model_package_arn (str | None)

• model_package_description (str | None)

• creation_time (datetime | None)

• inference_specification (InferenceSpecification | None)

• source_algorithm_specification (SourceAlgorithmSpecification | None)

• validation_specification (ModelPackageValidationSpecification | None)

• model_package_status (str | None)

• model_package_status_details (ModelPackageStatusDetails | None)

• certify_for_marketplace (bool | None)

• model_approval_status (str | None)

• created_by (UserContext | None)

• metadata_properties (MetadataProperties | None)

• model_metrics (ModelMetrics | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• approval_description (str | None)

• domain (str | None)

• task (str | None)

• sample_payload_url (str | None)

• customer_metadata_properties (Dict[str, str] | None)

• drift_check_baselines (DriftCheckBaselines | None)

• additional_inference_specifications (List[AdditionalInferenceSpecificationDefinition] | None)

• skip_model_validation (str | None)

• source_uri (str | None)

• security_config (ModelPackageSecurityConfig | None)

• model_card (ModelPackageModelCard | None)

• model_life_cycle (ModelLifeCycle | None)

model_package_name

The name of the model package being described.

Type:

str

model_package_arn

The Amazon Resource Name (ARN) of the model package.

Type:

str | None

creation_time

A timestamp specifying when the model package was created.

Type:

datetime.datetime | None

model_package_status

The current status of the model package.

Type:

str | None

model_package_status_details

Details about the current status of the model package.

Type:

sagemaker_core.main.shapes.ModelPackageStatusDetails | None

model_package_group_name

If the model is a versioned model, the name of the model group that the versioned model belongs to.

Type:

str | None

model_package_version

The version of the model package.

Type:

int | None

model_package_description

A brief summary of the model package.

Type:

str | None

inference_specification

Details about inference jobs that you can run with models based on this model package.

Type:

sagemaker_core.main.shapes.InferenceSpecification | None

source_algorithm_specification

Details about the algorithm that was used to create the model package.

Type:

sagemaker_core.main.shapes.SourceAlgorithmSpecification | None

validation_specification

Configurations for one or more transform jobs that SageMaker runs to test the model package.

Type:

sagemaker_core.main.shapes.ModelPackageValidationSpecification | None

certify_for_marketplace

Whether the model package is certified for listing on Amazon Web Services Marketplace.

Type:

bool | None

model_approval_status

The approval status of the model package.

Type:

str | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

metadata_properties

Type:

sagemaker_core.main.shapes.MetadataProperties | None

model_metrics

Metrics for the model.

Type:

sagemaker_core.main.shapes.ModelMetrics | None

last_modified_time

The last time that the model package was modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

approval_description

A description provided for the model approval.

Type:

str | None

domain

The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.

Type:

str | None

task

The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.

Type:

str | None

sample_payload_url

The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).

Type:

str | None

customer_metadata_properties

The metadata properties associated with the model package versions.

Type:

Dict[str, str] | None

drift_check_baselines

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

Type:

sagemaker_core.main.shapes.DriftCheckBaselines | None

additional_inference_specifications

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

Type:

List[sagemaker_core.main.shapes.AdditionalInferenceSpecificationDefinition] | None

skip_model_validation

Indicates if you want to skip model validation.

Type:

str | None

source_uri

The URI of the source for the model package.

Type:

str | None

security_config

The KMS Key ID (KMSKeyId) used for encryption of model package information.

Type:

sagemaker_core.main.shapes.ModelPackageSecurityConfig | None

model_card

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

Type:

sagemaker_core.main.shapes.ModelPackageModelCard | None

model_life_cycle

A structure describing the current state of the model in its life cycle.

Type:

sagemaker_core.main.shapes.ModelLifeCycle | None

batch_get(model_package_arn_list, session=None, region=None)

This action batch describes a list of versioned model packages.

Parameters:

• model_package_arn_list (List[str]) – The list of Amazon Resource Name (ARN) of the model package groups.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

BatchDescribeModelPackageOutput

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

BatchDescribeModelPackageOutput | None

classmethod create(model_package_name=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, model_package_description=<sagemaker_core.main.utils.Unassigned object>, inference_specification=<sagemaker_core.main.utils.Unassigned object>, validation_specification=<sagemaker_core.main.utils.Unassigned object>, source_algorithm_specification=<sagemaker_core.main.utils.Unassigned object>, certify_for_marketplace=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, model_approval_status=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, model_metrics=<sagemaker_core.main.utils.Unassigned object>, client_token=<sagemaker_core.main.utils.Unassigned object>, domain=<sagemaker_core.main.utils.Unassigned object>, task=<sagemaker_core.main.utils.Unassigned object>, sample_payload_url=<sagemaker_core.main.utils.Unassigned object>, customer_metadata_properties=<sagemaker_core.main.utils.Unassigned object>, drift_check_baselines=<sagemaker_core.main.utils.Unassigned object>, additional_inference_specifications=<sagemaker_core.main.utils.Unassigned object>, skip_model_validation=<sagemaker_core.main.utils.Unassigned object>, source_uri=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, model_card=<sagemaker_core.main.utils.Unassigned object>, model_life_cycle=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelPackage resource

Parameters:

• model_package_name (str | None) – The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). This parameter is required for unversioned models. It is not applicable to versioned models.

• model_package_group_name (str | object | None) – The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to. This parameter is required for versioned models, and does not apply to unversioned models.

• model_package_description (str | None) – A description of the model package.

• inference_specification (InferenceSpecification | None) – Specifies details about inference jobs that you can run with models based on this model package, including the following information: The Amazon ECR paths of containers that contain the inference code and model artifacts. The instance types that the model package supports for transform jobs and real-time endpoints used for inference. The input and output content formats that the model package supports for inference.

• validation_specification (ModelPackageValidationSpecification | None) – Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

• source_algorithm_specification (SourceAlgorithmSpecification | None) – Details about the algorithm that was used to create the model package.

• certify_for_marketplace (bool | None) – Whether to certify the model package for listing on Amazon Web Services Marketplace. This parameter is optional for unversioned models, and does not apply to versioned models.

• tags (List[Tag] | None) – A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide. If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

• model_approval_status (str | None) – Whether the model is approved for deployment. This parameter is optional for versioned models, and does not apply to unversioned models. For versioned models, the value of this parameter must be set to Approved to deploy the model.

• metadata_properties (MetadataProperties | None)

• model_metrics (ModelMetrics | None) – A structure that contains model metrics reports.

• client_token (str | None) – A unique token that guarantees that the call to this API is idempotent.

• domain (str | None) – The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

• task (str | None) – The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: “IMAGE_CLASSIFICATION” | “OBJECT_DETECTION” | “TEXT_GENERATION” |”IMAGE_SEGMENTATION” | “FILL_MASK” | “CLASSIFICATION” | “REGRESSION” | “OTHER”. Specify “OTHER” if none of the tasks listed fit your use case.

• sample_payload_url (str | None) – The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

• customer_metadata_properties (Dict[str, str] | None) – The metadata properties associated with the model package versions.

• drift_check_baselines (DriftCheckBaselines | None) – Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

• additional_inference_specifications (List[AdditionalInferenceSpecificationDefinition] | None) – An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

• skip_model_validation (str | None) – Indicates if you want to skip model validation.

• source_uri (str | None) – The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

• security_config (ModelPackageSecurityConfig | None) – The KMS Key ID (KMSKeyId) used for encryption of model package information.

• model_card (ModelPackageModelCard | None) – The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

• model_life_cycle (ModelLifeCycle | None) – A structure describing the current state of the model in its life cycle.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelPackage resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelPackage | None

delete()

Delete a ModelPackage resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

None

classmethod get(model_package_name, session=None, region=None)

Get a ModelPackage resource

Parameters:

• model_package_name (str) – The name or Amazon Resource Name (ARN) of the model package to describe. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelPackage resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ModelPackage | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, model_approval_status=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, model_package_type=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelPackage resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only model packages created after the specified time (timestamp).

• creation_time_before (datetime | None) – A filter that returns only model packages created before the specified time (timestamp).

• max_results – The maximum number of model packages to return in the response.

• name_contains (str | None) – A string in the model package name. This filter returns only model packages whose name contains the specified string.

• model_approval_status (str | None) – A filter that returns only the model packages with the specified approval status.

• model_package_group_name (str | None) – A filter that returns only model versions that belong to the specified model group.

• model_package_type (str | None) – A filter that returns only the model packages of the specified type. This can be one of the following values. UNVERSIONED - List only unversioined models. This is the default value if no ModelPackageType is specified. VERSIONED - List only versioned models. BOTH - List both versioned and unversioned models.

• next_token – If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.

• sort_by (str | None) – The parameter by which to sort the results. The default is CreationTime.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelPackage resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelPackage]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ModelPackage resource

Returns:

The ModelPackage resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ModelPackage | None

update(model_approval_status=<sagemaker_core.main.utils.Unassigned object>, approval_description=<sagemaker_core.main.utils.Unassigned object>, customer_metadata_properties=<sagemaker_core.main.utils.Unassigned object>, customer_metadata_properties_to_remove=<sagemaker_core.main.utils.Unassigned object>, additional_inference_specifications_to_add=<sagemaker_core.main.utils.Unassigned object>, inference_specification=<sagemaker_core.main.utils.Unassigned object>, source_uri=<sagemaker_core.main.utils.Unassigned object>, model_card=<sagemaker_core.main.utils.Unassigned object>, model_life_cycle=<sagemaker_core.main.utils.Unassigned object>, client_token=<sagemaker_core.main.utils.Unassigned object>)

Update a ModelPackage resource

Parameters:

• customer_metadata_properties_to_remove (List[str] | None) – The metadata properties associated with the model package versions to remove.

• additional_inference_specifications_to_add (List[AdditionalInferenceSpecificationDefinition] | None) – An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

• client_token (str | None) – A unique token that guarantees that the call to this API is idempotent.

• model_approval_status (str | None)

• approval_description (str | None)

• customer_metadata_properties (Dict[str, str] | None)

• inference_specification (InferenceSpecification | None)

• source_uri (str | None)

• model_card (ModelPackageModelCard | None)

• model_life_cycle (ModelLifeCycle | None)

Returns:

The ModelPackage resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

ModelPackage | None

wait_for_delete(poll=5, timeout=None)

Wait for a ModelPackage resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a ModelPackage resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'InProgress', 'Completed', 'Failed', 'Deleting']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ModelPackageGroup(*, model_package_group_name, model_package_group_arn=<sagemaker_core.main.utils.Unassigned object>, model_package_group_description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, model_package_group_status=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelPackageGroup

Parameters:

• model_package_group_name (str)

• model_package_group_arn (str | None)

• model_package_group_description (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• model_package_group_status (str | None)

model_package_group_name

The name of the model group.

Type:

str

model_package_group_arn

The Amazon Resource Name (ARN) of the model group.

Type:

str | None

creation_time

The time that the model group was created.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

model_package_group_status

The status of the model group.

Type:

str | None

model_package_group_description

A description of the model group.

Type:

str | None

classmethod create(model_package_group_name, model_package_group_description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelPackageGroup resource

Parameters:

• model_package_group_name (str) – The name of the model group.

• model_package_group_description (str | None) – A description for the model group.

• tags (List[Tag] | None) – A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelPackageGroup resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelPackageGroup | None

delete()

Delete a ModelPackageGroup resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

None

delete_policy(session=None, region=None)

Deletes a model group resource policy.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(model_package_group_name, session=None, region=None)

Get a ModelPackageGroup resource

Parameters:

• model_package_group_name (str) – The name of the model group to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelPackageGroup resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ModelPackageGroup | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, cross_account_filter_option=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelPackageGroup resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only model groups created after the specified time.

• creation_time_before (datetime | None) – A filter that returns only model groups created before the specified time.

• max_results – The maximum number of results to return in the response.

• name_contains (str | None) – A string in the model group name. This filter returns only model groups whose name contains the specified string.

• next_token – If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken. To retrieve the next set of model groups, use the token in the next request.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• cross_account_filter_option (str | None) – A filter that returns either model groups shared with you or model groups in your own account. When the value is CrossAccount, the results show the resources made discoverable to you from other accounts. When the value is SameAccount or null, the results show resources from your account. The default is SameAccount.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelPackageGroup resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelPackageGroup]

get_policy(session=None, region=None)

Gets a resource policy that manages access for a model group.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

str

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

put_policy(resource_policy, session=None, region=None)

Adds a resouce policy to control access to a model group.

Parameters:

• resource_policy (str) – The resource policy for the model group.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

None

refresh()

Refresh a ModelPackageGroup resource

Returns:

The ModelPackageGroup resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ModelPackageGroup | None

wait_for_delete(poll=5, timeout=None)

Wait for a ModelPackageGroup resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a ModelPackageGroup resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'InProgress', 'Completed', 'Failed', 'Deleting', 'DeleteFailed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ModelQualityJobDefinition(*, job_definition_name, job_definition_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, model_quality_baseline_config=<sagemaker_core.main.utils.Unassigned object>, model_quality_app_specification=<sagemaker_core.main.utils.Unassigned object>, model_quality_job_input=<sagemaker_core.main.utils.Unassigned object>, model_quality_job_output_config=<sagemaker_core.main.utils.Unassigned object>, job_resources=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ModelQualityJobDefinition

Parameters:

• job_definition_name (str)

• job_definition_arn (str | None)

• creation_time (datetime | None)

• model_quality_baseline_config (ModelQualityBaselineConfig | None)

• model_quality_app_specification (ModelQualityAppSpecification | None)

• model_quality_job_input (ModelQualityJobInput | None)

• model_quality_job_output_config (MonitoringOutputConfig | None)

• job_resources (MonitoringResources | None)

• network_config (MonitoringNetworkConfig | None)

• role_arn (str | None)

• stopping_condition (MonitoringStoppingCondition | None)

job_definition_arn

The Amazon Resource Name (ARN) of the model quality job.

Type:

str | None

job_definition_name

The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Type:

str

creation_time

The time at which the model quality job was created.

Type:

datetime.datetime | None

model_quality_app_specification

Configures the model quality job to run a specified Docker container image.

Type:

sagemaker_core.main.shapes.ModelQualityAppSpecification | None

model_quality_job_input

Inputs for the model quality job.

Type:

sagemaker_core.main.shapes.ModelQualityJobInput | None

model_quality_job_output_config

Type:

sagemaker_core.main.shapes.MonitoringOutputConfig | None

job_resources

Type:

sagemaker_core.main.shapes.MonitoringResources | None

role_arn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

Type:

str | None

model_quality_baseline_config

The baseline configuration for a model quality job.

Type:

sagemaker_core.main.shapes.ModelQualityBaselineConfig | None

network_config

Networking options for a model quality job.

Type:

sagemaker_core.main.shapes.MonitoringNetworkConfig | None

stopping_condition

Type:

sagemaker_core.main.shapes.MonitoringStoppingCondition | None

classmethod create(job_definition_name, model_quality_app_specification, model_quality_job_input, model_quality_job_output_config, job_resources, role_arn, model_quality_baseline_config=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ModelQualityJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the monitoring job definition.

• model_quality_app_specification (ModelQualityAppSpecification) – The container that runs the monitoring job.

• model_quality_job_input (ModelQualityJobInput) – A list of the inputs that are monitored. Currently endpoints are supported.

• model_quality_job_output_config (MonitoringOutputConfig)

• job_resources (MonitoringResources)

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

• model_quality_baseline_config (ModelQualityBaselineConfig | None) – Specifies the constraints and baselines for the monitoring job.

• network_config (MonitoringNetworkConfig | None) – Specifies the network configuration for the monitoring job.

• stopping_condition (MonitoringStoppingCondition | None)

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelQualityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ModelQualityJobDefinition | None

delete()

Delete a ModelQualityJobDefinition resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(job_definition_name, session=None, region=None)

Get a ModelQualityJobDefinition resource

Parameters:

• job_definition_name (str) – The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ModelQualityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelQualityJobDefinition | None

classmethod get_all(endpoint_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ModelQualityJobDefinition resources

Parameters:

• endpoint_name (str | None) – A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – Whether to sort the results in Ascending or Descending order. The default is Descending.

• next_token – If the result of the previous ListModelQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.

• max_results – The maximum number of results to return in a call to ListModelQualityJobDefinitions.

• name_contains (str | None) – A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only model quality monitoring job definitions created before the specified time.

• creation_time_after (datetime | None) – A filter that returns only model quality monitoring job definitions created after the specified time.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ModelQualityJobDefinition resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ModelQualityJobDefinition]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ModelQualityJobDefinition resource

Returns:

The ModelQualityJobDefinition resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ModelQualityJobDefinition | None

class sagemaker_core.main.resources.MonitoringAlert(*, monitoring_alert_name, creation_time, last_modified_time, alert_status, datapoints_to_alert, evaluation_period, actions)

Class representing resource MonitoringAlert

Parameters:

• monitoring_alert_name (str)

• creation_time (datetime)

• last_modified_time (datetime)

• alert_status (str)

• datapoints_to_alert (int)

• evaluation_period (int)

• actions (MonitoringAlertActions)

monitoring_alert_name

The name of a monitoring alert.

Type:

str

creation_time

A timestamp that indicates when a monitor alert was created.

Type:

datetime.datetime

last_modified_time

A timestamp that indicates when a monitor alert was last updated.

Type:

datetime.datetime

alert_status

The current status of an alert.

Type:

str

datapoints_to_alert

Within EvaluationPeriod, how many execution failures will raise an alert.

Type:

int

evaluation_period

The number of most recent monitoring executions to consider when evaluating alert status.

Type:

int

actions

A list of alert actions taken in response to an alert going into InAlert status.

Type:

sagemaker_core.main.shapes.MonitoringAlertActions

classmethod get_all(monitoring_schedule_name, session=None, region=None)

Get all MonitoringAlert resources

Parameters:

• monitoring_schedule_name (str) – The name of a monitoring schedule.

• next_token – If the result of the previous ListMonitoringAlerts request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.

• max_results – The maximum number of results to display. The default is 100.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed MonitoringAlert resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[MonitoringAlert]

list_history(monitoring_schedule_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, next_token=<sagemaker_core.main.utils.Unassigned object>, max_results=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Gets a list of past alerts in a model monitoring schedule.

Parameters:

• monitoring_schedule_name (str | None) – The name of a monitoring schedule.

• sort_by (str | None) – The field used to sort results. The default is CreationTime.

• sort_order (str | None) – The sort order, whether Ascending or Descending, of the alert history. The default is Descending.

• next_token (str | None) – If the result of the previous ListMonitoringAlertHistory request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.

• max_results (int | None) – The maximum number of results to display. The default is 100.

• creation_time_before (datetime | None) – A filter that returns only alerts created on or before the specified time.

• creation_time_after (datetime | None) – A filter that returns only alerts created on or after the specified time.

• status_equals (str | None) – A filter that retrieves only alerts with a specific status.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

MonitoringAlertHistorySummary

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

MonitoringAlertHistorySummary | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

update(monitoring_schedule_name, datapoints_to_alert, evaluation_period)

Update a MonitoringAlert resource

Parameters:

• monitoring_schedule_name (str) – The name of a monitoring schedule.

• datapoints_to_alert (int)

• evaluation_period (int)

Returns:

The MonitoringAlert resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

MonitoringAlert | None

class sagemaker_core.main.resources.MonitoringExecution(*, monitoring_schedule_name, scheduled_time, creation_time, last_modified_time, monitoring_execution_status, processing_job_arn=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource MonitoringExecution

Parameters:

• monitoring_schedule_name (str)

• scheduled_time (datetime)

• creation_time (datetime)

• last_modified_time (datetime)

• monitoring_execution_status (str)

• processing_job_arn (str | None)

• endpoint_name (str | None)

• failure_reason (str | None)

• monitoring_job_definition_name (str | None)

• monitoring_type (str | None)

monitoring_schedule_name

The name of the monitoring schedule.

Type:

str

scheduled_time

The time the monitoring job was scheduled.

Type:

datetime.datetime

creation_time

The time at which the monitoring job was created.

Type:

datetime.datetime

last_modified_time

A timestamp that indicates the last time the monitoring job was modified.

Type:

datetime.datetime

monitoring_execution_status

The status of the monitoring job.

Type:

str

processing_job_arn

The Amazon Resource Name (ARN) of the monitoring job.

Type:

str | None

endpoint_name

The name of the endpoint used to run the monitoring job.

Type:

str | None

failure_reason

Contains the reason a monitoring job failed, if it failed.

Type:

str | None

monitoring_job_definition_name

The name of the monitoring job.

Type:

str | None

monitoring_type

The type of the monitoring job.

Type:

str | None

classmethod get_all(monitoring_schedule_name=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, scheduled_time_before=<sagemaker_core.main.utils.Unassigned object>, scheduled_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_type_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all MonitoringExecution resources

Parameters:

• monitoring_schedule_name (str | None) – Name of a specific schedule to fetch jobs for.

• endpoint_name (str | None) – Name of a specific endpoint to fetch jobs for.

• sort_by (str | None) – Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.

• sort_order (str | None) – Whether to sort the results in Ascending or Descending order. The default is Descending.

• next_token – The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• max_results – The maximum number of jobs to return in the response. The default value is 10.

• scheduled_time_before (datetime | None) – Filter for jobs scheduled before a specified time.

• scheduled_time_after (datetime | None) – Filter for jobs scheduled after a specified time.

• creation_time_before (datetime | None) – A filter that returns only jobs created before a specified time.

• creation_time_after (datetime | None) – A filter that returns only jobs created after a specified time.

• last_modified_time_before (datetime | None) – A filter that returns only jobs modified after a specified time.

• last_modified_time_after (datetime | None) – A filter that returns only jobs modified before a specified time.

• status_equals (str | None) – A filter that retrieves only jobs with a specific status.

• monitoring_job_definition_name (str | None) – Gets a list of the monitoring job runs of the specified monitoring job definitions.

• monitoring_type_equals (str | None) – A filter that returns only the monitoring job runs of the specified monitoring type.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed MonitoringExecution resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[MonitoringExecution]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.MonitoringSchedule(*, monitoring_schedule_name, monitoring_schedule_arn=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_status=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_config=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, last_monitoring_execution_summary=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource MonitoringSchedule

Parameters:

• monitoring_schedule_name (str)

• monitoring_schedule_arn (str | None)

• monitoring_schedule_status (str | None)

• monitoring_type (str | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• monitoring_schedule_config (MonitoringScheduleConfig | None)

• endpoint_name (str | None)

• last_monitoring_execution_summary (MonitoringExecutionSummary | None)

monitoring_schedule_arn

The Amazon Resource Name (ARN) of the monitoring schedule.

Type:

str | None

monitoring_schedule_name

Name of the monitoring schedule.

Type:

str

monitoring_schedule_status

The status of an monitoring job.

Type:

str | None

creation_time

The time at which the monitoring job was created.

Type:

datetime.datetime | None

last_modified_time

The time at which the monitoring job was last modified.

Type:

datetime.datetime | None

monitoring_schedule_config

The configuration object that specifies the monitoring schedule and defines the monitoring job.

Type:

sagemaker_core.main.shapes.MonitoringScheduleConfig | None

monitoring_type

The type of the monitoring job that this schedule runs. This is one of the following values. DATA_QUALITY - The schedule is for a data quality monitoring job. MODEL_QUALITY - The schedule is for a model quality monitoring job. MODEL_BIAS - The schedule is for a bias monitoring job. MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.

Type:

str | None

failure_reason

A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.

Type:

str | None

endpoint_name

The name of the endpoint for the monitoring job.

Type:

str | None

last_monitoring_execution_summary

Describes metadata on the last execution to run, if there was one.

Type:

sagemaker_core.main.shapes.MonitoringExecutionSummary | None

classmethod create(monitoring_schedule_name, monitoring_schedule_config, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a MonitoringSchedule resource

Parameters:

• monitoring_schedule_name (str) – The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

• monitoring_schedule_config (MonitoringScheduleConfig) – The configuration object that specifies the monitoring schedule and defines the monitoring job.

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The MonitoringSchedule resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

MonitoringSchedule | None

delete()

Delete a MonitoringSchedule resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(monitoring_schedule_name, session=None, region=None)

Get a MonitoringSchedule resource

Parameters:

• monitoring_schedule_name (str) – Name of a previously created monitoring schedule.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The MonitoringSchedule resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

MonitoringSchedule | None

classmethod get_all(endpoint_name=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_type_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all MonitoringSchedule resources

Parameters:

• endpoint_name (str | None) – Name of a specific endpoint to fetch schedules for.

• sort_by (str | None) – Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.

• sort_order (str | None) – Whether to sort the results in Ascending or Descending order. The default is Descending.

• next_token – The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• max_results – The maximum number of jobs to return in the response. The default value is 10.

• name_contains (str | None) – Filter for monitoring schedules whose name contains a specified string.

• creation_time_before (datetime | None) – A filter that returns only monitoring schedules created before a specified time.

• creation_time_after (datetime | None) – A filter that returns only monitoring schedules created after a specified time.

• last_modified_time_before (datetime | None) – A filter that returns only monitoring schedules modified before a specified time.

• last_modified_time_after (datetime | None) – A filter that returns only monitoring schedules modified after a specified time.

• status_equals (str | None) – A filter that returns only monitoring schedules modified before a specified time.

• monitoring_job_definition_name (str | None) – Gets a list of the monitoring schedules for the specified monitoring job definition.

• monitoring_type_equals (str | None) – A filter that returns only the monitoring schedules for the specified monitoring type.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed MonitoringSchedule resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[MonitoringSchedule]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a MonitoringSchedule resource

Returns:

The MonitoringSchedule resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

MonitoringSchedule | None

stop()

Stop a MonitoringSchedule resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

update(monitoring_schedule_config)

Update a MonitoringSchedule resource

Returns:

The MonitoringSchedule resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Parameters:

monitoring_schedule_config (MonitoringScheduleConfig)

Return type:

MonitoringSchedule | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a MonitoringSchedule resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'Failed', 'Scheduled', 'Stopped']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.NotebookInstance(*, notebook_instance_name, notebook_instance_arn=<sagemaker_core.main.utils.Unassigned object>, notebook_instance_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, url=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, subnet_id=<sagemaker_core.main.utils.Unassigned object>, security_groups=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, network_interface_id=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, notebook_instance_lifecycle_config_name=<sagemaker_core.main.utils.Unassigned object>, direct_internet_access=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, accelerator_types=<sagemaker_core.main.utils.Unassigned object>, default_code_repository=<sagemaker_core.main.utils.Unassigned object>, additional_code_repositories=<sagemaker_core.main.utils.Unassigned object>, root_access=<sagemaker_core.main.utils.Unassigned object>, platform_identifier=<sagemaker_core.main.utils.Unassigned object>, instance_metadata_service_configuration=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource NotebookInstance

Parameters:

• notebook_instance_name (str)

• notebook_instance_arn (str | None)

• notebook_instance_status (str | None)

• failure_reason (str | None)

• url (str | None)

• instance_type (str | None)

• subnet_id (str | None)

• security_groups (List[str] | None)

• role_arn (str | None)

• kms_key_id (str | None)

• network_interface_id (str | None)

• last_modified_time (datetime | None)

• creation_time (datetime | None)

• notebook_instance_lifecycle_config_name (str | None)

• direct_internet_access (str | None)

• volume_size_in_gb (int | None)

• accelerator_types (List[str] | None)

• default_code_repository (str | None)

• additional_code_repositories (List[str] | None)

• root_access (str | None)

• platform_identifier (str | None)

• instance_metadata_service_configuration (InstanceMetadataServiceConfiguration | None)

notebook_instance_arn

The Amazon Resource Name (ARN) of the notebook instance.

Type:

str | None

notebook_instance_name

The name of the SageMaker AI notebook instance.

Type:

str

notebook_instance_status

The status of the notebook instance.

Type:

str | None

failure_reason

If status is Failed, the reason it failed.

Type:

str | None

url

The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.

Type:

str | None

instance_type

The type of ML compute instance running on the notebook instance.

Type:

str | None

subnet_id

The ID of the VPC subnet.

Type:

str | None

security_groups

The IDs of the VPC security groups.

Type:

List[str] | None

role_arn

The Amazon Resource Name (ARN) of the IAM role associated with the instance.

Type:

str | None

kms_key_id

The Amazon Web Services KMS key ID SageMaker AI uses to encrypt data when storing it on the ML storage volume attached to the instance.

Type:

str | None

network_interface_id

The network interface IDs that SageMaker AI created at the time of creating the instance.

Type:

str | None

last_modified_time

A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.

Type:

datetime.datetime | None

creation_time

A timestamp. Use this parameter to return the time when the notebook instance was created

Type:

datetime.datetime | None

notebook_instance_lifecycle_config_name

Returns the name of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance

Type:

str | None

direct_internet_access

Describes whether SageMaker AI provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker AI training and endpoint services. For more information, see Notebook Instances Are Internet-Enabled by Default.

Type:

str | None

volume_size_in_gb

The size, in GB, of the ML storage volume attached to the notebook instance.

Type:

int | None

accelerator_types

This parameter is no longer supported. Elastic Inference (EI) is no longer available. This parameter was used to specify a list of the EI instance types associated with this notebook instance.

Type:

List[str] | None

default_code_repository

The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

Type:

str | None

additional_code_repositories

An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

Type:

List[str] | None

root_access

Whether root access is enabled or disabled for users of the notebook instance. Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

Type:

str | None

platform_identifier

The platform identifier of the notebook instance runtime environment.

Type:

str | None

instance_metadata_service_configuration

Information on the IMDS configuration of the notebook instance

Type:

sagemaker_core.main.shapes.InstanceMetadataServiceConfiguration | None

classmethod create(notebook_instance_name, instance_type, role_arn, subnet_id=<sagemaker_core.main.utils.Unassigned object>, security_group_ids=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, lifecycle_config_name=<sagemaker_core.main.utils.Unassigned object>, direct_internet_access=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, accelerator_types=<sagemaker_core.main.utils.Unassigned object>, default_code_repository=<sagemaker_core.main.utils.Unassigned object>, additional_code_repositories=<sagemaker_core.main.utils.Unassigned object>, root_access=<sagemaker_core.main.utils.Unassigned object>, platform_identifier=<sagemaker_core.main.utils.Unassigned object>, instance_metadata_service_configuration=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a NotebookInstance resource

Parameters:

• notebook_instance_name (str) – The name of the new notebook instance.

• instance_type (str) – The type of ML compute instance to launch for the notebook instance.

• role_arn (str) – When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker AI assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker AI can perform these tasks. The policy must allow the SageMaker AI service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see SageMaker AI Roles. To be able to pass this role to SageMaker AI, the caller of this API must have the iam:PassRole permission.

• subnet_id (str | None) – The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

• security_group_ids (List[str] | None) – The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

• kms_key_id (str | None) – The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker AI uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• lifecycle_config_name (str | None) – The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

• direct_internet_access (str | None) – Sets whether SageMaker AI provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker AI training and endpoint services unless you configure a NAT Gateway in your VPC. For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

• volume_size_in_gb (int | None) – The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

• accelerator_types (List[str] | None) – This parameter is no longer supported. Elastic Inference (EI) is no longer available. This parameter was used to specify a list of EI instance types to associate with this notebook instance.

• default_code_repository (str | None) – A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

• additional_code_repositories (List[str] | None) – An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

• root_access (str | None) – Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled. Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

• platform_identifier (str | None) – The platform identifier of the notebook instance runtime environment.

• instance_metadata_service_configuration (InstanceMetadataServiceConfiguration | None) – Information on the IMDS configuration of the notebook instance

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The NotebookInstance resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

NotebookInstance | None

delete()

Delete a NotebookInstance resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(notebook_instance_name, session=None, region=None)

Get a NotebookInstance resource

Parameters:

• notebook_instance_name (str) – The name of the notebook instance that you want information about.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The NotebookInstance resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

NotebookInstance | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, notebook_instance_lifecycle_config_name_contains=<sagemaker_core.main.utils.Unassigned object>, default_code_repository_contains=<sagemaker_core.main.utils.Unassigned object>, additional_code_repository_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all NotebookInstance resources

Parameters:

• next_token – If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances. You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.

• max_results – The maximum number of notebook instances to return.

• sort_by (str | None) – The field to sort results by. The default is Name.

• sort_order (str | None) – The sort order for results.

• name_contains (str | None) – A string in the notebook instances’ name. This filter returns only notebook instances whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only notebook instances that were created before the specified time (timestamp).

• creation_time_after (datetime | None) – A filter that returns only notebook instances that were created after the specified time (timestamp).

• last_modified_time_before (datetime | None) – A filter that returns only notebook instances that were modified before the specified time (timestamp).

• last_modified_time_after (datetime | None) – A filter that returns only notebook instances that were modified after the specified time (timestamp).

• status_equals (str | None) – A filter that returns only notebook instances with the specified status.

• notebook_instance_lifecycle_config_name_contains (str | None) – A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.

• default_code_repository_contains (str | None) – A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.

• additional_code_repository_equals (str | None) – A filter that returns only notebook instances with associated with the specified git repository.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed NotebookInstance resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[NotebookInstance]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a NotebookInstance resource

Returns:

The NotebookInstance resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

NotebookInstance | None

stop()

Stop a NotebookInstance resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

update(instance_type=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, lifecycle_config_name=<sagemaker_core.main.utils.Unassigned object>, disassociate_lifecycle_config=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, default_code_repository=<sagemaker_core.main.utils.Unassigned object>, additional_code_repositories=<sagemaker_core.main.utils.Unassigned object>, accelerator_types=<sagemaker_core.main.utils.Unassigned object>, disassociate_accelerator_types=<sagemaker_core.main.utils.Unassigned object>, disassociate_default_code_repository=<sagemaker_core.main.utils.Unassigned object>, disassociate_additional_code_repositories=<sagemaker_core.main.utils.Unassigned object>, root_access=<sagemaker_core.main.utils.Unassigned object>, instance_metadata_service_configuration=<sagemaker_core.main.utils.Unassigned object>)

Update a NotebookInstance resource

Parameters:

• lifecycle_config_name (str | None) – The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

• disassociate_lifecycle_config (bool | None) – Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.

• disassociate_accelerator_types (bool | None) – This parameter is no longer supported. Elastic Inference (EI) is no longer available. This parameter was used to specify a list of the EI instance types to remove from this notebook instance.

• disassociate_default_code_repository (bool | None) – The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

• disassociate_additional_code_repositories (bool | None) – A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

• instance_type (str | None)

• role_arn (str | None)

• volume_size_in_gb (int | None)

• default_code_repository (str | None)

• additional_code_repositories (List[str] | None)

• accelerator_types (List[str] | None)

• root_access (str | None)

• instance_metadata_service_configuration (InstanceMetadataServiceConfiguration | None)

Returns:

The NotebookInstance resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

NotebookInstance | None

wait_for_delete(poll=5, timeout=None)

Wait for a NotebookInstance resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a NotebookInstance resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'InService', 'Stopping', 'Stopped', 'Failed', 'Deleting', 'Updating']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.NotebookInstanceLifecycleConfig(*, notebook_instance_lifecycle_config_name, notebook_instance_lifecycle_config_arn=<sagemaker_core.main.utils.Unassigned object>, on_create=<sagemaker_core.main.utils.Unassigned object>, on_start=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource NotebookInstanceLifecycleConfig

Parameters:

• notebook_instance_lifecycle_config_name (str)

• notebook_instance_lifecycle_config_arn (str | None)

• on_create (List[NotebookInstanceLifecycleHook] | None)

• on_start (List[NotebookInstanceLifecycleHook] | None)

• last_modified_time (datetime | None)

• creation_time (datetime | None)

notebook_instance_lifecycle_config_arn

The Amazon Resource Name (ARN) of the lifecycle configuration.

Type:

str | None

notebook_instance_lifecycle_config_name

The name of the lifecycle configuration.

Type:

str

on_create

The shell script that runs only once, when you create a notebook instance.

Type:

List[sagemaker_core.main.shapes.NotebookInstanceLifecycleHook] | None

on_start

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

Type:

List[sagemaker_core.main.shapes.NotebookInstanceLifecycleHook] | None

last_modified_time

A timestamp that tells when the lifecycle configuration was last modified.

Type:

datetime.datetime | None

creation_time

A timestamp that tells when the lifecycle configuration was created.

Type:

datetime.datetime | None

classmethod create(notebook_instance_lifecycle_config_name, on_create=<sagemaker_core.main.utils.Unassigned object>, on_start=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a NotebookInstanceLifecycleConfig resource

Parameters:

• notebook_instance_lifecycle_config_name (str) – The name of the lifecycle configuration.

• on_create (List[NotebookInstanceLifecycleHook] | None) – A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

• on_start (List[NotebookInstanceLifecycleHook] | None) – A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The NotebookInstanceLifecycleConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

NotebookInstanceLifecycleConfig | None

delete()

Delete a NotebookInstanceLifecycleConfig resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(notebook_instance_lifecycle_config_name, session=None, region=None)

Get a NotebookInstanceLifecycleConfig resource

Parameters:

• notebook_instance_lifecycle_config_name (str) – The name of the lifecycle configuration to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The NotebookInstanceLifecycleConfig resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

NotebookInstanceLifecycleConfig | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all NotebookInstanceLifecycleConfig resources

Parameters:

• next_token – If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.

• max_results – The maximum number of lifecycle configurations to return in the response.

• sort_by (str | None) – Sorts the list of results. The default is CreationTime.

• sort_order (str | None) – The sort order for results.

• name_contains (str | None) – A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

• creation_time_before (datetime | None) – A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

• creation_time_after (datetime | None) – A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

• last_modified_time_before (datetime | None) – A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

• last_modified_time_after (datetime | None) – A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed NotebookInstanceLifecycleConfig resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[NotebookInstanceLifecycleConfig]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a NotebookInstanceLifecycleConfig resource

Returns:

The NotebookInstanceLifecycleConfig resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

NotebookInstanceLifecycleConfig | None

update(on_create=<sagemaker_core.main.utils.Unassigned object>, on_start=<sagemaker_core.main.utils.Unassigned object>)

Update a NotebookInstanceLifecycleConfig resource

Returns:

The NotebookInstanceLifecycleConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Parameters:

• on_create (List[NotebookInstanceLifecycleHook] | None)

• on_start (List[NotebookInstanceLifecycleHook] | None)

Return type:

NotebookInstanceLifecycleConfig | None

class sagemaker_core.main.resources.OptimizationJob(*, optimization_job_name, optimization_job_arn=<sagemaker_core.main.utils.Unassigned object>, optimization_job_status=<sagemaker_core.main.utils.Unassigned object>, optimization_start_time=<sagemaker_core.main.utils.Unassigned object>, optimization_end_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, model_source=<sagemaker_core.main.utils.Unassigned object>, optimization_environment=<sagemaker_core.main.utils.Unassigned object>, deployment_instance_type=<sagemaker_core.main.utils.Unassigned object>, optimization_configs=<sagemaker_core.main.utils.Unassigned object>, output_config=<sagemaker_core.main.utils.Unassigned object>, optimization_output=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource OptimizationJob

Parameters:

• optimization_job_name (str)

• optimization_job_arn (str | None)

• optimization_job_status (str | None)

• optimization_start_time (datetime | None)

• optimization_end_time (datetime | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• model_source (OptimizationJobModelSource | None)

• optimization_environment (Dict[str, str] | None)

• deployment_instance_type (str | None)

• optimization_configs (List[OptimizationConfig] | None)

• output_config (OptimizationJobOutputConfig | None)

• optimization_output (OptimizationOutput | None)

• role_arn (str | None)

• stopping_condition (StoppingCondition | None)

• vpc_config (OptimizationVpcConfig | None)

optimization_job_arn

The Amazon Resource Name (ARN) of the optimization job.

Type:

str | None

optimization_job_status

The current status of the optimization job.

Type:

str | None

creation_time

The time when you created the optimization job.

Type:

datetime.datetime | None

last_modified_time

The time when the optimization job was last updated.

Type:

datetime.datetime | None

optimization_job_name

The name that you assigned to the optimization job.

Type:

str

model_source

The location of the source model to optimize with an optimization job.

Type:

sagemaker_core.main.shapes.OptimizationJobModelSource | None

deployment_instance_type

The type of instance that hosts the optimized model that you create with the optimization job.

Type:

str | None

optimization_configs

Settings for each of the optimization techniques that the job applies.

Type:

List[sagemaker_core.main.shapes.OptimizationConfig] | None

output_config

Details for where to store the optimized model that you create with the optimization job.

Type:

sagemaker_core.main.shapes.OptimizationJobOutputConfig | None

role_arn

The ARN of the IAM role that you assigned to the optimization job.

Type:

str | None

stopping_condition

Type:

sagemaker_core.main.shapes.StoppingCondition | None

optimization_start_time

The time when the optimization job started.

Type:

datetime.datetime | None

optimization_end_time

The time when the optimization job finished processing.

Type:

datetime.datetime | None

failure_reason

If the optimization job status is FAILED, the reason for the failure.

Type:

str | None

optimization_environment

The environment variables to set in the model container.

Type:

Dict[str, str] | None

optimization_output

Output values produced by an optimization job.

Type:

sagemaker_core.main.shapes.OptimizationOutput | None

vpc_config

A VPC in Amazon VPC that your optimized model has access to.

Type:

sagemaker_core.main.shapes.OptimizationVpcConfig | None

classmethod create(optimization_job_name, role_arn, model_source, deployment_instance_type, optimization_configs, output_config, stopping_condition, optimization_environment=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a OptimizationJob resource

Parameters:

• optimization_job_name (str) – A custom name for the new optimization job.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf. During model optimization, Amazon SageMaker AI needs your permission to: Read input data from an S3 bucket Write model artifacts to an S3 bucket Write logs to Amazon CloudWatch Logs Publish metrics to Amazon CloudWatch You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

• model_source (OptimizationJobModelSource) – The location of the source model to optimize with an optimization job.

• deployment_instance_type (str) – The type of instance that hosts the optimized model that you create with the optimization job.

• optimization_configs (List[OptimizationConfig]) – Settings for each of the optimization techniques that the job applies.

• output_config (OptimizationJobOutputConfig) – Details for where to store the optimized model that you create with the optimization job.

• stopping_condition (StoppingCondition)

• optimization_environment (Dict[str, str] | None) – The environment variables to set in the model container.

• tags (List[Tag] | None) – A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

• vpc_config (OptimizationVpcConfig | None) – A VPC in Amazon VPC that your optimized model has access to.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The OptimizationJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

OptimizationJob | None

delete()

Delete a OptimizationJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(optimization_job_name, session=None, region=None)

Get a OptimizationJob resource

Parameters:

• optimization_job_name (str) – The name that you assigned to the optimization job.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The OptimizationJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

OptimizationJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, optimization_contains=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all OptimizationJob resources

Parameters:

• next_token – A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

• max_results – The maximum number of optimization jobs to return in the response. The default is 50.

• creation_time_after (datetime | None) – Filters the results to only those optimization jobs that were created after the specified time.

• creation_time_before (datetime | None) – Filters the results to only those optimization jobs that were created before the specified time.

• last_modified_time_after (datetime | None) – Filters the results to only those optimization jobs that were updated after the specified time.

• last_modified_time_before (datetime | None) – Filters the results to only those optimization jobs that were updated before the specified time.

• optimization_contains (str | None) – Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either Quantization or Compilation.

• name_contains (str | None) – Filters the results to only those optimization jobs with a name that contains the specified string.

• status_equals (str | None) – Filters the results to only those optimization jobs with the specified status.

• sort_by (str | None) – The field by which to sort the optimization jobs in the response. The default is CreationTime

• sort_order (str | None) – The sort order for results. The default is Ascending

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed OptimizationJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[OptimizationJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a OptimizationJob resource

Returns:

The OptimizationJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

OptimizationJob | None

stop()

Stop a OptimizationJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None)

Wait for a OptimizationJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.PartnerApp(*, arn, name=<sagemaker_core.main.utils.Unassigned object>, type=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, base_url=<sagemaker_core.main.utils.Unassigned object>, maintenance_config=<sagemaker_core.main.utils.Unassigned object>, tier=<sagemaker_core.main.utils.Unassigned object>, version=<sagemaker_core.main.utils.Unassigned object>, application_config=<sagemaker_core.main.utils.Unassigned object>, auth_type=<sagemaker_core.main.utils.Unassigned object>, enable_iam_session_based_identity=<sagemaker_core.main.utils.Unassigned object>, error=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource PartnerApp

Parameters:

• arn (str)

• name (str | None)

• type (str | None)

• status (str | None)

• creation_time (datetime | None)

• execution_role_arn (str | None)

• base_url (str | None)

• maintenance_config (PartnerAppMaintenanceConfig | None)

• tier (str | None)

• version (str | None)

• application_config (PartnerAppConfig | None)

• auth_type (str | None)

• enable_iam_session_based_identity (bool | None)

• error (ErrorInfo | None)

arn

The ARN of the SageMaker Partner AI App that was described.

Type:

str

name

The name of the SageMaker Partner AI App.

Type:

str | None

type

The type of SageMaker Partner AI App. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.

Type:

str | None

status

The status of the SageMaker Partner AI App.

Type:

str | None

creation_time

The time that the SageMaker Partner AI App was created.

Type:

datetime.datetime | None

execution_role_arn

The ARN of the IAM role associated with the SageMaker Partner AI App.

Type:

str | None

base_url

The URL of the SageMaker Partner AI App that the Application SDK uses to support in-app calls for the user.

Type:

str | None

maintenance_config

Maintenance configuration settings for the SageMaker Partner AI App.

Type:

sagemaker_core.main.shapes.PartnerAppMaintenanceConfig | None

tier

The instance type and size of the cluster attached to the SageMaker Partner AI App.

Type:

str | None

version

The version of the SageMaker Partner AI App.

Type:

str | None

application_config

Configuration settings for the SageMaker Partner AI App.

Type:

sagemaker_core.main.shapes.PartnerAppConfig | None

auth_type

The authorization type that users use to access the SageMaker Partner AI App.

Type:

str | None

enable_iam_session_based_identity

When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.

Type:

bool | None

error

This is an error field object that contains the error code and the reason for an operation failure.

Type:

sagemaker_core.main.shapes.ErrorInfo | None

classmethod create(name, type, execution_role_arn, tier, auth_type, maintenance_config=<sagemaker_core.main.utils.Unassigned object>, application_config=<sagemaker_core.main.utils.Unassigned object>, enable_iam_session_based_identity=<sagemaker_core.main.utils.Unassigned object>, client_token=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a PartnerApp resource

Parameters:

• name (str) – The name to give the SageMaker Partner AI App.

• type (str) – The type of SageMaker Partner AI App to create. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.

• execution_role_arn (str) – The ARN of the IAM role that the partner application uses.

• tier (str) – Indicates the instance type and size of the cluster attached to the SageMaker Partner AI App.

• auth_type (str) – The authorization type that users use to access the SageMaker Partner AI App.

• maintenance_config (PartnerAppMaintenanceConfig | None) – Maintenance configuration settings for the SageMaker Partner AI App.

• application_config (PartnerAppConfig | None) – Configuration settings for the SageMaker Partner AI App.

• enable_iam_session_based_identity (bool | None) – When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.

• client_token (str | None) – A unique token that guarantees that the call to this API is idempotent.

• tags (List[Tag] | None) – Each tag consists of a key and an optional value. Tag keys must be unique per resource.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The PartnerApp resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

PartnerApp | None

delete(client_token=<sagemaker_core.main.utils.Unassigned object>)

Delete a PartnerApp resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Parameters:

client_token (str | None)

Return type:

None

classmethod get(arn, session=None, region=None)

Get a PartnerApp resource

Parameters:

• arn (str) – The ARN of the SageMaker Partner AI App to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The PartnerApp resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

PartnerApp | None

classmethod get_all(session=None, region=None)

Get all PartnerApp resources.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed PartnerApp resources.

Return type:

ResourceIterator[PartnerApp]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a PartnerApp resource

Returns:

The PartnerApp resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

PartnerApp | None

update(maintenance_config=<sagemaker_core.main.utils.Unassigned object>, tier=<sagemaker_core.main.utils.Unassigned object>, application_config=<sagemaker_core.main.utils.Unassigned object>, enable_iam_session_based_identity=<sagemaker_core.main.utils.Unassigned object>, client_token=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

Update a PartnerApp resource

Parameters:

• client_token (str | None) – A unique token that guarantees that the call to this API is idempotent.

• tags (List[Tag] | None) – Each tag consists of a key and an optional value. Tag keys must be unique per resource.

• maintenance_config (PartnerAppMaintenanceConfig | None)

• tier (str | None)

• application_config (PartnerAppConfig | None)

• enable_iam_session_based_identity (bool | None)

Returns:

The PartnerApp resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

PartnerApp | None

wait_for_delete(poll=5, timeout=None)

Wait for a PartnerApp resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a PartnerApp resource to reach certain status.

Parameters:

• target_status (Literal['Creating', 'Updating', 'Deleting', 'Available', 'Failed', 'UpdateFailed', 'Deleted']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.PartnerAppPresignedUrl(*, arn, expires_in_seconds=<sagemaker_core.main.utils.Unassigned object>, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>, url=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource PartnerAppPresignedUrl

Parameters:

• arn (str)

• expires_in_seconds (int | None)

• session_expiration_duration_in_seconds (int | None)

• url (str | None)

arn

The ARN of the SageMaker Partner AI App to create the presigned URL for.

Type:

str

expires_in_seconds

The time that will pass before the presigned URL expires.

Type:

int | None

session_expiration_duration_in_seconds

Indicates how long the Amazon SageMaker Partner AI App session can be accessed for after logging in.

Type:

int | None

url

The presigned URL that you can use to access the SageMaker Partner AI App.

Type:

str | None

classmethod create(arn, expires_in_seconds=<sagemaker_core.main.utils.Unassigned object>, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

Create a PartnerAppPresignedUrl resource

Parameters:

• arn (str) – The ARN of the SageMaker Partner AI App to create the presigned URL for.

• expires_in_seconds (int | None) – The time that will pass before the presigned URL expires.

• session_expiration_duration_in_seconds (int | None) – Indicates how long the Amazon SageMaker Partner AI App session can be accessed for after logging in.

• session – Boto3 session.

• region – Region name.

Returns:

The PartnerAppPresignedUrl resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

PartnerAppPresignedUrl | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.Pipeline(*, pipeline_name, pipeline_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_definition=<sagemaker_core.main.utils.Unassigned object>, pipeline_description=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_run_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Pipeline

Parameters:

• pipeline_name (str)

• pipeline_arn (str | None)

• pipeline_display_name (str | None)

• pipeline_definition (str | None)

• pipeline_description (str | None)

• role_arn (str | None)

• pipeline_status (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• last_run_time (datetime | None)

• created_by (UserContext | None)

• last_modified_by (UserContext | None)

• parallelism_configuration (ParallelismConfiguration | None)

pipeline_arn

The Amazon Resource Name (ARN) of the pipeline.

Type:

str | None

pipeline_name

The name of the pipeline.

Type:

str

pipeline_display_name

The display name of the pipeline.

Type:

str | None

pipeline_definition

The JSON pipeline definition.

Type:

str | None

pipeline_description

The description of the pipeline.

Type:

str | None

role_arn

The Amazon Resource Name (ARN) that the pipeline uses to execute.

Type:

str | None

pipeline_status

The status of the pipeline execution.

Type:

str | None

creation_time

The time when the pipeline was created.

Type:

datetime.datetime | None

last_modified_time

The time when the pipeline was last modified.

Type:

datetime.datetime | None

last_run_time

The time when the pipeline was last run.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

parallelism_configuration

Lists the parallelism configuration applied to the pipeline.

Type:

sagemaker_core.main.shapes.ParallelismConfiguration | None

classmethod create(pipeline_name, client_request_token, role_arn, pipeline_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_definition=<sagemaker_core.main.utils.Unassigned object>, pipeline_definition_s3_location=<sagemaker_core.main.utils.Unassigned object>, pipeline_description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Pipeline resource

Parameters:

• pipeline_name (str) – The name of the pipeline.

• client_request_token (str) – A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

• role_arn (str) – The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.

• pipeline_display_name (str | None) – The display name of the pipeline.

• pipeline_definition (str | None) – The JSON pipeline definition of the pipeline.

• pipeline_definition_s3_location (PipelineDefinitionS3Location | None) – The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

• pipeline_description (str | None) – A description of the pipeline.

• tags (List[Tag] | None) – A list of tags to apply to the created pipeline.

• parallelism_configuration (ParallelismConfiguration | None) – This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Pipeline resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Pipeline | None

delete(client_request_token)

Delete a Pipeline resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Parameters:

client_request_token (str)

Return type:

None

classmethod get(pipeline_name, session=None, region=None)

Get a Pipeline resource

Parameters:

• pipeline_name (str) – The name or Amazon Resource Name (ARN) of the pipeline to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Pipeline resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Pipeline | None

classmethod get_all(pipeline_name_prefix=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Pipeline resources

Parameters:

• pipeline_name_prefix (str | None) – The prefix of the pipeline name.

• created_after (datetime | None) – A filter that returns the pipelines that were created after a specified time.

• created_before (datetime | None) – A filter that returns the pipelines that were created before a specified time.

• sort_by (str | None) – The field by which to sort results. The default is CreatedTime.

• sort_order (str | None) – The sort order for results.

• next_token – If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.

• max_results – The maximum number of pipelines to return in the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Pipeline resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Pipeline]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Pipeline resource

Returns:

The Pipeline resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Pipeline | None

update(pipeline_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_definition=<sagemaker_core.main.utils.Unassigned object>, pipeline_definition_s3_location=<sagemaker_core.main.utils.Unassigned object>, pipeline_description=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>)

Update a Pipeline resource

Parameters:

• pipeline_definition_s3_location (PipelineDefinitionS3Location | None) – The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

• pipeline_display_name (str | None)

• pipeline_definition (str | None)

• pipeline_description (str | None)

• role_arn (str | None)

• parallelism_configuration (ParallelismConfiguration | None)

Returns:

The Pipeline resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

Pipeline | None

wait_for_delete(poll=5, timeout=None)

Wait for a Pipeline resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Pipeline resource to reach certain status.

Parameters:

• target_status (Literal['Active', 'Deleting']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.PipelineExecution(*, pipeline_execution_arn, pipeline_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_status=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_description=<sagemaker_core.main.utils.Unassigned object>, pipeline_experiment_config=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>, selective_execution_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource PipelineExecution

Parameters:

• pipeline_execution_arn (str)

• pipeline_arn (str | None)

• pipeline_execution_display_name (str | None)

• pipeline_execution_status (str | None)

• pipeline_execution_description (str | None)

• pipeline_experiment_config (PipelineExperimentConfig | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• created_by (UserContext | None)

• last_modified_by (UserContext | None)

• parallelism_configuration (ParallelismConfiguration | None)

• selective_execution_config (SelectiveExecutionConfig | None)

pipeline_arn

The Amazon Resource Name (ARN) of the pipeline.

Type:

str | None

pipeline_execution_arn

The Amazon Resource Name (ARN) of the pipeline execution.

Type:

str

pipeline_execution_display_name

The display name of the pipeline execution.

Type:

str | None

pipeline_execution_status

The status of the pipeline execution.

Type:

str | None

pipeline_execution_description

The description of the pipeline execution.

Type:

str | None

pipeline_experiment_config

Type:

sagemaker_core.main.shapes.PipelineExperimentConfig | None

failure_reason

If the execution failed, a message describing why.

Type:

str | None

creation_time

The time when the pipeline execution was created.

Type:

datetime.datetime | None

last_modified_time

The time when the pipeline execution was modified last.

Type:

datetime.datetime | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

parallelism_configuration

The parallelism configuration applied to the pipeline.

Type:

sagemaker_core.main.shapes.ParallelismConfiguration | None

selective_execution_config

The selective execution configuration applied to the pipeline run.

Type:

sagemaker_core.main.shapes.SelectiveExecutionConfig | None

classmethod get(pipeline_execution_arn, session=None, region=None)

Get a PipelineExecution resource

Parameters:

• pipeline_execution_arn (str) – The Amazon Resource Name (ARN) of the pipeline execution.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The PipelineExecution resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

PipelineExecution | None

classmethod get_all(pipeline_name, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all PipelineExecution resources

Parameters:

• pipeline_name (str) – The name or Amazon Resource Name (ARN) of the pipeline.

• created_after (datetime | None) – A filter that returns the pipeline executions that were created after a specified time.

• created_before (datetime | None) – A filter that returns the pipeline executions that were created before a specified time.

• sort_by (str | None) – The field by which to sort results. The default is CreatedTime.

• sort_order (str | None) – The sort order for results.

• next_token – If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.

• max_results – The maximum number of pipeline executions to return in the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed PipelineExecution resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[PipelineExecution]

get_all_parameters(session=None, region=None)

Gets a list of parameters for a pipeline execution.

Parameters:

• next_token – If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.

• max_results – The maximum number of parameters to return in the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Parameter.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[Parameter]

get_all_steps(sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Gets a list of PipeLineExecutionStep objects.

Parameters:

• next_token – If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.

• max_results – The maximum number of pipeline execution steps to return in the response.

• sort_order (str | None) – The field by which to sort results. The default is CreatedTime.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed PipelineExecutionStep.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[PipelineExecutionStep]

get_pipeline_definition(session=None, region=None)

Describes the details of an execution’s pipeline definition.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

DescribePipelineDefinitionForExecutionResponse

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

DescribePipelineDefinitionForExecutionResponse | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a PipelineExecution resource

Returns:

The PipelineExecution resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

PipelineExecution | None

retry(client_request_token, session=None, region=None)

Retry the execution of the pipeline.

Parameters:

• client_request_token (str) – A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

None

send_execution_step_failure(callback_token, client_request_token=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Notifies the pipeline that the execution of a callback step failed, along with a message describing why.

Parameters:

• callback_token (str) – The pipeline generated token from the Amazon SQS queue.

• client_request_token (str | None) – A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

None

send_execution_step_success(callback_token, output_parameters=<sagemaker_core.main.utils.Unassigned object>, client_request_token=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step’s output parameters.

Parameters:

• callback_token (str) – The pipeline generated token from the Amazon SQS queue.

• output_parameters (List[OutputParameter] | None) – A list of the output parameters of the callback step.

• client_request_token (str | None) – A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

None

stop()

Stop a PipelineExecution resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

None

update(pipeline_execution_description=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_display_name=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>)

Update a PipelineExecution resource

Returns:

The PipelineExecution resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Parameters:

• pipeline_execution_description (str | None)

• pipeline_execution_display_name (str | None)

• parallelism_configuration (ParallelismConfiguration | None)

Return type:

PipelineExecution | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a PipelineExecution resource to reach certain status.

Parameters:

• target_status (Literal['Executing', 'Stopping', 'Stopped', 'Failed', 'Succeeded']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.PresignedDomainUrl(*, domain_id, user_profile_name, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>, expires_in_seconds=<sagemaker_core.main.utils.Unassigned object>, space_name=<sagemaker_core.main.utils.Unassigned object>, landing_uri=<sagemaker_core.main.utils.Unassigned object>, authorized_url=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource PresignedDomainUrl

Parameters:

• domain_id (str)

• user_profile_name (str | object)

• session_expiration_duration_in_seconds (int | None)

• expires_in_seconds (int | None)

• space_name (str | object | None)

• landing_uri (str | None)

• authorized_url (str | None)

domain_id

The domain ID.

Type:

str

user_profile_name

The name of the UserProfile to sign-in as.

Type:

str | object

session_expiration_duration_in_seconds

The session expiration duration in seconds. This value defaults to 43200.

Type:

int | None

expires_in_seconds

The number of seconds until the pre-signed URL expires. This value defaults to 300.

Type:

int | None

space_name

The name of the space.

Type:

str | object | None

landing_uri

The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are: studio::relative/path: Directs users to the relative path in Studio. app:JupyterServer:relative/path: Directs users to the relative path in the Studio Classic application. app:JupyterLab:relative/path: Directs users to the relative path in the JupyterLab application. app:RStudioServerPro:relative/path: Directs users to the relative path in the RStudio application. app:CodeEditor:relative/path: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application. app:Canvas:relative/path: Directs users to the relative path in the Canvas application.

Type:

str | None

authorized_url

The presigned URL.

Type:

str | None

classmethod create(domain_id, user_profile_name, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>, expires_in_seconds=<sagemaker_core.main.utils.Unassigned object>, space_name=<sagemaker_core.main.utils.Unassigned object>, landing_uri=<sagemaker_core.main.utils.Unassigned object>)

Create a PresignedDomainUrl resource

Parameters:

• domain_id (str) – The domain ID.

• user_profile_name (str | object) – The name of the UserProfile to sign-in as.

• session_expiration_duration_in_seconds (int | None) – The session expiration duration in seconds. This value defaults to 43200.

• expires_in_seconds (int | None) – The number of seconds until the pre-signed URL expires. This value defaults to 300.

• space_name (str | object | None) – The name of the space.

• landing_uri (str | None) – The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are: studio::relative/path: Directs users to the relative path in Studio. app:JupyterServer:relative/path: Directs users to the relative path in the Studio Classic application. app:JupyterLab:relative/path: Directs users to the relative path in the JupyterLab application. app:RStudioServerPro:relative/path: Directs users to the relative path in the RStudio application. app:CodeEditor:relative/path: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application. app:Canvas:relative/path: Directs users to the relative path in the Canvas application.

• session – Boto3 session.

• region – Region name.

Returns:

The PresignedDomainUrl resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

PresignedDomainUrl | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.PresignedMlflowTrackingServerUrl(*, tracking_server_name, expires_in_seconds=<sagemaker_core.main.utils.Unassigned object>, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>, authorized_url=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource PresignedMlflowTrackingServerUrl

Parameters:

• tracking_server_name (str)

• expires_in_seconds (int | None)

• session_expiration_duration_in_seconds (int | None)

• authorized_url (str | None)

tracking_server_name

The name of the tracking server to connect to your MLflow UI.

Type:

str

expires_in_seconds

The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.

Type:

int | None

session_expiration_duration_in_seconds

The duration in seconds that your MLflow UI session is valid.

Type:

int | None

authorized_url

A presigned URL with an authorization token.

Type:

str | None

classmethod create(tracking_server_name, expires_in_seconds=<sagemaker_core.main.utils.Unassigned object>, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

Create a PresignedMlflowTrackingServerUrl resource

Parameters:

• tracking_server_name (str) – The name of the tracking server to connect to your MLflow UI.

• expires_in_seconds (int | None) – The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.

• session_expiration_duration_in_seconds (int | None) – The duration in seconds that your MLflow UI session is valid.

• session – Boto3 session.

• region – Region name.

Returns:

The PresignedMlflowTrackingServerUrl resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

PresignedMlflowTrackingServerUrl | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.PresignedNotebookInstanceUrl(*, notebook_instance_name, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>, authorized_url=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource PresignedNotebookInstanceUrl

Parameters:

• notebook_instance_name (str | object)

• session_expiration_duration_in_seconds (int | None)

• authorized_url (str | None)

notebook_instance_name

The name of the notebook instance.

Type:

str | object

session_expiration_duration_in_seconds

The duration of the session, in seconds. The default is 12 hours.

Type:

int | None

authorized_url

A JSON object that contains the URL string.

Type:

str | None

classmethod create(notebook_instance_name, session_expiration_duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

Create a PresignedNotebookInstanceUrl resource

Parameters:

• notebook_instance_name (str | object) – The name of the notebook instance.

• session_expiration_duration_in_seconds (int | None) – The duration of the session, in seconds. The default is 12 hours.

• session – Boto3 session.

• region – Region name.

Returns:

The PresignedNotebookInstanceUrl resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

PresignedNotebookInstanceUrl | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.ProcessingJob(*, processing_job_name, processing_inputs=<sagemaker_core.main.utils.Unassigned object>, processing_output_config=<sagemaker_core.main.utils.Unassigned object>, processing_resources=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, app_specification=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>, processing_job_arn=<sagemaker_core.main.utils.Unassigned object>, processing_job_status=<sagemaker_core.main.utils.Unassigned object>, exit_message=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, processing_end_time=<sagemaker_core.main.utils.Unassigned object>, processing_start_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_arn=<sagemaker_core.main.utils.Unassigned object>, training_job_arn=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource ProcessingJob

Parameters:

• processing_job_name (str)

• processing_inputs (List[ProcessingInput] | None)

• processing_output_config (ProcessingOutputConfig | None)

• processing_resources (ProcessingResources | None)

• stopping_condition (ProcessingStoppingCondition | None)

• app_specification (AppSpecification | None)

• environment (Dict[str, str] | None)

• network_config (NetworkConfig | None)

• role_arn (str | None)

• experiment_config (ExperimentConfig | None)

• processing_job_arn (str | None)

• processing_job_status (str | None)

• exit_message (str | None)

• failure_reason (str | None)

• processing_end_time (datetime | None)

• processing_start_time (datetime | None)

• last_modified_time (datetime | None)

• creation_time (datetime | None)

• monitoring_schedule_arn (str | None)

• auto_ml_job_arn (str | None)

• training_job_arn (str | None)

processing_job_name

The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

Type:

str

processing_resources

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

Type:

sagemaker_core.main.shapes.ProcessingResources | None

app_specification

Configures the processing job to run a specified container image.

Type:

sagemaker_core.main.shapes.AppSpecification | None

processing_job_arn

The Amazon Resource Name (ARN) of the processing job.

Type:

str | None

processing_job_status

Provides the status of a processing job.

Type:

str | None

creation_time

The time at which the processing job was created.

Type:

datetime.datetime | None

processing_inputs

The inputs for a processing job.

Type:

List[sagemaker_core.main.shapes.ProcessingInput] | None

processing_output_config

Output configuration for the processing job.

Type:

sagemaker_core.main.shapes.ProcessingOutputConfig | None

stopping_condition

The time limit for how long the processing job is allowed to run.

Type:

sagemaker_core.main.shapes.ProcessingStoppingCondition | None

environment

The environment variables set in the Docker container.

Type:

Dict[str, str] | None

network_config

Networking options for a processing job.

Type:

sagemaker_core.main.shapes.NetworkConfig | None

role_arn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Type:

str | None

experiment_config

The configuration information used to create an experiment.

Type:

sagemaker_core.main.shapes.ExperimentConfig | None

exit_message

An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

Type:

str | None

failure_reason

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

Type:

str | None

processing_end_time

The time at which the processing job completed.

Type:

datetime.datetime | None

processing_start_time

The time at which the processing job started.

Type:

datetime.datetime | None

last_modified_time

The time at which the processing job was last modified.

Type:

datetime.datetime | None

monitoring_schedule_arn

The ARN of a monitoring schedule for an endpoint associated with this processing job.

Type:

str | None

auto_ml_job_arn

The ARN of an AutoML job associated with this processing job.

Type:

str | None

training_job_arn

The ARN of a training job associated with this processing job.

Type:

str | None

classmethod create(processing_job_name, processing_resources, app_specification, role_arn, processing_inputs=<sagemaker_core.main.utils.Unassigned object>, processing_output_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a ProcessingJob resource

Parameters:

• processing_job_name (str) – The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• processing_resources (ProcessingResources) – Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

• app_specification (AppSpecification) – Configures the processing job to run a specified Docker container image.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

• processing_inputs (List[ProcessingInput] | None) – An array of inputs configuring the data to download into the processing container.

• processing_output_config (ProcessingOutputConfig | None) – Output configuration for the processing job.

• stopping_condition (ProcessingStoppingCondition | None) – The time limit for how long the processing job is allowed to run.

• environment (Dict[str, str] | None) – The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.

• network_config (NetworkConfig | None) – Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• experiment_config (ExperimentConfig | None)

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ProcessingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

ProcessingJob | None

classmethod get(processing_job_name, session=None, region=None)

Get a ProcessingJob resource

Parameters:

• processing_job_name (str) – The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The ProcessingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ProcessingJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ProcessingJob resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only processing jobs created after the specified time.

• creation_time_before (datetime | None) – A filter that returns only processing jobs created after the specified time.

• last_modified_time_after (datetime | None) – A filter that returns only processing jobs modified after the specified time.

• last_modified_time_before (datetime | None) – A filter that returns only processing jobs modified before the specified time.

• name_contains (str | None) – A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.

• status_equals (str | None) – A filter that retrieves only processing jobs with a specific status.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• next_token – If the result of the previous ListProcessingJobs request was truncated, the response includes a NextToken. To retrieve the next set of processing jobs, use the token in the next request.

• max_results – The maximum number of processing jobs to return in the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ProcessingJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ProcessingJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a ProcessingJob resource

Returns:

The ProcessingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ProcessingJob | None

stop()

Stop a ProcessingJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None, logs=False)

Wait for a ProcessingJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

• logs (bool | None) – Whether to print logs while waiting.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Project(*, project_name, project_arn=<sagemaker_core.main.utils.Unassigned object>, project_id=<sagemaker_core.main.utils.Unassigned object>, project_description=<sagemaker_core.main.utils.Unassigned object>, service_catalog_provisioning_details=<sagemaker_core.main.utils.Unassigned object>, service_catalog_provisioned_product_details=<sagemaker_core.main.utils.Unassigned object>, project_status=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Project

Parameters:

• project_name (str)

• project_arn (str | None)

• project_id (str | None)

• project_description (str | None)

• service_catalog_provisioning_details (ServiceCatalogProvisioningDetails | None)

• service_catalog_provisioned_product_details (ServiceCatalogProvisionedProductDetails | None)

• project_status (str | None)

• created_by (UserContext | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

project_arn

The Amazon Resource Name (ARN) of the project.

Type:

str | None

project_name

The name of the project.

Type:

str

project_id

The ID of the project.

Type:

str | None

service_catalog_provisioning_details

Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.

Type:

sagemaker_core.main.shapes.ServiceCatalogProvisioningDetails | None

project_status

The status of the project.

Type:

str | None

creation_time

The time when the project was created.

Type:

datetime.datetime | None

project_description

The description of the project.

Type:

str | None

service_catalog_provisioned_product_details

Information about a provisioned service catalog product.

Type:

sagemaker_core.main.shapes.ServiceCatalogProvisionedProductDetails | None

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

The timestamp when project was last modified.

Type:

datetime.datetime | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

classmethod create(project_name, service_catalog_provisioning_details, project_description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Project resource

Parameters:

• project_name (str) – The name of the project.

• service_catalog_provisioning_details (ServiceCatalogProvisioningDetails) – The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don’t provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.

• project_description (str | None) – A description for the project.

• tags (List[Tag] | None) – An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Project resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Project | None

delete()

Delete a Project resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

None

classmethod get(project_name, session=None, region=None)

Get a Project resource

Parameters:

• project_name (str) – The name of the project to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Project resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Project | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Project resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns the projects that were created after a specified time.

• creation_time_before (datetime | None) – A filter that returns the projects that were created before a specified time.

• max_results – The maximum number of projects to return in the response.

• name_contains (str | None) – A filter that returns the projects whose name contains a specified string.

• next_token – If the result of the previous ListProjects request was truncated, the response includes a NextToken. To retrieve the next set of projects, use the token in the next request.

• sort_by (str | None) – The field by which to sort results. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Project resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Project]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Project resource

Returns:

The Project resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Project | None

update(project_description=<sagemaker_core.main.utils.Unassigned object>, service_catalog_provisioning_update_details=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

Update a Project resource

Parameters:

• service_catalog_provisioning_update_details (ServiceCatalogProvisioningUpdateDetails | None) – The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don’t provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. In addition, the project must have tag update constraints set in order to include this parameter in the request. For more information, see Amazon Web Services Service Catalog Tag Update Constraints.

• project_description (str | None)

Returns:

The Project resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

Project | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Project resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'CreateInProgress', 'CreateCompleted', 'CreateFailed', 'DeleteInProgress', 'DeleteFailed', 'DeleteCompleted', 'UpdateInProgress', 'UpdateCompleted', 'UpdateFailed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.ResourceCatalog(*, resource_catalog_arn, resource_catalog_name, description, creation_time)

Class representing resource ResourceCatalog

Parameters:

• resource_catalog_arn (str)

• resource_catalog_name (str)

• description (str)

• creation_time (datetime)

resource_catalog_arn

The Amazon Resource Name (ARN) of the ResourceCatalog.

Type:

str

resource_catalog_name

The name of the ResourceCatalog.

Type:

str

description

A free form description of the ResourceCatalog.

Type:

str

creation_time

The time the ResourceCatalog was created.

Type:

datetime.datetime

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all ResourceCatalog resources

Parameters:

• name_contains (str | None) – A string that partially matches one or more ResourceCatalogs names. Filters ResourceCatalog by name.

• creation_time_after (datetime | None) – Use this parameter to search for ResourceCatalogs created after a specific date and time.

• creation_time_before (datetime | None) – Use this parameter to search for ResourceCatalogs created before a specific date and time.

• sort_order (str | None) – The order in which the resource catalogs are listed.

• sort_by (str | None) – The value on which the resource catalog list is sorted.

• max_results – The maximum number of results returned by ListResourceCatalogs.

• next_token – A token to resume pagination of ListResourceCatalogs results.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed ResourceCatalog resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[ResourceCatalog]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.SagemakerServicecatalogPortfolio

Class representing resource SagemakerServicecatalogPortfolio

static disable(session=None, region=None)

Disables using Service Catalog in SageMaker.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

static enable(session=None, region=None)

Enables using Service Catalog in SageMaker.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

static get_status(session=None, region=None)

Gets the status of Service Catalog in SageMaker.

Parameters:

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

str

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.Space(*, domain_id, space_name, space_arn=<sagemaker_core.main.utils.Unassigned object>, home_efs_file_system_uid=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, space_settings=<sagemaker_core.main.utils.Unassigned object>, ownership_settings=<sagemaker_core.main.utils.Unassigned object>, space_sharing_settings=<sagemaker_core.main.utils.Unassigned object>, space_display_name=<sagemaker_core.main.utils.Unassigned object>, url=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Space

Parameters:

• domain_id (str)

• space_name (str)

• space_arn (str | None)

• home_efs_file_system_uid (str | None)

• status (str | None)

• last_modified_time (datetime | None)

• creation_time (datetime | None)

• failure_reason (str | None)

• space_settings (SpaceSettings | None)

• ownership_settings (OwnershipSettings | None)

• space_sharing_settings (SpaceSharingSettings | None)

• space_display_name (str | None)

• url (str | None)

domain_id

The ID of the associated domain.

Type:

str

space_arn

The space’s Amazon Resource Name (ARN).

Type:

str | None

space_name

The name of the space.

Type:

str

home_efs_file_system_uid

The ID of the space’s profile in the Amazon EFS volume.

Type:

str | None

status

The status.

Type:

str | None

last_modified_time

The last modified time.

Type:

datetime.datetime | None

creation_time

The creation time.

Type:

datetime.datetime | None

failure_reason

The failure reason.

Type:

str | None

space_settings

A collection of space settings.

Type:

sagemaker_core.main.shapes.SpaceSettings | None

ownership_settings

The collection of ownership settings for a space.

Type:

sagemaker_core.main.shapes.OwnershipSettings | None

space_sharing_settings

The collection of space sharing settings for a space.

Type:

sagemaker_core.main.shapes.SpaceSharingSettings | None

space_display_name

The name of the space that appears in the Amazon SageMaker Studio UI.

Type:

str | None

url

Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center. The following application types are supported: Studio Classic: &amp;redirect=JupyterServer JupyterLab: &amp;redirect=JupyterLab Code Editor, based on Code-OSS, Visual Studio Code - Open Source: &amp;redirect=CodeEditor

Type:

str | None

classmethod create(domain_id, space_name, tags=<sagemaker_core.main.utils.Unassigned object>, space_settings=<sagemaker_core.main.utils.Unassigned object>, ownership_settings=<sagemaker_core.main.utils.Unassigned object>, space_sharing_settings=<sagemaker_core.main.utils.Unassigned object>, space_display_name=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Space resource

Parameters:

• domain_id (str) – The ID of the associated domain.

• space_name (str) – The name of the space.

• tags (List[Tag] | None) – Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.

• space_settings (SpaceSettings | None) – A collection of space settings.

• ownership_settings (OwnershipSettings | None) – A collection of ownership settings.

• space_sharing_settings (SpaceSharingSettings | None) – A collection of space sharing settings.

• space_display_name (str | None) – The name of the space that appears in the SageMaker Studio UI.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Space resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Space | None

delete()

Delete a Space resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(domain_id, space_name, session=None, region=None)

Get a Space resource

Parameters:

• domain_id (str) – The ID of the associated domain.

• space_name (str) – The name of the space.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Space resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Space | None

classmethod get_all(sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, domain_id_equals=<sagemaker_core.main.utils.Unassigned object>, space_name_contains=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Space resources

Parameters:

• next_token – If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• max_results – This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• sort_by (str | None) – The parameter by which to sort the results. The default is CreationTime.

• domain_id_equals (str | None) – A parameter to search for the domain ID.

• space_name_contains (str | None) – A parameter by which to filter the results.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Space resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Space]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Space resource

Returns:

The Space resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Space | None

update(space_settings=<sagemaker_core.main.utils.Unassigned object>, space_display_name=<sagemaker_core.main.utils.Unassigned object>)

Update a Space resource

Returns:

The Space resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Parameters:

• space_settings (SpaceSettings | None)

• space_display_name (str | None)

Return type:

Space | None

wait_for_delete(poll=5, timeout=None)

Wait for a Space resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Space resource to reach certain status.

Parameters:

• target_status (Literal['Deleting', 'Failed', 'InService', 'Pending', 'Updating', 'Update_Failed', 'Delete_Failed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.StudioLifecycleConfig(*, studio_lifecycle_config_name, studio_lifecycle_config_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, studio_lifecycle_config_content=<sagemaker_core.main.utils.Unassigned object>, studio_lifecycle_config_app_type=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource StudioLifecycleConfig

Parameters:

• studio_lifecycle_config_name (str)

• studio_lifecycle_config_arn (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• studio_lifecycle_config_content (str | None)

• studio_lifecycle_config_app_type (str | None)

studio_lifecycle_config_arn

The ARN of the Lifecycle Configuration to describe.

Type:

str | None

studio_lifecycle_config_name

The name of the Amazon SageMaker AI Studio Lifecycle Configuration that is described.

Type:

str

creation_time

The creation time of the Amazon SageMaker AI Studio Lifecycle Configuration.

Type:

datetime.datetime | None

last_modified_time

This value is equivalent to CreationTime because Amazon SageMaker AI Studio Lifecycle Configurations are immutable.

Type:

datetime.datetime | None

studio_lifecycle_config_content

The content of your Amazon SageMaker AI Studio Lifecycle Configuration script.

Type:

str | None

studio_lifecycle_config_app_type

The App type that the Lifecycle Configuration is attached to.

Type:

str | None

classmethod create(studio_lifecycle_config_name, studio_lifecycle_config_content, studio_lifecycle_config_app_type, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a StudioLifecycleConfig resource

Parameters:

• studio_lifecycle_config_name (str) – The name of the Amazon SageMaker AI Studio Lifecycle Configuration to create.

• studio_lifecycle_config_content (str) – The content of your Amazon SageMaker AI Studio Lifecycle Configuration script. This content must be base64 encoded.

• studio_lifecycle_config_app_type (str) – The App type that the Lifecycle Configuration is attached to.

• tags (List[Tag] | None) – Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The StudioLifecycleConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

StudioLifecycleConfig | None

delete()

Delete a StudioLifecycleConfig resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(studio_lifecycle_config_name, session=None, region=None)

Get a StudioLifecycleConfig resource

Parameters:

• studio_lifecycle_config_name (str) – The name of the Amazon SageMaker AI Studio Lifecycle Configuration to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The StudioLifecycleConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

StudioLifecycleConfig | None

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, app_type_equals=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, modified_time_before=<sagemaker_core.main.utils.Unassigned object>, modified_time_after=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all StudioLifecycleConfig resources

Parameters:

• max_results – The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.

• next_token – If the previous call to ListStudioLifecycleConfigs didn’t return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.

• name_contains (str | None) – A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.

• app_type_equals (str | None) – A parameter to search for the App Type to which the Lifecycle Configuration is attached.

• creation_time_before (datetime | None) – A filter that returns only Lifecycle Configurations created on or before the specified time.

• creation_time_after (datetime | None) – A filter that returns only Lifecycle Configurations created on or after the specified time.

• modified_time_before (datetime | None) – A filter that returns only Lifecycle Configurations modified before the specified time.

• modified_time_after (datetime | None) – A filter that returns only Lifecycle Configurations modified after the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed StudioLifecycleConfig resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

Return type:

ResourceIterator[StudioLifecycleConfig]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a StudioLifecycleConfig resource

Returns:

The StudioLifecycleConfig resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

StudioLifecycleConfig | None

class sagemaker_core.main.resources.SubscribedWorkteam(*, workteam_arn, subscribed_workteam=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource SubscribedWorkteam

Parameters:

• workteam_arn (str)

• subscribed_workteam (SubscribedWorkteam | None)

subscribed_workteam

A Workteam instance that contains information about the work team.

Type:

sagemaker_core.main.shapes.SubscribedWorkteam | None

classmethod get(workteam_arn, session=None, region=None)

Get a SubscribedWorkteam resource

Parameters:

• workteam_arn (str) – The Amazon Resource Name (ARN) of the subscribed work team to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The SubscribedWorkteam resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

SubscribedWorkteam | None

classmethod get_all(name_contains=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all SubscribedWorkteam resources

Parameters:

• name_contains (str | None) – A string in the work team name. This filter returns only work teams whose name contains the specified string.

• next_token – If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

• max_results – The maximum number of work teams to return in each page of the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed SubscribedWorkteam resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[SubscribedWorkteam]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a SubscribedWorkteam resource

Returns:

The SubscribedWorkteam resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

SubscribedWorkteam | None

class sagemaker_core.main.resources.Tag(*, key, value)

Class representing resource Tag

Parameters:

• key (str)

• value (str)

key

The tag key. Tag keys must be unique per resource.

Type:

str

value

The tag value.

Type:

str

classmethod add_tags(resource_arn, tags, session=None, region=None)

Adds or overwrites one or more tags for the specified SageMaker resource.

Parameters:

• resource_arn (str) – The Amazon Resource Name (ARN) of the resource that you want to tag.

• tags (List[Tag]) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod delete_tags(resource_arn, tag_keys, session=None, region=None)

Deletes the specified tags from an SageMaker resource.

Parameters:

• resource_arn (str) – The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

• tag_keys (List[str]) – An array or one or more tag keys to delete.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get_all(resource_arn, session=None, region=None)

Get all Tag resources

Parameters:

• resource_arn (str) – The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

• next_token – If the response to the previous ListTags request is truncated, SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

• max_results – Maximum number of tags to return.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Tag resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Tag]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.resources.TrainingJob(*, training_job_name, training_job_arn=<sagemaker_core.main.utils.Unassigned object>, tuning_job_arn=<sagemaker_core.main.utils.Unassigned object>, labeling_job_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_arn=<sagemaker_core.main.utils.Unassigned object>, model_artifacts=<sagemaker_core.main.utils.Unassigned object>, training_job_status=<sagemaker_core.main.utils.Unassigned object>, secondary_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, hyper_parameters=<sagemaker_core.main.utils.Unassigned object>, algorithm_specification=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, input_data_config=<sagemaker_core.main.utils.Unassigned object>, output_data_config=<sagemaker_core.main.utils.Unassigned object>, resource_config=<sagemaker_core.main.utils.Unassigned object>, warm_pool_status=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, training_start_time=<sagemaker_core.main.utils.Unassigned object>, training_end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, secondary_status_transitions=<sagemaker_core.main.utils.Unassigned object>, final_metric_data_list=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, enable_inter_container_traffic_encryption=<sagemaker_core.main.utils.Unassigned object>, enable_managed_spot_training=<sagemaker_core.main.utils.Unassigned object>, checkpoint_config=<sagemaker_core.main.utils.Unassigned object>, training_time_in_seconds=<sagemaker_core.main.utils.Unassigned object>, billable_time_in_seconds=<sagemaker_core.main.utils.Unassigned object>, debug_hook_config=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>, debug_rule_configurations=<sagemaker_core.main.utils.Unassigned object>, tensor_board_output_config=<sagemaker_core.main.utils.Unassigned object>, debug_rule_evaluation_statuses=<sagemaker_core.main.utils.Unassigned object>, profiler_config=<sagemaker_core.main.utils.Unassigned object>, profiler_rule_configurations=<sagemaker_core.main.utils.Unassigned object>, profiler_rule_evaluation_statuses=<sagemaker_core.main.utils.Unassigned object>, profiling_status=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, retry_strategy=<sagemaker_core.main.utils.Unassigned object>, remote_debug_config=<sagemaker_core.main.utils.Unassigned object>, infra_check_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource TrainingJob

Parameters:

• training_job_name (str)

• training_job_arn (str | None)

• tuning_job_arn (str | None)

• labeling_job_arn (str | None)

• auto_ml_job_arn (str | None)

• model_artifacts (ModelArtifacts | None)

• training_job_status (str | None)

• secondary_status (str | None)

• failure_reason (str | None)

• hyper_parameters (Dict[str, str] | None)

• algorithm_specification (AlgorithmSpecification | None)

• role_arn (str | None)

• input_data_config (List[Channel] | None)

• output_data_config (OutputDataConfig | None)

• resource_config (ResourceConfig | None)

• warm_pool_status (WarmPoolStatus | None)

• vpc_config (VpcConfig | None)

• stopping_condition (StoppingCondition | None)

• creation_time (datetime | None)

• training_start_time (datetime | None)

• training_end_time (datetime | None)

• last_modified_time (datetime | None)

• secondary_status_transitions (List[SecondaryStatusTransition] | None)

• final_metric_data_list (List[MetricData] | None)

• enable_network_isolation (bool | None)

• enable_inter_container_traffic_encryption (bool | None)

• enable_managed_spot_training (bool | None)

• checkpoint_config (CheckpointConfig | None)

• training_time_in_seconds (int | None)

• billable_time_in_seconds (int | None)

• debug_hook_config (DebugHookConfig | None)

• experiment_config (ExperimentConfig | None)

• debug_rule_configurations (List[DebugRuleConfiguration] | None)

• tensor_board_output_config (TensorBoardOutputConfig | None)

• debug_rule_evaluation_statuses (List[DebugRuleEvaluationStatus] | None)

• profiler_config (ProfilerConfig | None)

• profiler_rule_configurations (List[ProfilerRuleConfiguration] | None)

• profiler_rule_evaluation_statuses (List[ProfilerRuleEvaluationStatus] | None)

• profiling_status (str | None)

• environment (Dict[str, str] | None)

• retry_strategy (RetryStrategy | None)

• remote_debug_config (RemoteDebugConfig | None)

• infra_check_config (InfraCheckConfig | None)

training_job_name

Name of the model training job.

Type:

str

training_job_arn

The Amazon Resource Name (ARN) of the training job.

Type:

str | None

model_artifacts

Information about the Amazon S3 location that is configured for storing model artifacts.

Type:

sagemaker_core.main.shapes.ModelArtifacts | None

training_job_status

The status of the training job. SageMaker provides the following training job statuses: InProgress - The training is in progress. Completed - The training job has completed. Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call. Stopping - The training job is stopping. Stopped - The training job has stopped. For more detailed information, see SecondaryStatus.

Type:

str | None

secondary_status

Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition. SageMaker provides primary statuses and secondary statuses that apply to each of them: InProgress Starting - Starting the training job. Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes. Training - Training is in progress. Interrupted - The job stopped because the managed spot training instances were interrupted. Uploading - Training is complete and the model artifacts are being uploaded to the S3 location. Completed Completed - The training job has completed. Failed Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse. Stopped MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime. MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time. Stopped - The training job has stopped. Stopping Stopping - Stopping the training job. Valid values for SecondaryStatus are subject to change. We no longer support the following secondary statuses: LaunchingMLInstances PreparingTraining DownloadingTrainingImage

Type:

str | None

algorithm_specification

Information about the algorithm used for training, and algorithm metadata.

Type:

sagemaker_core.main.shapes.AlgorithmSpecification | None

resource_config

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

Type:

sagemaker_core.main.shapes.ResourceConfig | None

stopping_condition

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

Type:

sagemaker_core.main.shapes.StoppingCondition | None

creation_time

A timestamp that indicates when the training job was created.

Type:

datetime.datetime | None

tuning_job_arn

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

Type:

str | None

labeling_job_arn

The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.

Type:

str | None

auto_ml_job_arn

The Amazon Resource Name (ARN) of an AutoML job.

Type:

str | None

failure_reason

If the training job failed, the reason it failed.

Type:

str | None

hyper_parameters

Algorithm-specific parameters.

Type:

Dict[str, str] | None

role_arn

The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

Type:

str | None

input_data_config

An array of Channel objects that describes each data input channel.

Type:

List[sagemaker_core.main.shapes.Channel] | None

output_data_config

The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.

Type:

sagemaker_core.main.shapes.OutputDataConfig | None

warm_pool_status

The status of the warm pool associated with the training job.

Type:

sagemaker_core.main.shapes.WarmPoolStatus | None

vpc_config

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Type:

sagemaker_core.main.shapes.VpcConfig | None

training_start_time

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

Type:

datetime.datetime | None

training_end_time

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.

Type:

datetime.datetime | None

last_modified_time

A timestamp that indicates when the status of the training job was last modified.

Type:

datetime.datetime | None

secondary_status_transitions

A history of all of the secondary statuses that the training job has transitioned through.

Type:

List[sagemaker_core.main.shapes.SecondaryStatusTransition] | None

final_metric_data_list

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

Type:

List[sagemaker_core.main.shapes.MetricData] | None

enable_network_isolation

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Type:

bool | None

enable_inter_container_traffic_encryption

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.

Type:

bool | None

enable_managed_spot_training

A Boolean indicating whether managed spot training is enabled (True) or not (False).

Type:

bool | None

checkpoint_config

Type:

sagemaker_core.main.shapes.CheckpointConfig | None

training_time_in_seconds

The training time in seconds.

Type:

int | None

billable_time_in_seconds

The billable time in seconds. Billable time refers to the absolute wall-clock time. Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount . You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

Type:

int | None

debug_hook_config

Type:

sagemaker_core.main.shapes.DebugHookConfig | None

experiment_config

Type:

sagemaker_core.main.shapes.ExperimentConfig | None

debug_rule_configurations

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

Type:

List[sagemaker_core.main.shapes.DebugRuleConfiguration] | None

tensor_board_output_config

Type:

sagemaker_core.main.shapes.TensorBoardOutputConfig | None

debug_rule_evaluation_statuses

Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.

Type:

List[sagemaker_core.main.shapes.DebugRuleEvaluationStatus] | None

profiler_config

Type:

sagemaker_core.main.shapes.ProfilerConfig | None

profiler_rule_configurations

Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

Type:

List[sagemaker_core.main.shapes.ProfilerRuleConfiguration] | None

profiler_rule_evaluation_statuses

Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.

Type:

List[sagemaker_core.main.shapes.ProfilerRuleEvaluationStatus] | None

profiling_status

Profiling status of a training job.

Type:

str | None

environment

The environment variables to set in the Docker container.

Type:

Dict[str, str] | None

retry_strategy

The number of times to retry the job when the job fails due to an InternalServerError.

Type:

sagemaker_core.main.shapes.RetryStrategy | None

remote_debug_config

Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

Type:

sagemaker_core.main.shapes.RemoteDebugConfig | None

infra_check_config

Contains information about the infrastructure health check configuration for the training job.

Type:

sagemaker_core.main.shapes.InfraCheckConfig | None

classmethod create(training_job_name, algorithm_specification, role_arn, output_data_config, resource_config, stopping_condition, hyper_parameters=<sagemaker_core.main.utils.Unassigned object>, input_data_config=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, enable_inter_container_traffic_encryption=<sagemaker_core.main.utils.Unassigned object>, enable_managed_spot_training=<sagemaker_core.main.utils.Unassigned object>, checkpoint_config=<sagemaker_core.main.utils.Unassigned object>, debug_hook_config=<sagemaker_core.main.utils.Unassigned object>, debug_rule_configurations=<sagemaker_core.main.utils.Unassigned object>, tensor_board_output_config=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>, profiler_config=<sagemaker_core.main.utils.Unassigned object>, profiler_rule_configurations=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, retry_strategy=<sagemaker_core.main.utils.Unassigned object>, remote_debug_config=<sagemaker_core.main.utils.Unassigned object>, infra_check_config=<sagemaker_core.main.utils.Unassigned object>, session_chaining_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a TrainingJob resource

Parameters:

• training_job_name (str) – The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

• algorithm_specification (AlgorithmSpecification) – The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

• role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles. To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

• output_data_config (OutputDataConfig) – Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

• resource_config (ResourceConfig) – The resources, including the ML compute instances and ML storage volumes, to use for model training. ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

• stopping_condition (StoppingCondition) – Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

• hyper_parameters (Dict[str, str] | None) – Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms. You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint. Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

• input_data_config (List[Channel] | None) – An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location. Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format. Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded. Your input must be in the same Amazon Web Services region as your training job.

• vpc_config (VpcConfig | None) – A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

• tags (List[Tag] | None) – An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• enable_network_isolation (bool | None) – Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

• enable_inter_container_traffic_encryption (bool | None) – To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.

• enable_managed_spot_training (bool | None) – To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run. The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.

• checkpoint_config (CheckpointConfig | None) – Contains information about the output location for managed spot training checkpoint data.

• debug_hook_config (DebugHookConfig | None)

• debug_rule_configurations (List[DebugRuleConfiguration] | None) – Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

• tensor_board_output_config (TensorBoardOutputConfig | None)

• experiment_config (ExperimentConfig | None)

• profiler_config (ProfilerConfig | None)

• profiler_rule_configurations (List[ProfilerRuleConfiguration] | None) – Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

• environment (Dict[str, str] | None) – The environment variables to set in the Docker container.

• retry_strategy (RetryStrategy | None) – The number of times to retry the job when the job fails due to an InternalServerError.

• remote_debug_config (RemoteDebugConfig | None) – Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

• infra_check_config (InfraCheckConfig | None) – Contains information about the infrastructure health check configuration for the training job.

• session_chaining_config (SessionChainingConfig | None) – Contains information about attribute-based access control (ABAC) for the training job.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TrainingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

TrainingJob | None

classmethod get(training_job_name, session=None, region=None)

Get a TrainingJob resource

Parameters:

• training_job_name (str) – The name of the training job.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TrainingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TrainingJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, warm_pool_status_equals=<sagemaker_core.main.utils.Unassigned object>, training_plan_arn_equals=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all TrainingJob resources

Parameters:

• next_token – If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

• max_results – The maximum number of training jobs to return in the response.

• creation_time_after (datetime | None) – A filter that returns only training jobs created after the specified time (timestamp).

• creation_time_before (datetime | None) – A filter that returns only training jobs created before the specified time (timestamp).

• last_modified_time_after (datetime | None) – A filter that returns only training jobs modified after the specified time (timestamp).

• last_modified_time_before (datetime | None) – A filter that returns only training jobs modified before the specified time (timestamp).

• name_contains (str | None) – A string in the training job name. This filter returns only training jobs whose name contains the specified string.

• status_equals (str | None) – A filter that retrieves only training jobs with a specific status.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• warm_pool_status_equals (str | None) – A filter that retrieves only training jobs with a specific warm pool status.

• training_plan_arn_equals (str | None) – The Amazon Resource Name (ARN); of the training plan to filter training jobs by. For more information about reserving GPU capacity for your SageMaker training jobs using Amazon SageMaker Training Plan, see CreateTrainingPlan .

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed TrainingJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[TrainingJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a TrainingJob resource

Returns:

The TrainingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TrainingJob | None

stop()

Stop a TrainingJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

update(profiler_config=<sagemaker_core.main.utils.Unassigned object>, profiler_rule_configurations=<sagemaker_core.main.utils.Unassigned object>, resource_config=<sagemaker_core.main.utils.Unassigned object>, remote_debug_config=<sagemaker_core.main.utils.Unassigned object>)

Update a TrainingJob resource

Returns:

The TrainingJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Parameters:

• profiler_config (ProfilerConfigForUpdate | None)

• profiler_rule_configurations (List[ProfilerRuleConfiguration] | None)

• resource_config (ResourceConfigForUpdate | None)

• remote_debug_config (RemoteDebugConfigForUpdate | None)

Return type:

TrainingJob | None

wait(poll=5, timeout=None, logs=False)

Wait for a TrainingJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

• logs (bool | None) – Whether to print logs while waiting.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.TrainingPlan(*, training_plan_name, training_plan_arn=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, status_message=<sagemaker_core.main.utils.Unassigned object>, duration_hours=<sagemaker_core.main.utils.Unassigned object>, duration_minutes=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, upfront_fee=<sagemaker_core.main.utils.Unassigned object>, currency_code=<sagemaker_core.main.utils.Unassigned object>, total_instance_count=<sagemaker_core.main.utils.Unassigned object>, available_instance_count=<sagemaker_core.main.utils.Unassigned object>, in_use_instance_count=<sagemaker_core.main.utils.Unassigned object>, target_resources=<sagemaker_core.main.utils.Unassigned object>, reserved_capacity_summaries=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource TrainingPlan

Parameters:

• training_plan_name (str)

• training_plan_arn (str | None)

• status (str | None)

• status_message (str | None)

• duration_hours (int | None)

• duration_minutes (int | None)

• start_time (datetime | None)

• end_time (datetime | None)

• upfront_fee (str | None)

• currency_code (str | None)

• total_instance_count (int | None)

• available_instance_count (int | None)

• in_use_instance_count (int | None)

• target_resources (List[str] | None)

• reserved_capacity_summaries (List[ReservedCapacitySummary] | None)

training_plan_arn

The Amazon Resource Name (ARN); of the training plan.

Type:

str | None

training_plan_name

The name of the training plan.

Type:

str

status

The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the TrainingPlanSummary object.

Type:

str | None

status_message

A message providing additional information about the current status of the training plan.

Type:

str | None

duration_hours

The number of whole hours in the total duration for this training plan.

Type:

int | None

duration_minutes

The additional minutes beyond whole hours in the total duration for this training plan.

Type:

int | None

start_time

The start time of the training plan.

Type:

datetime.datetime | None

end_time

The end time of the training plan.

Type:

datetime.datetime | None

upfront_fee

The upfront fee for the training plan.

Type:

str | None

currency_code

The currency code for the upfront fee (e.g., USD).

Type:

str | None

total_instance_count

The total number of instances reserved in this training plan.

Type:

int | None

available_instance_count

The number of instances currently available for use in this training plan.

Type:

int | None

in_use_instance_count

The number of instances currently in use from this training plan.

Type:

int | None

target_resources

The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod) that can use this training plan. Training plans are specific to their target resource. A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs. A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster’s instance group.

Type:

List[str] | None

reserved_capacity_summaries

The list of Reserved Capacity providing the underlying compute resources of the plan.

Type:

List[sagemaker_core.main.shapes.ReservedCapacitySummary] | None

classmethod create(training_plan_name, training_plan_offering_id, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a TrainingPlan resource

Parameters:

• training_plan_name (str) – The name of the training plan to create.

• training_plan_offering_id (str) – The unique identifier of the training plan offering to use for creating this plan.

• tags (List[Tag] | None) – An array of key-value pairs to apply to this training plan.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TrainingPlan resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

TrainingPlan | None

classmethod get(training_plan_name, session=None, region=None)

Get a TrainingPlan resource

Parameters:

• training_plan_name (str) – The name of the training plan to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TrainingPlan resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TrainingPlan | None

classmethod get_all(start_time_after=<sagemaker_core.main.utils.Unassigned object>, start_time_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, filters=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all TrainingPlan resources

Parameters:

• next_token – A token to continue pagination if more results are available.

• max_results – The maximum number of results to return in the response.

• start_time_after (datetime | None) – Filter to list only training plans with an actual start time after this date.

• start_time_before (datetime | None) – Filter to list only training plans with an actual start time before this date.

• sort_by (str | None) – The training plan field to sort the results by (e.g., StartTime, Status).

• sort_order (str | None) – The order to sort the results (Ascending or Descending).

• filters (List[TrainingPlanFilter] | None) – Additional filters to apply to the list of training plans.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed TrainingPlan resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[TrainingPlan]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a TrainingPlan resource

Returns:

The TrainingPlan resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TrainingPlan | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a TrainingPlan resource to reach certain status.

Parameters:

• target_status (Literal['Pending', 'Active', 'Scheduled', 'Expired', 'Failed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.TransformJob(*, transform_job_name, transform_job_arn=<sagemaker_core.main.utils.Unassigned object>, transform_job_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, model_name=<sagemaker_core.main.utils.Unassigned object>, max_concurrent_transforms=<sagemaker_core.main.utils.Unassigned object>, model_client_config=<sagemaker_core.main.utils.Unassigned object>, max_payload_in_mb=<sagemaker_core.main.utils.Unassigned object>, batch_strategy=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, transform_input=<sagemaker_core.main.utils.Unassigned object>, transform_output=<sagemaker_core.main.utils.Unassigned object>, data_capture_config=<sagemaker_core.main.utils.Unassigned object>, transform_resources=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, transform_start_time=<sagemaker_core.main.utils.Unassigned object>, transform_end_time=<sagemaker_core.main.utils.Unassigned object>, labeling_job_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_arn=<sagemaker_core.main.utils.Unassigned object>, data_processing=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource TransformJob

Parameters:

• transform_job_name (str)

• transform_job_arn (str | None)

• transform_job_status (str | None)

• failure_reason (str | None)

• model_name (str | None)

• max_concurrent_transforms (int | None)

• model_client_config (ModelClientConfig | None)

• max_payload_in_mb (int | None)

• batch_strategy (str | None)

• environment (Dict[str, str] | None)

• transform_input (TransformInput | None)

• transform_output (TransformOutput | None)

• data_capture_config (BatchDataCaptureConfig | None)

• transform_resources (TransformResources | None)

• creation_time (datetime | None)

• transform_start_time (datetime | None)

• transform_end_time (datetime | None)

• labeling_job_arn (str | None)

• auto_ml_job_arn (str | None)

• data_processing (DataProcessing | None)

• experiment_config (ExperimentConfig | None)

transform_job_name

The name of the transform job.

Type:

str

transform_job_arn

The Amazon Resource Name (ARN) of the transform job.

Type:

str | None

transform_job_status

The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

Type:

str | None

model_name

The name of the model used in the transform job.

Type:

str | None

transform_input

Describes the dataset to be transformed and the Amazon S3 location where it is stored.

Type:

sagemaker_core.main.shapes.TransformInput | None

transform_resources

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Type:

sagemaker_core.main.shapes.TransformResources | None

creation_time

A timestamp that shows when the transform Job was created.

Type:

datetime.datetime | None

failure_reason

If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.

Type:

str | None

max_concurrent_transforms

The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

Type:

int | None

model_client_config

The timeout and maximum number of retries for processing a transform job invocation.

Type:

sagemaker_core.main.shapes.ModelClientConfig | None

max_payload_in_mb

The maximum payload size, in MB, used in the transform job.

Type:

int | None

batch_strategy

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

Type:

str | None

environment

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

Type:

Dict[str, str] | None

transform_output

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

Type:

sagemaker_core.main.shapes.TransformOutput | None

data_capture_config

Configuration to control how SageMaker captures inference data.

Type:

sagemaker_core.main.shapes.BatchDataCaptureConfig | None

transform_start_time

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

Type:

datetime.datetime | None

transform_end_time

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

Type:

datetime.datetime | None

labeling_job_arn

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

Type:

str | None

auto_ml_job_arn

The Amazon Resource Name (ARN) of the AutoML transform job.

Type:

str | None

data_processing

Type:

sagemaker_core.main.shapes.DataProcessing | None

experiment_config

Type:

sagemaker_core.main.shapes.ExperimentConfig | None

classmethod create(transform_job_name, model_name, transform_input, transform_output, transform_resources, max_concurrent_transforms=<sagemaker_core.main.utils.Unassigned object>, model_client_config=<sagemaker_core.main.utils.Unassigned object>, max_payload_in_mb=<sagemaker_core.main.utils.Unassigned object>, batch_strategy=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, data_capture_config=<sagemaker_core.main.utils.Unassigned object>, data_processing=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a TransformJob resource

Parameters:

• transform_job_name (str) – The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

• model_name (str | object) – The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.

• transform_input (TransformInput) – Describes the input source and the way the transform job consumes it.

• transform_output (TransformOutput) – Describes the results of the transform job.

• transform_resources (TransformResources) – Describes the resources, including ML instance types and ML instance count, to use for the transform job.

• max_concurrent_transforms (int | None) – The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don’t need to set a value for MaxConcurrentTransforms.

• model_client_config (ModelClientConfig | None) – Configures the timeout and maximum number of retries for processing a transform job invocation.

• max_payload_in_mb (int | None) – The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

• batch_strategy (str | None) – Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord. To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line. To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

• environment (Dict[str, str] | None) – The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.

• data_capture_config (BatchDataCaptureConfig | None) – Configuration to control how SageMaker captures inference data.

• data_processing (DataProcessing | None) – The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

• tags (List[Tag] | None) – (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• experiment_config (ExperimentConfig | None)

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TransformJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

TransformJob | None

classmethod get(transform_job_name, session=None, region=None)

Get a TransformJob resource

Parameters:

• transform_job_name (str) – The name of the transform job that you want to view details of.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TransformJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TransformJob | None

classmethod get_all(creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_after=<sagemaker_core.main.utils.Unassigned object>, last_modified_time_before=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, status_equals=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all TransformJob resources

Parameters:

• creation_time_after (datetime | None) – A filter that returns only transform jobs created after the specified time.

• creation_time_before (datetime | None) – A filter that returns only transform jobs created before the specified time.

• last_modified_time_after (datetime | None) – A filter that returns only transform jobs modified after the specified time.

• last_modified_time_before (datetime | None) – A filter that returns only transform jobs modified before the specified time.

• name_contains (str | None) – A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

• status_equals (str | None) – A filter that retrieves only transform jobs with a specific status.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Descending.

• next_token – If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.

• max_results – The maximum number of transform jobs to return in the response. The default value is 10.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed TransformJob resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[TransformJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a TransformJob resource

Returns:

The TransformJob resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TransformJob | None

stop()

Stop a TransformJob resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

wait(poll=5, timeout=None, logs=False)

Wait for a TransformJob resource.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

• logs (bool | None) – Whether to print logs while waiting.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Trial(*, trial_name, trial_arn=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, experiment_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Trial

Parameters:

• trial_name (str)

• trial_arn (str | None)

• display_name (str | None)

• experiment_name (str | None)

• source (TrialSource | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• metadata_properties (MetadataProperties | None)

trial_name

The name of the trial.

Type:

str

trial_arn

The Amazon Resource Name (ARN) of the trial.

Type:

str | None

display_name

The name of the trial as displayed. If DisplayName isn’t specified, TrialName is displayed.

Type:

str | None

experiment_name

The name of the experiment the trial is part of.

Type:

str | None

source

The Amazon Resource Name (ARN) of the source and, optionally, the job type.

Type:

sagemaker_core.main.shapes.TrialSource | None

creation_time

When the trial was created.

Type:

datetime.datetime | None

created_by

Who created the trial.

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

When the trial was last modified.

Type:

datetime.datetime | None

last_modified_by

Who last modified the trial.

Type:

sagemaker_core.main.shapes.UserContext | None

metadata_properties

Type:

sagemaker_core.main.shapes.MetadataProperties | None

classmethod create(trial_name, experiment_name, display_name=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Trial resource

Parameters:

• trial_name (str) – The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.

• experiment_name (str | object) – The name of the experiment to associate the trial with.

• display_name (str | None) – The name of the trial as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, TrialName is displayed.

• metadata_properties (MetadataProperties | None)

• tags (List[Tag] | None) – A list of tags to associate with the trial. You can use Search API to search on the tags.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Trial resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Trial | None

delete()

Delete a Trial resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(trial_name, session=None, region=None)

Get a Trial resource

Parameters:

• trial_name (str) – The name of the trial to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Trial resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Trial | None

classmethod get_all(experiment_name=<sagemaker_core.main.utils.Unassigned object>, trial_component_name=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Trial resources

Parameters:

• experiment_name (str | None) – A filter that returns only trials that are part of the specified experiment.

• trial_component_name (str | None) – A filter that returns only trials that are associated with the specified trial component.

• created_after (datetime | None) – A filter that returns only trials created after the specified time.

• created_before (datetime | None) – A filter that returns only trials created before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• max_results – The maximum number of trials to return in the response. The default value is 10.

• next_token – If the previous call to ListTrials didn’t return the full set of trials, the call returns a token for getting the next set of trials.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Trial resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[Trial]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Trial resource

Returns:

The Trial resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

Trial | None

update(display_name=<sagemaker_core.main.utils.Unassigned object>)

Update a Trial resource

Returns:

The Trial resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Parameters:

display_name (str | None)

Return type:

Trial | None

class sagemaker_core.main.resources.TrialComponent(*, trial_component_name, trial_component_arn=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, parameters=<sagemaker_core.main.utils.Unassigned object>, input_artifacts=<sagemaker_core.main.utils.Unassigned object>, output_artifacts=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, metrics=<sagemaker_core.main.utils.Unassigned object>, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>, sources=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource TrialComponent

Parameters:

• trial_component_name (str)

• trial_component_arn (str | None)

• display_name (str | None)

• source (TrialComponentSource | None)

• status (TrialComponentStatus | None)

• start_time (datetime | None)

• end_time (datetime | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• parameters (Dict[str, TrialComponentParameterValue] | None)

• input_artifacts (Dict[str, TrialComponentArtifact] | None)

• output_artifacts (Dict[str, TrialComponentArtifact] | None)

• metadata_properties (MetadataProperties | None)

• metrics (List[TrialComponentMetricSummary] | None)

• lineage_group_arn (str | None)

• sources (List[TrialComponentSource] | None)

trial_component_name

The name of the trial component.

Type:

str

trial_component_arn

The Amazon Resource Name (ARN) of the trial component.

Type:

str | None

display_name

The name of the component as displayed. If DisplayName isn’t specified, TrialComponentName is displayed.

Type:

str | None

source

The Amazon Resource Name (ARN) of the source and, optionally, the job type.

Type:

sagemaker_core.main.shapes.TrialComponentSource | None

status

The status of the component. States include: InProgress Completed Failed

Type:

sagemaker_core.main.shapes.TrialComponentStatus | None

start_time

When the component started.

Type:

datetime.datetime | None

end_time

When the component ended.

Type:

datetime.datetime | None

creation_time

When the component was created.

Type:

datetime.datetime | None

created_by

Who created the trial component.

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

When the component was last modified.

Type:

datetime.datetime | None

last_modified_by

Who last modified the component.

Type:

sagemaker_core.main.shapes.UserContext | None

parameters

The hyperparameters of the component.

Type:

Dict[str, sagemaker_core.main.shapes.TrialComponentParameterValue] | None

input_artifacts

The input artifacts of the component.

Type:

Dict[str, sagemaker_core.main.shapes.TrialComponentArtifact] | None

output_artifacts

The output artifacts of the component.

Type:

Dict[str, sagemaker_core.main.shapes.TrialComponentArtifact] | None

metadata_properties

Type:

sagemaker_core.main.shapes.MetadataProperties | None

metrics

The metrics for the component.

Type:

List[sagemaker_core.main.shapes.TrialComponentMetricSummary] | None

lineage_group_arn

The Amazon Resource Name (ARN) of the lineage group.

Type:

str | None

sources

A list of ARNs and, if applicable, job types for multiple sources of an experiment run.

Type:

List[sagemaker_core.main.shapes.TrialComponentSource] | None

associate_trail(trial_name, session=None, region=None)

Associates a trial component with a trial.

Parameters:

• trial_name (str) – The name of the trial to associate with.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod batch_get_metrics(metric_queries, session=None, region=None)

Used to retrieve training metrics from SageMaker.

Parameters:

• metric_queries (List[MetricQuery]) – Queries made to retrieve training metrics from SageMaker.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

BatchGetMetricsResponse

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

BatchGetMetricsResponse | None

batch_put_metrics(metric_data, session=None, region=None)

Used to ingest training metrics into SageMaker.

Parameters:

• metric_data (List[RawMetricData]) – A list of raw metric values to put.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod create(trial_component_name, display_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, parameters=<sagemaker_core.main.utils.Unassigned object>, input_artifacts=<sagemaker_core.main.utils.Unassigned object>, output_artifacts=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a TrialComponent resource

Parameters:

• trial_component_name (str) – The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.

• display_name (str | None) – The name of the component as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, TrialComponentName is displayed.

• status (TrialComponentStatus | None) – The status of the component. States include: InProgress Completed Failed

• start_time (datetime | None) – When the component started.

• end_time (datetime | None) – When the component ended.

• parameters (Dict[str, TrialComponentParameterValue] | None) – The hyperparameters for the component.

• input_artifacts (Dict[str, TrialComponentArtifact] | None) – The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.

• output_artifacts (Dict[str, TrialComponentArtifact] | None) – The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.

• metadata_properties (MetadataProperties | None)

• tags (List[Tag] | None) – A list of tags to associate with the component. You can use Search API to search on the tags.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TrialComponent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

TrialComponent | None

delete()

Delete a TrialComponent resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

disassociate_trail(trial_name, session=None, region=None)

Disassociates a trial component from a trial.

Parameters:

• trial_name (str) – The name of the trial to disassociate from.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(trial_component_name, session=None, region=None)

Get a TrialComponent resource

Parameters:

• trial_component_name (str) – The name of the trial component to describe.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The TrialComponent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TrialComponent | None

classmethod get_all(experiment_name=<sagemaker_core.main.utils.Unassigned object>, trial_name=<sagemaker_core.main.utils.Unassigned object>, source_arn=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all TrialComponent resources

Parameters:

• experiment_name (str | None) – A filter that returns only components that are part of the specified experiment. If you specify ExperimentName, you can’t filter by SourceArn or TrialName.

• trial_name (str | None) – A filter that returns only components that are part of the specified trial. If you specify TrialName, you can’t filter by ExperimentName or SourceArn.

• source_arn (str | None) – A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify SourceArn, you can’t filter by ExperimentName or TrialName.

• created_after (datetime | None) – A filter that returns only components created after the specified time.

• created_before (datetime | None) – A filter that returns only components created before the specified time.

• sort_by (str | None) – The property used to sort results. The default value is CreationTime.

• sort_order (str | None) – The sort order. The default value is Descending.

• max_results – The maximum number of components to return in the response. The default value is 10.

• next_token – If the previous call to ListTrialComponents didn’t return the full set of components, the call returns a token for getting the next set of components.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed TrialComponent resources.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[TrialComponent]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a TrialComponent resource

Returns:

The TrialComponent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

TrialComponent | None

update(display_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, parameters=<sagemaker_core.main.utils.Unassigned object>, parameters_to_remove=<sagemaker_core.main.utils.Unassigned object>, input_artifacts=<sagemaker_core.main.utils.Unassigned object>, input_artifacts_to_remove=<sagemaker_core.main.utils.Unassigned object>, output_artifacts=<sagemaker_core.main.utils.Unassigned object>, output_artifacts_to_remove=<sagemaker_core.main.utils.Unassigned object>)

Update a TrialComponent resource

Parameters:

• parameters_to_remove (List[str] | None) – The hyperparameters to remove from the component.

• input_artifacts_to_remove (List[str] | None) – The input artifacts to remove from the component.

• output_artifacts_to_remove (List[str] | None) – The output artifacts to remove from the component.

• display_name (str | None)

• status (TrialComponentStatus | None)

• start_time (datetime | None)

• end_time (datetime | None)

• parameters (Dict[str, TrialComponentParameterValue] | None)

• input_artifacts (Dict[str, TrialComponentArtifact] | None)

• output_artifacts (Dict[str, TrialComponentArtifact] | None)

Returns:

The TrialComponent resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

• ResourceNotFound – Resource being access is not found.

Return type:

TrialComponent | None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a TrialComponent resource to reach certain status.

Parameters:

• target_status (Literal['InProgress', 'Completed', 'Failed', 'Stopping', 'Stopped']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.UserProfile(*, domain_id, user_profile_name, user_profile_arn=<sagemaker_core.main.utils.Unassigned object>, home_efs_file_system_uid=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, single_sign_on_user_identifier=<sagemaker_core.main.utils.Unassigned object>, single_sign_on_user_value=<sagemaker_core.main.utils.Unassigned object>, user_settings=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource UserProfile

Parameters:

• domain_id (str)

• user_profile_name (str)

• user_profile_arn (str | None)

• home_efs_file_system_uid (str | None)

• status (str | None)

• last_modified_time (datetime | None)

• creation_time (datetime | None)

• failure_reason (str | None)

• single_sign_on_user_identifier (str | None)

• single_sign_on_user_value (str | None)

• user_settings (UserSettings | None)

domain_id

The ID of the domain that contains the profile.

Type:

str

user_profile_arn

The user profile Amazon Resource Name (ARN).

Type:

str | None

user_profile_name

The user profile name.

Type:

str

home_efs_file_system_uid

The ID of the user’s profile in the Amazon Elastic File System volume.

Type:

str | None

status

The status.

Type:

str | None

last_modified_time

The last modified time.

Type:

datetime.datetime | None

creation_time

The creation time.

Type:

datetime.datetime | None

failure_reason

The failure reason.

Type:

str | None

single_sign_on_user_identifier

The IAM Identity Center user identifier.

Type:

str | None

single_sign_on_user_value

The IAM Identity Center user value.

Type:

str | None

user_settings

A collection of settings.

Type:

sagemaker_core.main.shapes.UserSettings | None

classmethod create(domain_id, user_profile_name, single_sign_on_user_identifier=<sagemaker_core.main.utils.Unassigned object>, single_sign_on_user_value=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, user_settings=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a UserProfile resource

Parameters:

• domain_id (str) – The ID of the associated Domain.

• user_profile_name (str) – A name for the UserProfile. This value is not case sensitive.

• single_sign_on_user_identifier (str | None) – A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is “UserName”. If the Domain’s AuthMode is IAM Identity Center, this field is required. If the Domain’s AuthMode is not IAM Identity Center, this field cannot be specified.

• single_sign_on_user_value (str | None) – The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain’s AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain’s AuthMode is not IAM Identity Center, this field cannot be specified.

• tags (List[Tag] | None) – Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.

• user_settings (UserSettings | None) – A collection of settings.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The UserProfile resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

UserProfile | None

delete()

Delete a UserProfile resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceNotFound – Resource being access is not found.

Return type:

None

classmethod get(domain_id, user_profile_name, session=None, region=None)

Get a UserProfile resource

Parameters:

• domain_id (str) – The domain ID.

• user_profile_name (str) – The user profile name. This value is not case sensitive.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The UserProfile resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

UserProfile | None

classmethod get_all(sort_order=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, domain_id_equals=<sagemaker_core.main.utils.Unassigned object>, user_profile_name_contains=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all UserProfile resources

Parameters:

• next_token – If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• max_results – This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

• sort_order (str | None) – The sort order for the results. The default is Ascending.

• sort_by (str | None) – The parameter by which to sort the results. The default is CreationTime.

• domain_id_equals (str | None) – A parameter by which to filter the results.

• user_profile_name_contains (str | None) – A parameter by which to filter the results.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed UserProfile resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[UserProfile]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a UserProfile resource

Returns:

The UserProfile resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Return type:

UserProfile | None

update(user_settings=<sagemaker_core.main.utils.Unassigned object>)

Update a UserProfile resource

Returns:

The UserProfile resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ResourceNotFound – Resource being access is not found.

Parameters:

user_settings (UserSettings | None)

Return type:

UserProfile | None

wait_for_delete(poll=5, timeout=None)

Wait for a UserProfile resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a UserProfile resource to reach certain status.

Parameters:

• target_status (Literal['Deleting', 'Failed', 'InService', 'Pending', 'Updating', 'Update_Failed', 'Delete_Failed']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Workforce(*, workforce_name, workforce=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Workforce

Parameters:

• workforce_name (str)

• workforce (Workforce | None)

workforce

A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.

Type:

sagemaker_core.main.shapes.Workforce | None

classmethod create(workforce_name, cognito_config=<sagemaker_core.main.utils.Unassigned object>, oidc_config=<sagemaker_core.main.utils.Unassigned object>, source_ip_config=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, workforce_vpc_config=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Workforce resource

Parameters:

• workforce_name (str) – The name of the private workforce.

• cognito_config (CognitoConfig | None) – Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool. Do not use OidcConfig if you specify values for CognitoConfig.

• oidc_config (OidcConfig | None) – Use this parameter to configure a private workforce using your own OIDC Identity Provider. Do not use CognitoConfig if you specify values for OidcConfig.

• source_ip_config (SourceIpConfig | None)

• tags (List[Tag] | None) – An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.

• workforce_vpc_config (WorkforceVpcConfigRequest | None) – Use this parameter to configure a workforce using VPC.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Workforce resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Workforce | None

delete()

Delete a Workforce resource

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

None

classmethod get(workforce_name, session=None, region=None)

Get a Workforce resource

Parameters:

• workforce_name (str) – The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Workforce resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Workforce | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Workforce resources

Parameters:

• sort_by (str | None) – Sort workforces using the workforce name or creation date.

• sort_order (str | None) – Sort workforces in ascending or descending order.

• name_contains (str | None) – A filter you can use to search for workforces using part of the workforce name.

• next_token – A token to resume pagination.

• max_results – The maximum number of workforces returned in the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Workforce resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Workforce]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Workforce resource

Returns:

The Workforce resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Workforce | None

update(source_ip_config=<sagemaker_core.main.utils.Unassigned object>, oidc_config=<sagemaker_core.main.utils.Unassigned object>, workforce_vpc_config=<sagemaker_core.main.utils.Unassigned object>)

Update a Workforce resource

Parameters:

• source_ip_config (SourceIpConfig | None) – A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce. Maximum: Ten CIDR values

• oidc_config (OidcConfig | None) – Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.

• workforce_vpc_config (WorkforceVpcConfigRequest | None) – Use this parameter to update your VPC configuration for a workforce.

Returns:

The Workforce resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ConflictException – There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Return type:

Workforce | None

wait_for_delete(poll=5, timeout=None)

Wait for a Workforce resource to be deleted.

Parameters:

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• DeleteFailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

wait_for_status(target_status, poll=5, timeout=None)

Wait for a Workforce resource to reach certain status.

Parameters:

• target_status (Literal['Initializing', 'Updating', 'Deleting', 'Failed', 'Active']) – The status to wait for.

• poll (int) – The number of seconds to wait between each poll.

• timeout (int | None) – The maximum number of seconds to wait before timing out.

Raises:

• TimeoutExceededError – If the resource does not reach a terminal state before the timeout.

• FailedStatusError – If the resource reaches a failed state.

• WaiterError – Raised when an error occurs while waiting.

Return type:

None

class sagemaker_core.main.resources.Workteam(*, workteam_name, workteam=<sagemaker_core.main.utils.Unassigned object>)

Class representing resource Workteam

Parameters:

• workteam_name (str)

• workteam (Workteam | None)

workteam

A Workteam instance that contains information about the work team.

Type:

sagemaker_core.main.shapes.Workteam | None

classmethod create(workteam_name, member_definitions, description, workforce_name=<sagemaker_core.main.utils.Unassigned object>, notification_configuration=<sagemaker_core.main.utils.Unassigned object>, worker_access_configuration=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Create a Workteam resource

Parameters:

• workteam_name (str) – The name of the work team. Use this name to identify the work team.

• member_definitions (List[MemberDefinition]) – A list of MemberDefinition objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. Do not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups.

• description (str) – A description of the work team.

• workforce_name (str | object | None) – The name of the workforce.

• notification_configuration (NotificationConfiguration | None) – Configures notification of workers regarding available or expiring work items.

• worker_access_configuration (WorkerAccessConfiguration | None) – Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

• tags (List[Tag] | None) – An array of key-value pairs. For more information, see Resource Tag and Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Workteam resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceInUse – Resource being accessed is in use.

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

• ConfigSchemaValidationError – Raised when a configuration file does not adhere to the schema

• LocalConfigNotFoundError – Raised when a configuration file is not found in local file system

• S3ConfigNotFoundError – Raised when a configuration file is not found in S3

Return type:

Workteam | None

delete()

Delete a Workteam resource

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

None

classmethod get(workteam_name, session=None, region=None)

Get a Workteam resource

Parameters:

• workteam_name (str) – The name of the work team to return a description of.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

The Workteam resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Workteam | None

classmethod get_all(sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, name_contains=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Get all Workteam resources

Parameters:

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• name_contains (str | None) – A string in the work team’s name. This filter returns only work teams whose name contains the specified string.

• next_token – If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

• max_results – The maximum number of work teams to return in each page of the response.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed Workteam resources.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

ResourceIterator[Workteam]

get_all_labeling_jobs(workteam_arn, creation_time_after=<sagemaker_core.main.utils.Unassigned object>, creation_time_before=<sagemaker_core.main.utils.Unassigned object>, job_reference_code_contains=<sagemaker_core.main.utils.Unassigned object>, sort_by=<sagemaker_core.main.utils.Unassigned object>, sort_order=<sagemaker_core.main.utils.Unassigned object>, session=None, region=None)

Gets a list of labeling jobs assigned to a specified work team.

Parameters:

• workteam_arn (str) – The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

• max_results – The maximum number of labeling jobs to return in each page of the response.

• next_token – If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

• creation_time_after (datetime | None) – A filter that returns only labeling jobs created after the specified time (timestamp).

• creation_time_before (datetime | None) – A filter that returns only labeling jobs created before the specified time (timestamp).

• job_reference_code_contains (str | None) – A filter the limits jobs to only the ones whose job reference code contains the specified string.

• sort_by (str | None) – The field to sort results by. The default is CreationTime.

• sort_order (str | None) – The sort order for results. The default is Ascending.

• session (Session | None) – Boto3 session.

• region (str | None) – Region name.

Returns:

Iterator for listed LabelingJob.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceNotFound – Resource being access is not found.

Return type:

ResourceIterator[LabelingJob]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

refresh()

Refresh a Workteam resource

Returns:

The Workteam resource.

Raises:

botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

Return type:

Workteam | None

update(member_definitions=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, notification_configuration=<sagemaker_core.main.utils.Unassigned object>, worker_access_configuration=<sagemaker_core.main.utils.Unassigned object>)

Update a Workteam resource

Parameters:

• member_definitions (List[MemberDefinition] | None) – A list of MemberDefinition objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. You should not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups. Be aware that user groups that are already in the work team must also be listed in Groups when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.

• description (str | None) – An updated description for the work team.

• notification_configuration (NotificationConfiguration | None) – Configures SNS topic notifications for available or expiring work items

• worker_access_configuration (WorkerAccessConfiguration | None) – Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

Returns:

The Workteam resource.

Raises:

• botocore.exceptions.ClientError – This exception is raised for AWS service related errors. The error message and error code can be parsed from the exception as follows: `     try:         # AWS service call here     except botocore.exceptions.ClientError as e:         error_message = e.response['Error']['Message']         error_code = e.response['Error']['Code']     `

• ResourceLimitExceeded – You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Return type:

Workteam | None

SageMaker Core Shapes

class sagemaker_core.main.shapes.AccessForbidden(*, message=<sagemaker_core.main.utils.Unassigned object>)

You do not have permission to perform an action.

Parameters:

message (str | None)

message

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ActionSource(*, source_uri, source_type=<sagemaker_core.main.utils.Unassigned object>, source_id=<sagemaker_core.main.utils.Unassigned object>)

A structure describing the source of an action.

Parameters:

• source_uri (str)

• source_type (str | None)

• source_id (str | None)

source_uri

Type:

The URI of the source.

source_type

Type:

The type of the source.

source_id

Type:

The ID of the source.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ActionSummary(*, action_arn=<sagemaker_core.main.utils.Unassigned object>, action_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, action_type=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Lists the properties of an action. An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.

Parameters:

• action_arn (str | None)

• action_name (str | object | None)

• source (ActionSource | None)

• action_type (str | None)

• status (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

action_arn

Type:

The Amazon Resource Name (ARN) of the action.

action_name

Type:

The name of the action.

source

Type:

The source of the action.

action_type

Type:

The type of the action.

status

Type:

The status of the action.

creation_time

Type:

When the action was created.

last_modified_time

Type:

When the action was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AdditionalInferenceSpecificationDefinition(*, name, containers, description=<sagemaker_core.main.utils.Unassigned object>, supported_transform_instance_types=<sagemaker_core.main.utils.Unassigned object>, supported_realtime_inference_instance_types=<sagemaker_core.main.utils.Unassigned object>, supported_content_types=<sagemaker_core.main.utils.Unassigned object>, supported_response_mime_types=<sagemaker_core.main.utils.Unassigned object>)

A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package

Parameters:

• name (str)

• containers (List[ModelPackageContainerDefinition])

• description (str | None)

• supported_transform_instance_types (List[str] | None)

• supported_realtime_inference_instance_types (List[str] | None)

• supported_content_types (List[str] | None)

• supported_response_mime_types (List[str] | None)

name

Type:

A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.

description

Type:

A description of the additional Inference specification

containers

Type:

The Amazon ECR registry path of the Docker image that contains the inference code.

supported_transform_instance_types

Type:

A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

supported_realtime_inference_instance_types

Type:

A list of the instance types that are used to generate inferences in real-time.

supported_content_types

Type:

The supported MIME types for the input data.

supported_response_mime_types

Type:

The supported MIME types for the output data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AdditionalModelDataSource(*, channel_name, s3_data_source)

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

Parameters:

• channel_name (str)

• s3_data_source (S3ModelDataSource)

channel_name

Type:

A custom name for this AdditionalModelDataSource object.

s3_data_source

Type:

sagemaker_core.main.shapes.S3ModelDataSource

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AdditionalS3DataSource(*, s3_data_type, s3_uri, compression_type=<sagemaker_core.main.utils.Unassigned object>, e_tag=<sagemaker_core.main.utils.Unassigned object>)

A data source used for training or inference that is in addition to the input dataset or model data.

Parameters:

• s3_data_type (str)

• s3_uri (str)

• compression_type (str | None)

• e_tag (str | None)

s3_data_type

Type:

The data type of the additional data source that you specify for use in inference or training.

s3_uri

Type:

The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

compression_type

Type:

The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

e_tag

Type:

The ETag associated with S3 URI.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AgentVersion(*, version, agent_count)

Edge Manager agent version.

Parameters:

• version (str)

• agent_count (int)

version

Type:

Version of the agent.

agent_count

Type:

The number of Edge Manager agents.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Alarm(*, alarm_name=<sagemaker_core.main.utils.Unassigned object>)

An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

Parameters:

alarm_name (str | None)

alarm_name

Type:

The name of a CloudWatch alarm in your account.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AlgorithmSpecification(*, training_input_mode, training_image=<sagemaker_core.main.utils.Unassigned object>, algorithm_name=<sagemaker_core.main.utils.Unassigned object>, metric_definitions=<sagemaker_core.main.utils.Unassigned object>, enable_sage_maker_metrics_time_series=<sagemaker_core.main.utils.Unassigned object>, container_entrypoint=<sagemaker_core.main.utils.Unassigned object>, container_arguments=<sagemaker_core.main.utils.Unassigned object>, training_image_config=<sagemaker_core.main.utils.Unassigned object>)

Specifies the training algorithm to use in a CreateTrainingJob request. SageMaker uses its own SageMaker account credentials to pull and access built-in algorithms so built-in algorithms are universally accessible across all Amazon Web Services accounts. As a result, built-in algorithms have standard, unrestricted access. You cannot restrict built-in algorithms using IAM roles. Use custom algorithms if you require specific access controls. For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Parameters:

• training_input_mode (str)

• training_image (str | None)

• algorithm_name (str | object | None)

• metric_definitions (List[MetricDefinition] | None)

• enable_sage_maker_metrics_time_series (bool | None)

• container_entrypoint (List[str] | None)

• container_arguments (List[str] | None)

• training_image_config (TrainingImageConfig | None)

training_image

Type:

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker. You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. For more information, see the note in the AlgorithmName parameter description.

algorithm_name

Type:

The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can’t specify a value for TrainingImage, and vice versa. If you specify values for both parameters, the training job might break; if you don’t specify any value for both parameters, the training job might raise a null error.

training_input_mode

Type:

str

metric_definitions

Type:

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.

enable_sage_maker_metrics_time_series

Type:

To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren’t generated except in the following cases: You use one of the SageMaker built-in algorithms You use one of the following Prebuilt SageMaker Docker Images: Tensorflow (version &gt;= 1.15) MXNet (version &gt;= 1.6) PyTorch (version &gt;= 1.3) You specify at least one MetricDefinition

container_entrypoint

Type:

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

container_arguments

Type:

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

training_image_config

Type:

The configuration to use an image from a private Docker registry for a training job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AlgorithmStatusDetails(*, validation_statuses=<sagemaker_core.main.utils.Unassigned object>, image_scan_statuses=<sagemaker_core.main.utils.Unassigned object>)

Specifies the validation and image scan statuses of the algorithm.

Parameters:

• validation_statuses (List[AlgorithmStatusItem] | None)

• image_scan_statuses (List[AlgorithmStatusItem] | None)

validation_statuses

Type:

The status of algorithm validation.

image_scan_statuses

Type:

The status of the scan of the algorithm’s Docker image container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AlgorithmStatusItem(*, name, status, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

Represents the overall status of an algorithm.

Parameters:

• name (str)

• status (str)

• failure_reason (str | None)

name

Type:

The name of the algorithm for which the overall status is being reported.

status

Type:

The current status.

failure_reason

Type:

if the overall status is Failed, the reason for the failure.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AlgorithmSummary(*, algorithm_name, algorithm_arn, creation_time, algorithm_status, algorithm_description=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information about an algorithm.

Parameters:

• algorithm_name (str | object)

• algorithm_arn (str)

• creation_time (datetime)

• algorithm_status (str)

• algorithm_description (str | None)

algorithm_name

Type:

The name of the algorithm that is described by the summary.

algorithm_arn

Type:

The Amazon Resource Name (ARN) of the algorithm.

algorithm_description

Type:

A brief description of the algorithm.

creation_time

Type:

A timestamp that shows when the algorithm was created.

algorithm_status

Type:

The overall status of the algorithm.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AlgorithmValidationProfile(*, profile_name, training_job_definition, transform_job_definition=<sagemaker_core.main.utils.Unassigned object>)

Defines a training job and a batch transform job that SageMaker runs to validate your algorithm. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

Parameters:

• profile_name (str)

• training_job_definition (TrainingJobDefinition)

• transform_job_definition (TransformJobDefinition | None)

profile_name

Type:

The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

training_job_definition

Type:

The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.

transform_job_definition

Type:

The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AlgorithmValidationSpecification(*, validation_role, validation_profiles)

Specifies configurations for one or more training jobs that SageMaker runs to test the algorithm.

Parameters:

• validation_role (str)

• validation_profiles (List[AlgorithmValidationProfile])

validation_role

Type:

The IAM roles that SageMaker uses to run the training jobs.

validation_profiles

Type:

An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AmazonQSettings(*, status=<sagemaker_core.main.utils.Unassigned object>, q_profile_arn=<sagemaker_core.main.utils.Unassigned object>)

A collection of settings that configure the Amazon Q experience within the domain.

Parameters:

• status (str | None)

• q_profile_arn (str | None)

status

Type:

Whether Amazon Q has been enabled within the domain.

q_profile_arn

Type:

The ARN of the Amazon Q profile used within the domain.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AnnotationConsolidationConfig(*, annotation_consolidation_lambda_arn)

Configures how labels are consolidated across human workers and processes output data.

Parameters:

annotation_consolidation_lambda_arn (str)

annotation_consolidation_lambda_arn

Type:

The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn. For custom labeling workflows, see Post-annotation Lambda. Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes. arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as “votes” for the correct label. arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label. arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video. arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking 3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection 3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking 3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation Use the following ARNs for Label Verification and Adjustment Jobs Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels . Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as “votes” for the correct label. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking 3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud. arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection 3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames. arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking 3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool. arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AppDetails(*, domain_id=<sagemaker_core.main.utils.Unassigned object>, user_profile_name=<sagemaker_core.main.utils.Unassigned object>, space_name=<sagemaker_core.main.utils.Unassigned object>, app_type=<sagemaker_core.main.utils.Unassigned object>, app_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, resource_spec=<sagemaker_core.main.utils.Unassigned object>)

Details about an Amazon SageMaker AI app.

Parameters:

• domain_id (str | None)

• user_profile_name (str | object | None)

• space_name (str | object | None)

• app_type (str | None)

• app_name (str | object | None)

• status (str | None)

• creation_time (datetime | None)

• resource_spec (ResourceSpec | None)

domain_id

Type:

The domain ID.

user_profile_name

Type:

The user profile name.

space_name

Type:

The name of the space.

app_type

Type:

The type of app.

app_name

Type:

The name of the app.

status

Type:

The status.

creation_time

Type:

The creation time.

resource_spec

Type:

sagemaker_core.main.shapes.ResourceSpec | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AppImageConfigDetails(*, app_image_config_arn=<sagemaker_core.main.utils.Unassigned object>, app_image_config_name=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, kernel_gateway_image_config=<sagemaker_core.main.utils.Unassigned object>, jupyter_lab_app_image_config=<sagemaker_core.main.utils.Unassigned object>, code_editor_app_image_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration for running a SageMaker AI image as a KernelGateway app.

Parameters:

• app_image_config_arn (str | None)

• app_image_config_name (str | object | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• kernel_gateway_image_config (KernelGatewayImageConfig | None)

• jupyter_lab_app_image_config (JupyterLabAppImageConfig | None)

• code_editor_app_image_config (CodeEditorAppImageConfig | None)

app_image_config_arn

Type:

The ARN of the AppImageConfig.

app_image_config_name

Type:

The name of the AppImageConfig. Must be unique to your account.

creation_time

Type:

When the AppImageConfig was created.

last_modified_time

Type:

When the AppImageConfig was last modified.

kernel_gateway_image_config

Type:

The configuration for the file system and kernels in the SageMaker AI image.

jupyter_lab_app_image_config

Type:

The configuration for the file system and the runtime, such as the environment variables and entry point.

code_editor_app_image_config

Type:

The configuration for the file system and the runtime, such as the environment variables and entry point.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AppLifecycleManagement(*, idle_settings=<sagemaker_core.main.utils.Unassigned object>)

Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications.

Parameters:

idle_settings (IdleSettings | None)

idle_settings

Type:

Settings related to idle shutdown of Studio applications.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AppSpecification(*, image_uri, container_entrypoint=<sagemaker_core.main.utils.Unassigned object>, container_arguments=<sagemaker_core.main.utils.Unassigned object>)

Configuration to run a processing job in a specified container image.

Parameters:

• image_uri (str)

• container_entrypoint (List[str] | None)

• container_arguments (List[str] | None)

image_uri

Type:

The container image to be run by the processing job.

container_entrypoint

Type:

The entrypoint for a container used to run a processing job.

container_arguments

Type:

The arguments for a container used to run a processing job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ArtifactSource(*, source_uri, source_types=<sagemaker_core.main.utils.Unassigned object>)

A structure describing the source of an artifact.

Parameters:

• source_uri (str)

• source_types (List[ArtifactSourceType] | None)

source_uri

Type:

The URI of the source.

source_types

Type:

A list of source types.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ArtifactSourceType(*, source_id_type, value)

The ID and ID type of an artifact source.

Parameters:

• source_id_type (str)

• value (str)

source_id_type

Type:

The type of ID.

value

Type:

The ID.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ArtifactSummary(*, artifact_arn=<sagemaker_core.main.utils.Unassigned object>, artifact_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, artifact_type=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.

Parameters:

• artifact_arn (str | None)

• artifact_name (str | object | None)

• source (ArtifactSource | None)

• artifact_type (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

artifact_arn

Type:

The Amazon Resource Name (ARN) of the artifact.

artifact_name

Type:

The name of the artifact.

source

Type:

The source of the artifact.

artifact_type

Type:

The type of the artifact.

creation_time

Type:

When the artifact was created.

last_modified_time

Type:

When the artifact was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AssociationSummary(*, source_arn=<sagemaker_core.main.utils.Unassigned object>, destination_arn=<sagemaker_core.main.utils.Unassigned object>, source_type=<sagemaker_core.main.utils.Unassigned object>, destination_type=<sagemaker_core.main.utils.Unassigned object>, association_type=<sagemaker_core.main.utils.Unassigned object>, source_name=<sagemaker_core.main.utils.Unassigned object>, destination_name=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>)

Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.

Parameters:

• source_arn (str | None)

• destination_arn (str | None)

• source_type (str | None)

• destination_type (str | None)

• association_type (str | None)

• source_name (str | None)

• destination_name (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

source_arn

Type:

The ARN of the source.

destination_arn

Type:

The Amazon Resource Name (ARN) of the destination.

source_type

Type:

The source type.

destination_type

Type:

The destination type.

association_type

Type:

The type of the association.

source_name

Type:

The name of the source.

destination_name

Type:

The name of the destination.

creation_time

Type:

When the association was created.

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AsyncInferenceClientConfig(*, max_concurrent_invocations_per_instance=<sagemaker_core.main.utils.Unassigned object>)

Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

Parameters:

max_concurrent_invocations_per_instance (int | None)

max_concurrent_invocations_per_instance

Type:

The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AsyncInferenceConfig(*, output_config, client_config=<sagemaker_core.main.utils.Unassigned object>)

Specifies configuration for how an endpoint performs asynchronous inference.

Parameters:

• output_config (AsyncInferenceOutputConfig)

• client_config (AsyncInferenceClientConfig | None)

client_config

Type:

Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

output_config

Type:

Specifies the configuration for asynchronous inference invocation outputs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AsyncInferenceNotificationConfig(*, success_topic=<sagemaker_core.main.utils.Unassigned object>, error_topic=<sagemaker_core.main.utils.Unassigned object>, include_inference_response_in=<sagemaker_core.main.utils.Unassigned object>)

Specifies the configuration for notifications of inference results for asynchronous inference.

Parameters:

• success_topic (str | None)

• error_topic (str | None)

• include_inference_response_in (List[str] | None)

success_topic

Type:

Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

error_topic

Type:

Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

include_inference_response_in

Type:

The Amazon SNS topics where you want the inference response to be included. The inference response is included only if the response size is less than or equal to 128 KB.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AsyncInferenceOutputConfig(*, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, s3_output_path=<sagemaker_core.main.utils.Unassigned object>, notification_config=<sagemaker_core.main.utils.Unassigned object>, s3_failure_path=<sagemaker_core.main.utils.Unassigned object>)

Specifies the configuration for asynchronous inference invocation outputs.

Parameters:

• kms_key_id (str | None)

• s3_output_path (str | None)

• notification_config (AsyncInferenceNotificationConfig | None)

• s3_failure_path (str | None)

kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

s3_output_path

Type:

The Amazon S3 location to upload inference responses to.

notification_config

Type:

Specifies the configuration for notifications of inference results for asynchronous inference.

s3_failure_path

Type:

The Amazon S3 location to upload failure inference responses to.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AthenaDatasetDefinition(*, catalog, database, query_string, output_s3_uri, output_format, work_group=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, output_compression=<sagemaker_core.main.utils.Unassigned object>)

Configuration for Athena Dataset Definition input.

Parameters:

• catalog (str)

• database (str)

• query_string (str)

• output_s3_uri (str)

• output_format (str)

• work_group (str | None)

• kms_key_id (str | None)

• output_compression (str | None)

catalog

Type:

str

database

Type:

str

query_string

Type:

str

work_group

Type:

str | None

output_s3_uri

Type:

The location in Amazon S3 where Athena query results are stored.

kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.

output_format

Type:

str

output_compression

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLAlgorithmConfig(*, auto_ml_algorithms)

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

Parameters:

auto_ml_algorithms (List[str])

auto_ml_algorithms

Type:

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job. For the tabular problem type TabularJobConfig: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm. In ENSEMBLING mode: “catboost” “extra-trees” “fastai” “lightgbm” “linear-learner” “nn-torch” “randomforest” “xgboost” In HYPERPARAMETER_TUNING mode: “linear-learner” “mlp” “xgboost” For the time-series forecasting problem type TimeSeriesForecastingJobConfig: Choose your algorithms from this list. “cnn-qr” “deepar” “prophet” “arima” “npts” “ets”

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLCandidate(*, candidate_name, objective_status, candidate_steps, candidate_status, creation_time, last_modified_time, final_auto_ml_job_objective_metric=<sagemaker_core.main.utils.Unassigned object>, inference_containers=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, candidate_properties=<sagemaker_core.main.utils.Unassigned object>, inference_container_definitions=<sagemaker_core.main.utils.Unassigned object>)

Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.

Parameters:

• candidate_name (str)

• objective_status (str)

• candidate_steps (List[AutoMLCandidateStep])

• candidate_status (str)

• creation_time (datetime)

• last_modified_time (datetime)

• final_auto_ml_job_objective_metric (FinalAutoMLJobObjectiveMetric | None)

• inference_containers (List[AutoMLContainerDefinition] | None)

• end_time (datetime | None)

• failure_reason (str | None)

• candidate_properties (CandidateProperties | None)

• inference_container_definitions (Dict[str, List[AutoMLContainerDefinition]] | None)

candidate_name

Type:

The name of the candidate.

final_auto_ml_job_objective_metric

Type:

sagemaker_core.main.shapes.FinalAutoMLJobObjectiveMetric | None

objective_status

Type:

The objective’s status.

candidate_steps

Type:

Information about the candidate’s steps.

candidate_status

Type:

The candidate’s status.

inference_containers

Type:

Information about the recommended inference container definitions.

creation_time

Type:

The creation time.

end_time

Type:

The end time.

last_modified_time

Type:

The last modified time.

failure_reason

Type:

The failure reason.

candidate_properties

Type:

The properties of an AutoML candidate job.

inference_container_definitions

Type:

The mapping of all supported processing unit (CPU, GPU, etc…) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2) related to image or text classification problem types only.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLCandidateGenerationConfig(*, feature_specification_s3_uri=<sagemaker_core.main.utils.Unassigned object>, algorithms_config=<sagemaker_core.main.utils.Unassigned object>)

Stores the configuration information for how a candidate is generated (optional).

Parameters:

• feature_specification_s3_uri (str | None)

• algorithms_config (List[AutoMLAlgorithmConfig] | None)

feature_specification_s3_uri

Type:

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below: { “FeatureAttributeNames”:[“col1”, “col2”, …] }. You can also specify the data type of the feature (optional) in the format shown below: { “FeatureDataTypes”:{“col1”:”numeric”, “col2”:”categorical” … } } These column keys may not include the target column. In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence. If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data. If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames. The key name FeatureAttributeNames is fixed. The values listed in [“col1”, “col2”, …] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

algorithms_config

Type:

Stores the configuration information for the selection of algorithms trained on tabular data. The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode . AlgorithmsConfig should not be set if the training mode is set on AUTO. When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode. When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode. For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig. For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLCandidateStep(*, candidate_step_type, candidate_step_arn, candidate_step_name)

Information about the steps for a candidate and what step it is working on.

Parameters:

• candidate_step_type (str)

• candidate_step_arn (str)

• candidate_step_name (str)

candidate_step_type

Type:

Whether the candidate is at the transform, training, or processing step.

candidate_step_arn

Type:

The ARN for the candidate’s step.

candidate_step_name

Type:

The name for the candidate’s step.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLChannel(*, target_attribute_name, data_source=<sagemaker_core.main.utils.Unassigned object>, compression_type=<sagemaker_core.main.utils.Unassigned object>, content_type=<sagemaker_core.main.utils.Unassigned object>, channel_type=<sagemaker_core.main.utils.Unassigned object>, sample_weight_attribute_name=<sagemaker_core.main.utils.Unassigned object>)

A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel. A validation dataset must contain the same headers as the training dataset.

Parameters:

• target_attribute_name (str)

• data_source (AutoMLDataSource | None)

• compression_type (str | None)

• content_type (str | None)

• channel_type (str | None)

• sample_weight_attribute_name (str | None)

data_source

Type:

The data source for an AutoML channel.

compression_type

Type:

You can use Gzip or None. The default value is None.

target_attribute_name

Type:

The name of the target variable in supervised learning, usually represented by ‘y’.

content_type

Type:

The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

channel_type

Type:

The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.

sample_weight_attribute_name

Type:

If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation. Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded. Support for sample weights is available in Ensembling mode only.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLComputeConfig(*, emr_serverless_compute_config=<sagemaker_core.main.utils.Unassigned object>)

This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment. Specifies the compute configuration for an AutoML job V2.

Parameters:

emr_serverless_compute_config (EmrServerlessComputeConfig | None)

emr_serverless_compute_config

Type:

The configuration for using EMR Serverless to run the AutoML job V2. To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request. By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you. EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLContainerDefinition(*, image, model_data_url, environment=<sagemaker_core.main.utils.Unassigned object>)

A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.

Parameters:

• image (str)

• model_data_url (str)

• environment (Dict[str, str] | None)

image

Type:

The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.

model_data_url

Type:

The location of the model artifacts. For more information, see ContainerDefinition.

environment

Type:

The environment variables to set in the container. For more information, see ContainerDefinition.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLDataSource(*, s3_data_source)

The data source for the Autopilot job.

Parameters:

s3_data_source (AutoMLS3DataSource)

s3_data_source

Type:

The Amazon S3 location of the input data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLDataSplitConfig(*, validation_fraction=<sagemaker_core.main.utils.Unassigned object>)

This structure specifies how to split the data into train and validation datasets. The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

Parameters:

validation_fraction (float | None)

validation_fraction

Type:

The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobArtifacts(*, candidate_definition_notebook_location=<sagemaker_core.main.utils.Unassigned object>, data_exploration_notebook_location=<sagemaker_core.main.utils.Unassigned object>)

The artifacts that are generated during an AutoML job.

Parameters:

• candidate_definition_notebook_location (str | None)

• data_exploration_notebook_location (str | None)

candidate_definition_notebook_location

Type:

The URL of the notebook location.

data_exploration_notebook_location

Type:

The URL of the notebook location.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobChannel(*, channel_type=<sagemaker_core.main.utils.Unassigned object>, content_type=<sagemaker_core.main.utils.Unassigned object>, compression_type=<sagemaker_core.main.utils.Unassigned object>, data_source=<sagemaker_core.main.utils.Unassigned object>)

A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2).

Parameters:

• channel_type (str | None)

• content_type (str | None)

• compression_type (str | None)

• data_source (AutoMLDataSource | None)

channel_type

Type:

The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType The type of channel defaults to training for the time-series forecasting problem type.

content_type

Type:

The content type of the data from the input source. The following are the allowed content types for different problems: For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present. For image classification: image/png, image/jpeg, or image/. The default value is image/. For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present. For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present. For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

compression_type

Type:

The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

data_source

Type:

The data source for an AutoML channel (Required).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobCompletionCriteria(*, max_candidates=<sagemaker_core.main.utils.Unassigned object>, max_runtime_per_training_job_in_seconds=<sagemaker_core.main.utils.Unassigned object>, max_auto_ml_job_runtime_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

How long a job is allowed to run, or how many candidates a job is allowed to generate.

Parameters:

• max_candidates (int | None)

• max_runtime_per_training_job_in_seconds (int | None)

• max_auto_ml_job_runtime_in_seconds (int | None)

max_candidates

Type:

The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

max_runtime_per_training_job_in_seconds

Type:

The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action. For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate. For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

max_auto_ml_job_runtime_in_seconds

Type:

The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobConfig(*, completion_criteria=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, candidate_generation_config=<sagemaker_core.main.utils.Unassigned object>, data_split_config=<sagemaker_core.main.utils.Unassigned object>, mode=<sagemaker_core.main.utils.Unassigned object>)

A collection of settings used for an AutoML job.

Parameters:

• completion_criteria (AutoMLJobCompletionCriteria | None)

• security_config (AutoMLSecurityConfig | None)

• candidate_generation_config (AutoMLCandidateGenerationConfig | None)

• data_split_config (AutoMLDataSplitConfig | None)

• mode (str | None)

completion_criteria

Type:

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

security_config

Type:

The security configuration for traffic encryption or Amazon VPC settings.

candidate_generation_config

Type:

The configuration for generating a candidate for an AutoML job (optional).

data_split_config

Type:

The configuration for splitting the input training dataset. Type: AutoMLDataSplitConfig

mode

Type:

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones. The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode. The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobObjective(*, metric_name)

Specifies a metric to minimize or maximize as the objective of an AutoML job.

Parameters:

metric_name (str)

metric_name

Type:

The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model’s parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type. For tabular problem types: List of available metrics: Regression: MAE, MSE, R2, RMSE Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro For a description of each metric, see Autopilot metrics for classification and regression. Default objective metrics: Regression: MSE. Binary classification: F1. Multiclass classification: Accuracy. For image or text classification problem types: List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification. Default objective metrics: Accuracy For time-series forecasting problem types: List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE For a description of each metric, see Autopilot metrics for time-series forecasting. Default objective metrics: AverageWeightedQuantileLoss For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobStepMetadata(*, arn=<sagemaker_core.main.utils.Unassigned object>)

Metadata for an AutoML job step.

Parameters:

arn (str | None)

arn

Type:

The Amazon Resource Name (ARN) of the AutoML job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLJobSummary(*, auto_ml_job_name, auto_ml_job_arn, auto_ml_job_status, auto_ml_job_secondary_status, creation_time, last_modified_time, end_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, partial_failure_reasons=<sagemaker_core.main.utils.Unassigned object>)

Provides a summary about an AutoML job.

Parameters:

• auto_ml_job_name (str)

• auto_ml_job_arn (str)

• auto_ml_job_status (str)

• auto_ml_job_secondary_status (str)

• creation_time (datetime)

• last_modified_time (datetime)

• end_time (datetime | None)

• failure_reason (str | None)

• partial_failure_reasons (List[AutoMLPartialFailureReason] | None)

auto_ml_job_name

Type:

The name of the AutoML job you are requesting.

auto_ml_job_arn

Type:

The ARN of the AutoML job.

auto_ml_job_status

Type:

The status of the AutoML job.

auto_ml_job_secondary_status

Type:

The secondary status of the AutoML job.

creation_time

Type:

When the AutoML job was created.

end_time

Type:

The end time of an AutoML job.

last_modified_time

Type:

When the AutoML job was last modified.

failure_reason

Type:

The failure reason of an AutoML job.

partial_failure_reasons

Type:

The list of reasons for partial failures within an AutoML job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLOutputDataConfig(*, s3_output_path, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

The output data configuration.

Parameters:

• s3_output_path (str)

• kms_key_id (str | None)

kms_key_id

Type:

The Key Management Service encryption key ID.

s3_output_path

Type:

The Amazon S3 output path. Must be 512 characters or less.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLPartialFailureReason(*, partial_failure_message=<sagemaker_core.main.utils.Unassigned object>)

The reason for a partial failure of an AutoML job.

Parameters:

partial_failure_message (str | None)

partial_failure_message

Type:

The message containing the reason for a partial failure of an AutoML job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLProblemTypeConfig(*, image_classification_job_config=<sagemaker_core.main.utils.Unassigned object>, text_classification_job_config=<sagemaker_core.main.utils.Unassigned object>, time_series_forecasting_job_config=<sagemaker_core.main.utils.Unassigned object>, tabular_job_config=<sagemaker_core.main.utils.Unassigned object>, text_generation_job_config=<sagemaker_core.main.utils.Unassigned object>)

A collection of settings specific to the problem type used to configure an AutoML job V2. There must be one and only one config of the following type.

Parameters:

• image_classification_job_config (ImageClassificationJobConfig | None)

• text_classification_job_config (TextClassificationJobConfig | None)

• time_series_forecasting_job_config (TimeSeriesForecastingJobConfig | None)

• tabular_job_config (TabularJobConfig | None)

• text_generation_job_config (TextGenerationJobConfig | None)

image_classification_job_config

Type:

Settings used to configure an AutoML job V2 for the image classification problem type.

text_classification_job_config

Type:

Settings used to configure an AutoML job V2 for the text classification problem type.

time_series_forecasting_job_config

Type:

Settings used to configure an AutoML job V2 for the time-series forecasting problem type.

tabular_job_config

Type:

Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).

text_generation_job_config

Type:

Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type. The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLProblemTypeResolvedAttributes(*, tabular_resolved_attributes=<sagemaker_core.main.utils.Unassigned object>, text_generation_resolved_attributes=<sagemaker_core.main.utils.Unassigned object>)

Stores resolved attributes specific to the problem type of an AutoML job V2.

Parameters:

• tabular_resolved_attributes (TabularResolvedAttributes | None)

• text_generation_resolved_attributes (TextGenerationResolvedAttributes | None)

tabular_resolved_attributes

Type:

The resolved attributes for the tabular problem type.

text_generation_resolved_attributes

Type:

The resolved attributes for the text generation problem type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLResolvedAttributes(*, auto_ml_job_objective=<sagemaker_core.main.utils.Unassigned object>, completion_criteria=<sagemaker_core.main.utils.Unassigned object>, auto_ml_problem_type_resolved_attributes=<sagemaker_core.main.utils.Unassigned object>)

The resolved attributes used to configure an AutoML job V2.

Parameters:

• auto_ml_job_objective (AutoMLJobObjective | None)

• completion_criteria (AutoMLJobCompletionCriteria | None)

• auto_ml_problem_type_resolved_attributes (AutoMLProblemTypeResolvedAttributes | None)

auto_ml_job_objective

Type:

sagemaker_core.main.shapes.AutoMLJobObjective | None

completion_criteria

Type:

sagemaker_core.main.shapes.AutoMLJobCompletionCriteria | None

auto_ml_problem_type_resolved_attributes

Type:

Defines the resolved attributes specific to a problem type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLS3DataSource(*, s3_data_type, s3_uri)

Describes the Amazon S3 data source.

Parameters:

• s3_data_type (str)

• s3_uri (str)

s3_data_type

Type:

The data type. If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker AI uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker AI to use for model training. A ManifestFile should have the format shown below: [ {“prefix”: “s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/”}, “DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1”, “DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2”, … “DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N” ] If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2). Here is a minimal, single-record example of an AugmentedManifestFile: {“source-ref”: “s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg”, “label-metadata”: {“class-name”: “cat” } For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

s3_uri

Type:

The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoMLSecurityConfig(*, volume_kms_key_id=<sagemaker_core.main.utils.Unassigned object>, enable_inter_container_traffic_encryption=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>)

Security options.

Parameters:

• volume_kms_key_id (str | None)

• enable_inter_container_traffic_encryption (bool | None)

• vpc_config (VpcConfig | None)

volume_kms_key_id

Type:

The key used to encrypt stored data.

enable_inter_container_traffic_encryption

Type:

Whether to use traffic encryption between the container layers.

vpc_config

Type:

The VPC configuration.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoParameter(*, name, value_hint)

The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.

Parameters:

• name (str)

• value_hint (str)

name

Type:

The name of the hyperparameter to optimize using Autotune.

value_hint

Type:

An example value of the hyperparameter to optimize using Autotune.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.AutoRollbackConfig(*, alarms=<sagemaker_core.main.utils.Unassigned object>)

Automatic rollback configuration for handling endpoint deployment failures and recovery.

Parameters:

alarms (List[Alarm] | None)

alarms

Type:

List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Autotune(*, mode)

A flag to indicate if you want to use Autotune to automatically find optimal values for the following fields: ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job. RetryStrategy: The number of times to retry a training job. Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches. ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Parameters:

mode (str)

mode

Type:

Set Mode to Enabled if you want to use Autotune.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Base

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchDataCaptureConfig(*, destination_s3_uri, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, generate_inference_id=<sagemaker_core.main.utils.Unassigned object>)

Configuration to control how SageMaker captures inference data for batch transform jobs.

Parameters:

• destination_s3_uri (str)

• kms_key_id (str | None)

• generate_inference_id (bool | None)

destination_s3_uri

Type:

The Amazon S3 location being used to capture the data.

kms_key_id

Type:

The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

generate_inference_id

Type:

Flag that indicates whether to append inference id to the output.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchDeleteClusterNodesError(*, code, message, node_id)

Represents an error encountered when deleting a node from a SageMaker HyperPod cluster.

Parameters:

• code (str)

• message (str)

• node_id (str)

code

Type:

The error code associated with the error encountered when deleting a node. The code provides information about the specific issue encountered, such as the node not being found, the node’s status being invalid for deletion, or the node ID being in use by another process.

message

Type:

A message describing the error encountered when deleting a node.

node_id

Type:

The ID of the node that encountered an error during the deletion process.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchDeleteClusterNodesResponse(*, failed=<sagemaker_core.main.utils.Unassigned object>, successful=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• failed (List[BatchDeleteClusterNodesError] | None)

• successful (List[str] | None)

failed

Type:

A list of errors encountered when deleting the specified nodes.

successful

Type:

A list of node IDs that were successfully deleted from the specified cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchDescribeModelPackageError(*, error_code, error_response)

The error code and error description associated with the resource.

Parameters:

• error_code (str)

• error_response (str)

error_code

Type:

str

error_response

Type:

str

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchDescribeModelPackageOutput(*, model_package_summaries=<sagemaker_core.main.utils.Unassigned object>, batch_describe_model_package_error_map=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• model_package_summaries (Dict[str, BatchDescribeModelPackageSummary] | None)

• batch_describe_model_package_error_map (Dict[str, BatchDescribeModelPackageError] | None)

model_package_summaries

Type:

The summaries for the model package versions

batch_describe_model_package_error_map

Type:

A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchDescribeModelPackageSummary(*, model_package_group_name, model_package_arn, creation_time, inference_specification, model_package_status, model_package_version=<sagemaker_core.main.utils.Unassigned object>, model_package_description=<sagemaker_core.main.utils.Unassigned object>, model_approval_status=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information about the model package.

Parameters:

• model_package_group_name (str | object)

• model_package_arn (str)

• creation_time (datetime)

• inference_specification (InferenceSpecification)

• model_package_status (str)

• model_package_version (int | None)

• model_package_description (str | None)

• model_approval_status (str | None)

model_package_group_name

Type:

The group name for the model package

model_package_version

Type:

The version number of a versioned model.

model_package_arn

Type:

The Amazon Resource Name (ARN) of the model package.

model_package_description

Type:

The description of the model package.

creation_time

Type:

The creation time of the mortgage package summary.

inference_specification

Type:

sagemaker_core.main.shapes.InferenceSpecification

model_package_status

Type:

The status of the mortgage package.

model_approval_status

Type:

The approval status of the model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchGetMetricsResponse(*, metric_query_results=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

metric_query_results (List[MetricQueryResult] | None)

metric_query_results

Type:

The results of a query to retrieve training metrics from SageMaker.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchGetRecordError(*, feature_group_name, record_identifier_value_as_string, error_code, error_message)

The error that has occurred when attempting to retrieve a batch of Records.

Parameters:

• feature_group_name (str | object)

• record_identifier_value_as_string (str)

• error_code (str)

• error_message (str)

feature_group_name

Type:

The name of the feature group that the record belongs to.

record_identifier_value_as_string

Type:

The value for the RecordIdentifier in string format of a Record from a FeatureGroup that is causing an error when attempting to be retrieved.

error_code

Type:

The error code of an error that has occurred when attempting to retrieve a batch of Records. For more information on errors, see Errors.

error_message

Type:

The error message of an error that has occurred when attempting to retrieve a record in the batch.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchGetRecordIdentifier(*, feature_group_name, record_identifiers_value_as_string, feature_names=<sagemaker_core.main.utils.Unassigned object>)

The identifier that identifies the batch of Records you are retrieving in a batch.

Parameters:

• feature_group_name (str | object)

• record_identifiers_value_as_string (List[str])

• feature_names (List[str] | None)

feature_group_name

Type:

The name or Amazon Resource Name (ARN) of the FeatureGroup containing the records you are retrieving in a batch.

record_identifiers_value_as_string

Type:

The value for a list of record identifiers in string format.

feature_names

Type:

List of names of Features to be retrieved. If not specified, the latest value for all the Features are returned.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchGetRecordResponse(*, records, errors, unprocessed_identifiers)

Parameters:

• records (List[BatchGetRecordResultDetail])

• errors (List[BatchGetRecordError])

• unprocessed_identifiers (List[BatchGetRecordIdentifier])

records

Type:

A list of Records you requested to be retrieved in batch.

errors

Type:

A list of errors that have occurred when retrieving a batch of Records.

unprocessed_identifiers

Type:

A unprocessed list of FeatureGroup names, with their corresponding RecordIdentifier value, and Feature name.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchGetRecordResultDetail(*, feature_group_name, record_identifier_value_as_string, record, expires_at=<sagemaker_core.main.utils.Unassigned object>)

The output of records that have been retrieved in a batch.

Parameters:

• feature_group_name (str | object)

• record_identifier_value_as_string (str)

• record (List[FeatureValue])

• expires_at (str | None)

feature_group_name

Type:

The FeatureGroupName containing Records you retrieved in a batch.

record_identifier_value_as_string

Type:

The value of the record identifier in string format.

record

Type:

The Record retrieved.

expires_at

Type:

The ExpiresAt ISO string of the requested record.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchPutMetricsError(*, code=<sagemaker_core.main.utils.Unassigned object>, metric_index=<sagemaker_core.main.utils.Unassigned object>)

An error that occured when putting the metric data.

Parameters:

• code (str | None)

• metric_index (int | None)

code

Type:

The error code of an error that occured when attempting to put metrics. METRIC_LIMIT_EXCEEDED: The maximum amount of metrics per resource is exceeded. INTERNAL_ERROR: An internal error occured. VALIDATION_ERROR: The metric data failed validation. CONFLICT_ERROR: Multiple requests attempted to modify the same data simultaneously.

metric_index

Type:

An index that corresponds to the metric in the request.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BatchTransformInput(*, data_captured_destination_s3_uri, dataset_format, local_path, s3_input_mode=<sagemaker_core.main.utils.Unassigned object>, s3_data_distribution_type=<sagemaker_core.main.utils.Unassigned object>, features_attribute=<sagemaker_core.main.utils.Unassigned object>, inference_attribute=<sagemaker_core.main.utils.Unassigned object>, probability_attribute=<sagemaker_core.main.utils.Unassigned object>, probability_threshold_attribute=<sagemaker_core.main.utils.Unassigned object>, start_time_offset=<sagemaker_core.main.utils.Unassigned object>, end_time_offset=<sagemaker_core.main.utils.Unassigned object>, exclude_features_attribute=<sagemaker_core.main.utils.Unassigned object>)

Input object for the batch transform job.

Parameters:

• data_captured_destination_s3_uri (str)

• dataset_format (MonitoringDatasetFormat)

• local_path (str)

• s3_input_mode (str | None)

• s3_data_distribution_type (str | None)

• features_attribute (str | None)

• inference_attribute (str | None)

• probability_attribute (str | None)

• probability_threshold_attribute (float | None)

• start_time_offset (str | None)

• end_time_offset (str | None)

• exclude_features_attribute (str | None)

data_captured_destination_s3_uri

Type:

The Amazon S3 location being used to capture the data.

dataset_format

Type:

The dataset format for your batch transform job.

local_path

Type:

Path to the filesystem where the batch transform data is available to the container.

s3_input_mode

Type:

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

s3_data_distribution_type

Type:

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

features_attribute

Type:

The attributes of the input data that are the input features.

inference_attribute

Type:

The attribute of the input data that represents the ground truth label.

probability_attribute

Type:

In a classification problem, the attribute that represents the class probability.

probability_threshold_attribute

Type:

The threshold for the class probability to be evaluated as a positive result.

start_time_offset

Type:

If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

end_time_offset

Type:

If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

exclude_features_attribute

Type:

The attributes of the input data to exclude from the analysis.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BestObjectiveNotImproving(*, max_number_of_training_jobs_not_improving=<sagemaker_core.main.utils.Unassigned object>)

A structure that keeps track of which training jobs launched by your hyperparameter tuning job are not improving model performance as evaluated against an objective function.

Parameters:

max_number_of_training_jobs_not_improving (int | None)

max_number_of_training_jobs_not_improving

Type:

The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Bias(*, report=<sagemaker_core.main.utils.Unassigned object>, pre_training_report=<sagemaker_core.main.utils.Unassigned object>, post_training_report=<sagemaker_core.main.utils.Unassigned object>)

Contains bias metrics for a model.

Parameters:

• report (MetricsSource | None)

• pre_training_report (MetricsSource | None)

• post_training_report (MetricsSource | None)

report

Type:

The bias report for a model

pre_training_report

Type:

The pre-training bias report for a model.

post_training_report

Type:

The post-training bias report for a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.BlueGreenUpdatePolicy(*, traffic_routing_configuration, termination_wait_in_seconds=<sagemaker_core.main.utils.Unassigned object>, maximum_execution_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

Parameters:

• traffic_routing_configuration (TrafficRoutingConfig)

• termination_wait_in_seconds (int | None)

• maximum_execution_timeout_in_seconds (int | None)

traffic_routing_configuration

Type:

Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.

termination_wait_in_seconds

Type:

Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.

maximum_execution_timeout_in_seconds

Type:

Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CacheHitResult(*, source_pipeline_execution_arn=<sagemaker_core.main.utils.Unassigned object>)

Details on the cache hit of a pipeline execution step.

Parameters:

source_pipeline_execution_arn (str | None)

source_pipeline_execution_arn

Type:

The Amazon Resource Name (ARN) of the pipeline execution.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CallbackStepMetadata(*, callback_token=<sagemaker_core.main.utils.Unassigned object>, sqs_queue_url=<sagemaker_core.main.utils.Unassigned object>, output_parameters=<sagemaker_core.main.utils.Unassigned object>)

Metadata about a callback step.

Parameters:

• callback_token (str | None)

• sqs_queue_url (str | None)

• output_parameters (List[OutputParameter] | None)

callback_token

Type:

The pipeline generated token from the Amazon SQS queue.

sqs_queue_url

Type:

The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.

output_parameters

Type:

A list of the output parameters of the callback step.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CandidateArtifactLocations(*, explainability, model_insights=<sagemaker_core.main.utils.Unassigned object>, backtest_results=<sagemaker_core.main.utils.Unassigned object>)

The location of artifacts for an AutoML candidate job.

Parameters:

• explainability (str)

• model_insights (str | None)

• backtest_results (str | None)

explainability

Type:

The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

model_insights

Type:

The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.

backtest_results

Type:

The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CandidateGenerationConfig(*, algorithms_config=<sagemaker_core.main.utils.Unassigned object>)

Stores the configuration information for how model candidates are generated using an AutoML job V2.

Parameters:

algorithms_config (List[AutoMLAlgorithmConfig] | None)

algorithms_config

Type:

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type. AlgorithmsConfig stores the customized selection of algorithms to train on your data. For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode . AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO. When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode. When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode. For the list of all algorithms per training mode, see AlgorithmConfig. For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide. For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide. When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting. When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CandidateProperties(*, candidate_artifact_locations=<sagemaker_core.main.utils.Unassigned object>, candidate_metrics=<sagemaker_core.main.utils.Unassigned object>)

The properties of an AutoML candidate job.

Parameters:

• candidate_artifact_locations (CandidateArtifactLocations | None)

• candidate_metrics (List[MetricDatum] | None)

candidate_artifact_locations

Type:

The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

candidate_metrics

Type:

Information about the candidate metrics for an AutoML job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CanvasAppSettings(*, time_series_forecasting_settings=<sagemaker_core.main.utils.Unassigned object>, model_register_settings=<sagemaker_core.main.utils.Unassigned object>, workspace_settings=<sagemaker_core.main.utils.Unassigned object>, identity_provider_o_auth_settings=<sagemaker_core.main.utils.Unassigned object>, direct_deploy_settings=<sagemaker_core.main.utils.Unassigned object>, kendra_settings=<sagemaker_core.main.utils.Unassigned object>, generative_ai_settings=<sagemaker_core.main.utils.Unassigned object>, emr_serverless_settings=<sagemaker_core.main.utils.Unassigned object>)

The SageMaker Canvas application settings.

Parameters:

• time_series_forecasting_settings (TimeSeriesForecastingSettings | None)

• model_register_settings (ModelRegisterSettings | None)

• workspace_settings (WorkspaceSettings | None)

• identity_provider_o_auth_settings (List[IdentityProviderOAuthSetting] | None)

• direct_deploy_settings (DirectDeploySettings | None)

• kendra_settings (KendraSettings | None)

• generative_ai_settings (GenerativeAiSettings | None)

• emr_serverless_settings (EmrServerlessSettings | None)

time_series_forecasting_settings

Type:

Time series forecast settings for the SageMaker Canvas application.

model_register_settings

Type:

The model registry settings for the SageMaker Canvas application.

workspace_settings

Type:

The workspace settings for the SageMaker Canvas application.

identity_provider_o_auth_settings

Type:

The settings for connecting to an external data source with OAuth.

direct_deploy_settings

Type:

The model deployment settings for the SageMaker Canvas application.

kendra_settings

Type:

The settings for document querying.

generative_ai_settings

Type:

The generative AI settings for the SageMaker Canvas application.

emr_serverless_settings

Type:

The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CapacitySize(*, type, value)

Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy. You can specify your batches as either instance count or the overall percentage or your fleet. For a rollback strategy, if you don’t specify the fields in this object, or if you set the Value to 100%, then SageMaker uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.

Parameters:

• type (str)

• value (int)

type

Type:

Specifies the endpoint capacity type. INSTANCE_COUNT: The endpoint activates based on the number of instances. CAPACITY_PERCENT: The endpoint activates based on the specified percentage of capacity.

value

Type:

Defines the capacity size, either as a number of instances or a capacity percentage.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CaptureContentTypeHeader(*, csv_content_types=<sagemaker_core.main.utils.Unassigned object>, json_content_types=<sagemaker_core.main.utils.Unassigned object>)

Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.

Parameters:

• csv_content_types (List[str] | None)

• json_content_types (List[str] | None)

csv_content_types

Type:

The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.

json_content_types

Type:

The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CaptureOption(*, capture_mode)

Specifies data Model Monitor will capture.

Parameters:

capture_mode (str)

capture_mode

Type:

Specify the boundary of data to capture.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CategoricalParameter(*, name, value)

Environment parameters you want to benchmark your load test against.

Parameters:

• name (str)

• value (List[str])

name

Type:

The Name of the environment variable.

value

Type:

The list of values you can pass.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CategoricalParameterRange(*, name, values)

A list of categorical hyperparameters to tune.

Parameters:

• name (str)

• values (List[str])

name

Type:

The name of the categorical hyperparameter to tune.

values

Type:

A list of the categories for the hyperparameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CategoricalParameterRangeSpecification(*, values)

Defines the possible values for a categorical hyperparameter.

Parameters:

values (List[str])

values

Type:

The allowed categories for the hyperparameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Channel(*, channel_name, data_source, content_type=<sagemaker_core.main.utils.Unassigned object>, compression_type=<sagemaker_core.main.utils.Unassigned object>, record_wrapper_type=<sagemaker_core.main.utils.Unassigned object>, input_mode=<sagemaker_core.main.utils.Unassigned object>, shuffle_config=<sagemaker_core.main.utils.Unassigned object>)

A channel is a named input source that training algorithms can consume.

Parameters:

• channel_name (str)

• data_source (DataSource)

• content_type (str | None)

• compression_type (str | None)

• record_wrapper_type (str | None)

• input_mode (str | None)

• shuffle_config (ShuffleConfig | None)

channel_name

Type:

The name of the channel.

data_source

Type:

The location of the channel data.

content_type

Type:

The MIME type of the data.

compression_type

Type:

If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

record_wrapper_type

Type:

Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don’t need to set this attribute. For more information, see Create a Dataset Using RecordIO. In File mode, leave this field unset or set it to None.

input_mode

Type:

(Optional) The input mode to use for the data channel in a training job. If you don’t set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job’s general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode. To use a model for incremental training, choose File input model.

shuffle_config

Type:

A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value. For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ChannelSpecification(*, name, supported_content_types, supported_input_modes, description=<sagemaker_core.main.utils.Unassigned object>, is_required=<sagemaker_core.main.utils.Unassigned object>, supported_compression_types=<sagemaker_core.main.utils.Unassigned object>)

Defines a named input source, called a channel, to be used by an algorithm.

Parameters:

• name (str)

• supported_content_types (List[str])

• supported_input_modes (List[str])

• description (str | None)

• is_required (bool | None)

• supported_compression_types (List[str] | None)

name

Type:

The name of the channel.

description

Type:

A brief description of the channel.

is_required

Type:

Indicates whether the channel is required by the algorithm.

supported_content_types

Type:

The supported MIME types for the data.

supported_compression_types

Type:

The allowed compression types, if data compression is used.

supported_input_modes

Type:

The allowed input mode, either FILE or PIPE. In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CheckpointConfig(*, s3_uri, local_path=<sagemaker_core.main.utils.Unassigned object>)

Contains information about the output location for managed spot training checkpoint data.

Parameters:

• s3_uri (str)

• local_path (str | None)

s3_uri

Type:

Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.

local_path

Type:

(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClarifyCheckStepMetadata(*, check_type=<sagemaker_core.main.utils.Unassigned object>, baseline_used_for_drift_check_constraints=<sagemaker_core.main.utils.Unassigned object>, calculated_baseline_constraints=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, violation_report=<sagemaker_core.main.utils.Unassigned object>, check_job_arn=<sagemaker_core.main.utils.Unassigned object>, skip_check=<sagemaker_core.main.utils.Unassigned object>, register_new_baseline=<sagemaker_core.main.utils.Unassigned object>)

The container for the metadata for the ClarifyCheck step. For more information, see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.

Parameters:

• check_type (str | None)

• baseline_used_for_drift_check_constraints (str | None)

• calculated_baseline_constraints (str | None)

• model_package_group_name (str | object | None)

• violation_report (str | None)

• check_job_arn (str | None)

• skip_check (bool | None)

• register_new_baseline (bool | None)

check_type

Type:

The type of the Clarify Check step

baseline_used_for_drift_check_constraints

Type:

The Amazon S3 URI of baseline constraints file to be used for the drift check.

calculated_baseline_constraints

Type:

The Amazon S3 URI of the newly calculated baseline constraints file.

model_package_group_name

Type:

The model package group name.

violation_report

Type:

The Amazon S3 URI of the violation report if violations are detected.

check_job_arn

Type:

The Amazon Resource Name (ARN) of the check processing job that was run by this step’s execution.

skip_check

Type:

This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.

register_new_baseline

Type:

This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints property.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClarifyExplainerConfig(*, shap_config, enable_explanations=<sagemaker_core.main.utils.Unassigned object>, inference_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration parameters for the SageMaker Clarify explainer.

Parameters:

• shap_config (ClarifyShapConfig)

• enable_explanations (str | None)

• inference_config (ClarifyInferenceConfig | None)

enable_explanations

Type:

A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See EnableExplanations for additional information.

inference_config

Type:

The inference configuration parameter for the model container.

shap_config

Type:

The configuration for SHAP analysis.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClarifyInferenceConfig(*, features_attribute=<sagemaker_core.main.utils.Unassigned object>, content_template=<sagemaker_core.main.utils.Unassigned object>, max_record_count=<sagemaker_core.main.utils.Unassigned object>, max_payload_in_mb=<sagemaker_core.main.utils.Unassigned object>, probability_index=<sagemaker_core.main.utils.Unassigned object>, label_index=<sagemaker_core.main.utils.Unassigned object>, probability_attribute=<sagemaker_core.main.utils.Unassigned object>, label_attribute=<sagemaker_core.main.utils.Unassigned object>, label_headers=<sagemaker_core.main.utils.Unassigned object>, feature_headers=<sagemaker_core.main.utils.Unassigned object>, feature_types=<sagemaker_core.main.utils.Unassigned object>)

The inference configuration parameter for the model container.

Parameters:

• features_attribute (str | None)

• content_template (str | None)

• max_record_count (int | None)

• max_payload_in_mb (int | None)

• probability_index (int | None)

• label_index (int | None)

• probability_attribute (str | None)

• label_attribute (str | None)

• label_headers (List[str] | None)

• feature_headers (List[str] | None)

• feature_types (List[str] | None)

features_attribute

Type:

Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression ‘myfeatures’, it extracts a list of features [1,2,3] from request data ‘{“myfeatures”:[1,2,3]}’.

content_template

Type:

A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string ‘{“myfeatures”:$features}’ will format a list of features [1,2,3] into the record string ‘{“myfeatures”:[1,2,3]}’. Required only when the model container input is in JSON Lines format.

max_record_count

Type:

The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container’s capacity at runtime.

max_payload_in_mb

Type:

The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

probability_index

Type:

A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list. Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: ‘1,0.6’, set ProbabilityIndex to 1 to select the probability value 0.6. Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: ‘”[‘cat’,’dog’,’fish’]”,”[0.1,0.6,0.3]”’, set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3].

label_index

Type:

A zero-based index used to extract a label header or list of label headers from model container output in CSV format. Example for a multiclass model: If the model container output consists of label headers followed by probabilities: ‘”[‘cat’,’dog’,’fish’]”,”[0.1,0.6,0.3]”’, set LabelIndex to 0 to select the label headers [‘cat’,’dog’,’fish’].

probability_attribute

Type:

A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format. Example: If the model container output of a single request is ‘{“predicted_label”:1,”probability”:0.6}’, then set ProbabilityAttribute to ‘probability’.

label_attribute

Type:

A JMESPath expression used to locate the list of label headers in the model container output. Example: If the model container output of a batch request is ‘{“labels”:[“cat”,”dog”,”fish”],”probability”:[0.6,0.3,0.1]}’, then set LabelAttribute to ‘labels’ to extract the list of label headers [“cat”,”dog”,”fish”]

label_headers

Type:

For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

feature_headers

Type:

The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

feature_types

Type:

A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one ‘text’ string (for example, [‘text’]). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClarifyShapBaselineConfig(*, mime_type=<sagemaker_core.main.utils.Unassigned object>, shap_baseline=<sagemaker_core.main.utils.Unassigned object>, shap_baseline_uri=<sagemaker_core.main.utils.Unassigned object>)

The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm. The number of records in the baseline data determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint. ShapBaseline and ShapBaselineUri are mutually exclusive parameters. One or the either is required to configure a SHAP baseline.

Parameters:

• mime_type (str | None)

• shap_baseline (str | None)

• shap_baseline_uri (str | None)

mime_type

Type:

The MIME type of the baseline data. Choose from ‘text/csv’ or ‘application/jsonlines’. Defaults to ‘text/csv’.

shap_baseline

Type:

The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

shap_baseline_uri

Type:

The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClarifyShapConfig(*, shap_baseline_config, number_of_samples=<sagemaker_core.main.utils.Unassigned object>, use_logit=<sagemaker_core.main.utils.Unassigned object>, seed=<sagemaker_core.main.utils.Unassigned object>, text_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration for SHAP analysis using SageMaker Clarify Explainer.

Parameters:

• shap_baseline_config (ClarifyShapBaselineConfig)

• number_of_samples (int | None)

• use_logit (bool | None)

• seed (int | None)

• text_config (ClarifyTextConfig | None)

shap_baseline_config

Type:

The configuration for the SHAP baseline of the Kernal SHAP algorithm.

number_of_samples

Type:

The number of samples to be used for analysis by the Kernal SHAP algorithm. The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.

use_logit

Type:

A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

seed

Type:

The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

text_config

Type:

A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClarifyTextConfig(*, language, granularity)

A parameter used to configure the SageMaker Clarify explainer to treat text features as text so that explanations are provided for individual units of text. Required only for natural language processing (NLP) explainability.

Parameters:

• language (str)

• granularity (str)

language

Type:

Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language. For a mix of multiple languages, use code ‘xx’.

granularity

Type:

The unit of granularity for the analysis of text features. For example, if the unit is ‘token’, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterEbsVolumeConfig(*, volume_size_in_gb)

Defines the configuration for attaching an additional Amazon Elastic Block Store (EBS) volume to each instance of the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.

Parameters:

volume_size_in_gb (int)

volume_size_in_gb

Type:

The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterInstanceGroupDetails(*, current_count=<sagemaker_core.main.utils.Unassigned object>, target_count=<sagemaker_core.main.utils.Unassigned object>, instance_group_name=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, life_cycle_config=<sagemaker_core.main.utils.Unassigned object>, execution_role=<sagemaker_core.main.utils.Unassigned object>, threads_per_core=<sagemaker_core.main.utils.Unassigned object>, instance_storage_configs=<sagemaker_core.main.utils.Unassigned object>, on_start_deep_health_checks=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, training_plan_arn=<sagemaker_core.main.utils.Unassigned object>, training_plan_status=<sagemaker_core.main.utils.Unassigned object>, override_vpc_config=<sagemaker_core.main.utils.Unassigned object>)

Details of an instance group in a SageMaker HyperPod cluster.

Parameters:

• current_count (int | None)

• target_count (int | None)

• instance_group_name (str | None)

• instance_type (str | None)

• life_cycle_config (ClusterLifeCycleConfig | None)

• execution_role (str | None)

• threads_per_core (int | None)

• instance_storage_configs (List[ClusterInstanceStorageConfig] | None)

• on_start_deep_health_checks (List[str] | None)

• status (str | None)

• training_plan_arn (str | None)

• training_plan_status (str | None)

• override_vpc_config (VpcConfig | None)

current_count

Type:

The number of instances that are currently in the instance group of a SageMaker HyperPod cluster.

target_count

Type:

The number of instances you specified to add to the instance group of a SageMaker HyperPod cluster.

instance_group_name

Type:

The name of the instance group of a SageMaker HyperPod cluster.

instance_type

Type:

The instance type of the instance group of a SageMaker HyperPod cluster.

life_cycle_config

Type:

Details of LifeCycle configuration for the instance group.

execution_role

Type:

The execution role for the instance group to assume.

threads_per_core

Type:

The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

instance_storage_configs

Type:

The additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.

on_start_deep_health_checks

Type:

A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated.

status

Type:

The current status of the cluster instance group. InService: The instance group is active and healthy. Creating: The instance group is being provisioned. Updating: The instance group is being updated. Failed: The instance group has failed to provision or is no longer healthy. Degraded: The instance group is degraded, meaning that some instances have failed to provision or are no longer healthy. Deleting: The instance group is being deleted.

training_plan_arn

Type:

The Amazon Resource Name (ARN); of the training plan associated with this cluster instance group. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

training_plan_status

Type:

The current status of the training plan associated with this cluster instance group.

override_vpc_config

Type:

The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterInstanceGroupSpecification(*, instance_count, instance_group_name, instance_type, life_cycle_config, execution_role, threads_per_core=<sagemaker_core.main.utils.Unassigned object>, instance_storage_configs=<sagemaker_core.main.utils.Unassigned object>, on_start_deep_health_checks=<sagemaker_core.main.utils.Unassigned object>, training_plan_arn=<sagemaker_core.main.utils.Unassigned object>, override_vpc_config=<sagemaker_core.main.utils.Unassigned object>)

The specifications of an instance group that you need to define.

Parameters:

• instance_count (int)

• instance_group_name (str)

• instance_type (str)

• life_cycle_config (ClusterLifeCycleConfig)

• execution_role (str)

• threads_per_core (int | None)

• instance_storage_configs (List[ClusterInstanceStorageConfig] | None)

• on_start_deep_health_checks (List[str] | None)

• training_plan_arn (str | None)

• override_vpc_config (VpcConfig | None)

instance_count

Type:

Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.

instance_group_name

Type:

Specifies the name of the instance group.

instance_type

Type:

Specifies the instance type of the instance group.

life_cycle_config

Type:

Specifies the LifeCycle configuration for the instance group.

execution_role

Type:

Specifies an IAM execution role to be assumed by the instance group.

threads_per_core

Type:

Specifies the value for Threads per core. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn’t support multithreading, specify 1. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

instance_storage_configs

Type:

Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.

on_start_deep_health_checks

Type:

A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated.

training_plan_arn

Type:

The Amazon Resource Name (ARN); of the training plan to use for this cluster instance group. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

override_vpc_config

Type:

To configure multi-AZ deployments, customize the Amazon VPC configuration at the instance group level. You can specify different subnets and security groups across different AZs in the instance group specification to override a SageMaker HyperPod cluster’s default Amazon VPC configuration. For more information about deploying a cluster in multiple AZs, see Setting up SageMaker HyperPod clusters across multiple AZs. When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform: Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications. In Amazon EKS-orchestrated clusters, nodes receive dual-stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment. Additional resources for IPv6 configuration: For information about adding IPv6 support to your VPC, see to IPv6 Support for VPC. For information about creating a new IPv6-compatible VPC, see Amazon VPC Creation Guide. To configure SageMaker HyperPod with a custom Amazon VPC, see Custom Amazon VPC Setup for SageMaker HyperPod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterInstancePlacement(*, availability_zone=<sagemaker_core.main.utils.Unassigned object>, availability_zone_id=<sagemaker_core.main.utils.Unassigned object>)

Specifies the placement details for the node in the SageMaker HyperPod cluster, including the Availability Zone and the unique identifier (ID) of the Availability Zone.

Parameters:

• availability_zone (str | None)

• availability_zone_id (str | None)

availability_zone

Type:

The Availability Zone where the node in the SageMaker HyperPod cluster is launched.

availability_zone_id

Type:

The unique identifier (ID) of the Availability Zone where the node in the SageMaker HyperPod cluster is launched.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterInstanceStatusDetails(*, status, message=<sagemaker_core.main.utils.Unassigned object>)

Details of an instance in a SageMaker HyperPod cluster.

Parameters:

• status (str)

• message (str | None)

status

Type:

The status of an instance in a SageMaker HyperPod cluster.

message

Type:

The message from an instance in a SageMaker HyperPod cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterInstanceStorageConfig(*, ebs_volume_config=<sagemaker_core.main.utils.Unassigned object>)

Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.

Parameters:

ebs_volume_config (ClusterEbsVolumeConfig | None)

ebs_volume_config

Type:

Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterLifeCycleConfig(*, source_s3_uri, on_create)

The lifecycle configuration for a SageMaker HyperPod cluster.

Parameters:

• source_s3_uri (str)

• on_create (str)

source_s3_uri

Type:

An Amazon S3 bucket path where your lifecycle scripts are stored. Make sure that the S3 bucket path starts with s3://sagemaker-. The IAM role for SageMaker HyperPod has the managed AmazonSageMakerClusterInstanceRolePolicy attached, which allows access to S3 buckets with the specific prefix sagemaker-.

on_create

Type:

The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This entrypoint script runs during cluster creation.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterNodeDetails(*, instance_group_name=<sagemaker_core.main.utils.Unassigned object>, instance_id=<sagemaker_core.main.utils.Unassigned object>, instance_status=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, launch_time=<sagemaker_core.main.utils.Unassigned object>, life_cycle_config=<sagemaker_core.main.utils.Unassigned object>, override_vpc_config=<sagemaker_core.main.utils.Unassigned object>, threads_per_core=<sagemaker_core.main.utils.Unassigned object>, instance_storage_configs=<sagemaker_core.main.utils.Unassigned object>, private_primary_ip=<sagemaker_core.main.utils.Unassigned object>, private_primary_ipv6=<sagemaker_core.main.utils.Unassigned object>, private_dns_hostname=<sagemaker_core.main.utils.Unassigned object>, placement=<sagemaker_core.main.utils.Unassigned object>)

Details of an instance (also called a node interchangeably) in a SageMaker HyperPod cluster.

Parameters:

• instance_group_name (str | None)

• instance_id (str | None)

• instance_status (ClusterInstanceStatusDetails | None)

• instance_type (str | None)

• launch_time (datetime | None)

• life_cycle_config (ClusterLifeCycleConfig | None)

• override_vpc_config (VpcConfig | None)

• threads_per_core (int | None)

• instance_storage_configs (List[ClusterInstanceStorageConfig] | None)

• private_primary_ip (str | None)

• private_primary_ipv6 (str | None)

• private_dns_hostname (str | None)

• placement (ClusterInstancePlacement | None)

instance_group_name

Type:

The instance group name in which the instance is.

instance_id

Type:

The ID of the instance.

instance_status

Type:

The status of the instance.

instance_type

Type:

The type of the instance.

launch_time

Type:

The time when the instance is launched.

life_cycle_config

Type:

The LifeCycle configuration applied to the instance.

override_vpc_config

Type:

The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster.

threads_per_core

Type:

The number of threads per CPU core you specified under CreateCluster.

instance_storage_configs

Type:

The configurations of additional storage specified to the instance group where the instance (node) is launched.

private_primary_ip

Type:

The private primary IP address of the SageMaker HyperPod cluster node.

private_primary_ipv6

Type:

The private primary IPv6 address of the SageMaker HyperPod cluster node when configured with an Amazon VPC that supports IPv6 and includes subnets with IPv6 addressing enabled in either the cluster Amazon VPC configuration or the instance group Amazon VPC configuration.

private_dns_hostname

Type:

The private DNS hostname of the SageMaker HyperPod cluster node.

placement

Type:

The placement details of the SageMaker HyperPod cluster node.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterNodeSummary(*, instance_group_name, instance_id, instance_type, launch_time, instance_status)

Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.

Parameters:

• instance_group_name (str)

• instance_id (str)

• instance_type (str)

• launch_time (datetime)

• instance_status (ClusterInstanceStatusDetails)

instance_group_name

Type:

The name of the instance group in which the instance is.

instance_id

Type:

The ID of the instance.

instance_type

Type:

The type of the instance.

launch_time

Type:

The time when the instance is launched.

instance_status

Type:

The status of the instance.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterOrchestrator(*, eks)

The type of orchestrator used for the SageMaker HyperPod cluster.

Parameters:

eks (ClusterOrchestratorEksConfig)

eks

Type:

The Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterOrchestratorEksConfig(*, cluster_arn)

The configuration settings for the Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.

Parameters:

cluster_arn (str)

cluster_arn

Type:

The Amazon Resource Name (ARN) of the Amazon EKS cluster associated with the SageMaker HyperPod cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterSchedulerConfigSummary(*, cluster_scheduler_config_arn, cluster_scheduler_config_id, name, creation_time, status, cluster_scheduler_config_version=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, cluster_arn=<sagemaker_core.main.utils.Unassigned object>)

Summary of the cluster policy.

Parameters:

• cluster_scheduler_config_arn (str)

• cluster_scheduler_config_id (str)

• name (str)

• creation_time (datetime)

• status (str)

• cluster_scheduler_config_version (int | None)

• last_modified_time (datetime | None)

• cluster_arn (str | None)

cluster_scheduler_config_arn

Type:

ARN of the cluster policy.

cluster_scheduler_config_id

Type:

ID of the cluster policy.

cluster_scheduler_config_version

Type:

Version of the cluster policy.

name

Type:

Name of the cluster policy.

creation_time

Type:

Creation time of the cluster policy.

last_modified_time

Type:

Last modified time of the cluster policy.

status

Type:

Status of the cluster policy.

cluster_arn

Type:

ARN of the cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ClusterSummary(*, cluster_arn, cluster_name, creation_time, cluster_status, training_plan_arns=<sagemaker_core.main.utils.Unassigned object>)

Lists a summary of the properties of a SageMaker HyperPod cluster.

Parameters:

• cluster_arn (str)

• cluster_name (str | object)

• creation_time (datetime)

• cluster_status (str)

• training_plan_arns (List[str] | None)

cluster_arn

Type:

The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

cluster_name

Type:

The name of the SageMaker HyperPod cluster.

creation_time

Type:

The time when the SageMaker HyperPod cluster is created.

cluster_status

Type:

The status of the SageMaker HyperPod cluster.

training_plan_arns

Type:

A list of Amazon Resource Names (ARNs) of the training plans associated with this cluster. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CodeEditorAppImageConfig(*, file_system_config=<sagemaker_core.main.utils.Unassigned object>, container_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration for the file system and kernels in a SageMaker image running as a Code Editor app. The FileSystemConfig object is not supported.

Parameters:

• file_system_config (FileSystemConfig | None)

• container_config (ContainerConfig | None)

file_system_config

Type:

sagemaker_core.main.shapes.FileSystemConfig | None

container_config

Type:

sagemaker_core.main.shapes.ContainerConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CodeEditorAppSettings(*, default_resource_spec=<sagemaker_core.main.utils.Unassigned object>, custom_images=<sagemaker_core.main.utils.Unassigned object>, lifecycle_config_arns=<sagemaker_core.main.utils.Unassigned object>, app_lifecycle_management=<sagemaker_core.main.utils.Unassigned object>, built_in_lifecycle_config_arn=<sagemaker_core.main.utils.Unassigned object>)

The Code Editor application settings. For more information about Code Editor, see Get started with Code Editor in Amazon SageMaker.

Parameters:

• default_resource_spec (ResourceSpec | None)

• custom_images (List[CustomImage] | None)

• lifecycle_config_arns (List[str] | None)

• app_lifecycle_management (AppLifecycleManagement | None)

• built_in_lifecycle_config_arn (str | None)

default_resource_spec

Type:

sagemaker_core.main.shapes.ResourceSpec | None

custom_images

Type:

A list of custom SageMaker images that are configured to run as a Code Editor app.

lifecycle_config_arns

Type:

The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.

app_lifecycle_management

Type:

Settings that are used to configure and manage the lifecycle of CodeEditor applications.

built_in_lifecycle_config_arn

Type:

The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CodeRepository(*, repository_url)

A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.

Parameters:

repository_url (str)

repository_url

Type:

The URL of the Git repository.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CodeRepositorySummary(*, code_repository_name, code_repository_arn, creation_time, last_modified_time, git_config=<sagemaker_core.main.utils.Unassigned object>)

Specifies summary information about a Git repository.

Parameters:

• code_repository_name (str | object)

• code_repository_arn (str)

• creation_time (datetime)

• last_modified_time (datetime)

• git_config (GitConfig | None)

code_repository_name

Type:

The name of the Git repository.

code_repository_arn

Type:

The Amazon Resource Name (ARN) of the Git repository.

creation_time

Type:

The date and time that the Git repository was created.

last_modified_time

Type:

The date and time that the Git repository was last modified.

git_config

Type:

Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CognitoConfig(*, user_pool, client_id)

Use this parameter to configure your Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.

Parameters:

• user_pool (str)

• client_id (str)

user_pool

Type:

A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.

client_id

Type:

The client ID for your Amazon Cognito user pool.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CognitoMemberDefinition(*, user_pool, user_group, client_id)

Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.

Parameters:

• user_pool (str)

• user_group (str)

• client_id (str)

user_pool

Type:

An identifier for a user pool. The user pool must be in the same region as the service that you are calling.

user_group

Type:

An identifier for a user group.

client_id

Type:

An identifier for an application client. You must create the app client ID using Amazon Cognito.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CollectionConfig(*, vector_config=<sagemaker_core.main.utils.Unassigned object>)

Configuration for your collection.

Parameters:

vector_config (VectorConfig | None)

vector_config

Type:

Configuration for your vector collection type. Dimension: The number of elements in your vector.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CollectionConfiguration(*, collection_name=<sagemaker_core.main.utils.Unassigned object>, collection_parameters=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for the Amazon SageMaker Debugger output tensor collections.

Parameters:

• collection_name (str | None)

• collection_parameters (Dict[str, str] | None)

collection_name

Type:

The name of the tensor collection. The name must be unique relative to other rule configuration names.

collection_parameters

Type:

Parameter values for the tensor collection. The allowed parameters are “name”, “include_regex”, “reduction_config”, “save_config”, “tensor_names”, and “save_histogram”.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CompilationJobSummary(*, compilation_job_name, compilation_job_arn, creation_time, compilation_job_status, compilation_start_time=<sagemaker_core.main.utils.Unassigned object>, compilation_end_time=<sagemaker_core.main.utils.Unassigned object>, compilation_target_device=<sagemaker_core.main.utils.Unassigned object>, compilation_target_platform_os=<sagemaker_core.main.utils.Unassigned object>, compilation_target_platform_arch=<sagemaker_core.main.utils.Unassigned object>, compilation_target_platform_accelerator=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

A summary of a model compilation job.

Parameters:

• compilation_job_name (str | object)

• compilation_job_arn (str)

• creation_time (datetime)

• compilation_job_status (str)

• compilation_start_time (datetime | None)

• compilation_end_time (datetime | None)

• compilation_target_device (str | None)

• compilation_target_platform_os (str | None)

• compilation_target_platform_arch (str | None)

• compilation_target_platform_accelerator (str | None)

• last_modified_time (datetime | None)

compilation_job_name

Type:

The name of the model compilation job that you want a summary for.

compilation_job_arn

Type:

The Amazon Resource Name (ARN) of the model compilation job.

creation_time

Type:

The time when the model compilation job was created.

compilation_start_time

Type:

The time when the model compilation job started.

compilation_end_time

Type:

The time when the model compilation job completed.

compilation_target_device

Type:

The type of device that the model will run on after the compilation job has completed.

compilation_target_platform_os

Type:

The type of OS that the model will run on after the compilation job has completed.

compilation_target_platform_arch

Type:

The type of architecture that the model will run on after the compilation job has completed.

compilation_target_platform_accelerator

Type:

The type of accelerator that the model will run on after the compilation job has completed.

last_modified_time

Type:

The time when the model compilation job was last modified.

compilation_job_status

Type:

The status of the model compilation job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ComputeQuotaConfig(*, compute_quota_resources=<sagemaker_core.main.utils.Unassigned object>, resource_sharing_config=<sagemaker_core.main.utils.Unassigned object>, preempt_team_tasks=<sagemaker_core.main.utils.Unassigned object>)

Configuration of the compute allocation definition for an entity. This includes the resource sharing option and the setting to preempt low priority tasks.

Parameters:

• compute_quota_resources (List[ComputeQuotaResourceConfig] | None)

• resource_sharing_config (ResourceSharingConfig | None)

• preempt_team_tasks (str | None)

compute_quota_resources

Type:

Allocate compute resources by instance types.

resource_sharing_config

Type:

Resource sharing configuration. This defines how an entity can lend and borrow idle compute with other entities within the cluster.

preempt_team_tasks

Type:

Allows workloads from within an entity to preempt same-team workloads. When set to LowerPriority, the entity’s lower priority tasks are preempted by their own higher priority tasks. Default is LowerPriority.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ComputeQuotaResourceConfig(*, instance_type, count)

Configuration of the resources used for the compute allocation definition.

Parameters:

• instance_type (str)

• count (int)

instance_type

Type:

The instance type of the instance group for the cluster.

count

Type:

The number of instances to add to the instance group of a SageMaker HyperPod cluster.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ComputeQuotaSummary(*, compute_quota_arn, compute_quota_id, name, status, compute_quota_target, creation_time, compute_quota_version=<sagemaker_core.main.utils.Unassigned object>, cluster_arn=<sagemaker_core.main.utils.Unassigned object>, compute_quota_config=<sagemaker_core.main.utils.Unassigned object>, activation_state=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Summary of the compute allocation definition.

Parameters:

• compute_quota_arn (str)

• compute_quota_id (str)

• name (str)

• status (str)

• compute_quota_target (ComputeQuotaTarget)

• creation_time (datetime)

• compute_quota_version (int | None)

• cluster_arn (str | None)

• compute_quota_config (ComputeQuotaConfig | None)

• activation_state (str | None)

• last_modified_time (datetime | None)

compute_quota_arn

Type:

ARN of the compute allocation definition.

compute_quota_id

Type:

ID of the compute allocation definition.

name

Type:

Name of the compute allocation definition.

compute_quota_version

Type:

Version of the compute allocation definition.

status

Type:

Status of the compute allocation definition.

cluster_arn

Type:

ARN of the cluster.

compute_quota_config

Type:

Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

compute_quota_target

Type:

The target entity to allocate compute resources to.

activation_state

Type:

The state of the compute allocation being described. Use to enable or disable compute allocation. Default is Enabled.

creation_time

Type:

Creation time of the compute allocation definition.

last_modified_time

Type:

Last modified time of the compute allocation definition.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ComputeQuotaTarget(*, team_name, fair_share_weight=<sagemaker_core.main.utils.Unassigned object>)

The target entity to allocate compute resources to.

Parameters:

• team_name (str)

• fair_share_weight (int | None)

team_name

Type:

Name of the team to allocate compute resources to.

fair_share_weight

Type:

Assigned entity fair-share weight. Idle compute will be shared across entities based on these assigned weights. This weight is only used when FairShare is enabled. A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ConditionStepMetadata(*, outcome=<sagemaker_core.main.utils.Unassigned object>)

Metadata for a Condition step.

Parameters:

outcome (str | None)

outcome

Type:

The outcome of the Condition step evaluation.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ConflictException(*, message=<sagemaker_core.main.utils.Unassigned object>)

There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Parameters:

message (str | None)

message

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ContainerConfig(*, container_arguments=<sagemaker_core.main.utils.Unassigned object>, container_entrypoint=<sagemaker_core.main.utils.Unassigned object>, container_environment_variables=<sagemaker_core.main.utils.Unassigned object>)

The configuration used to run the application image container.

Parameters:

• container_arguments (List[str] | None)

• container_entrypoint (List[str] | None)

• container_environment_variables (Dict[str, str] | None)

container_arguments

Type:

The arguments for the container when you’re running the application.

container_entrypoint

Type:

The entrypoint used to run the application in the container.

container_environment_variables

Type:

The environment variables to set in the container

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ContainerDefinition(*, container_hostname=<sagemaker_core.main.utils.Unassigned object>, image=<sagemaker_core.main.utils.Unassigned object>, image_config=<sagemaker_core.main.utils.Unassigned object>, mode=<sagemaker_core.main.utils.Unassigned object>, model_data_url=<sagemaker_core.main.utils.Unassigned object>, model_data_source=<sagemaker_core.main.utils.Unassigned object>, additional_model_data_sources=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, model_package_name=<sagemaker_core.main.utils.Unassigned object>, inference_specification_name=<sagemaker_core.main.utils.Unassigned object>, multi_model_config=<sagemaker_core.main.utils.Unassigned object>)

Describes the container, as part of model definition.

Parameters:

• container_hostname (str | None)

• image (str | None)

• image_config (ImageConfig | None)

• mode (str | None)

• model_data_url (str | None)

• model_data_source (ModelDataSource | None)

• additional_model_data_sources (List[AdditionalModelDataSource] | None)

• environment (Dict[str, str] | None)

• model_package_name (str | object | None)

• inference_specification_name (str | None)

• multi_model_config (MultiModelConfig | None)

container_hostname

Type:

This parameter is ignored for models that contain only a PrimaryContainer. When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don’t specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

image

Type:

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

image_config

Type:

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers. The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

mode

Type:

Whether the container hosts a single model or multiple models.

model_data_url

Type:

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters. The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating. If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide. If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

model_data_source

Type:

Specifies the location of ML model data to deploy. Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

additional_model_data_sources

Type:

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

environment

Type:

The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables. The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

model_package_name

Type:

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

inference_specification_name

Type:

The inference specification name in the model package version.

multi_model_config

Type:

Specifies additional configuration for multi-model endpoints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ContextSource(*, source_uri, source_type=<sagemaker_core.main.utils.Unassigned object>, source_id=<sagemaker_core.main.utils.Unassigned object>)

A structure describing the source of a context.

Parameters:

• source_uri (str)

• source_type (str | None)

• source_id (str | None)

source_uri

Type:

The URI of the source.

source_type

Type:

The type of the source.

source_id

Type:

The ID of the source.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ContextSummary(*, context_arn=<sagemaker_core.main.utils.Unassigned object>, context_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, context_type=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Lists a summary of the properties of a context. A context provides a logical grouping of other entities.

Parameters:

• context_arn (str | None)

• context_name (str | object | None)

• source (ContextSource | None)

• context_type (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

context_arn

Type:

The Amazon Resource Name (ARN) of the context.

context_name

Type:

The name of the context.

source

Type:

The source of the context.

context_type

Type:

The type of the context.

creation_time

Type:

When the context was created.

last_modified_time

Type:

When the context was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ContinuousParameterRange(*, name, min_value, max_value, scaling_type=<sagemaker_core.main.utils.Unassigned object>)

A list of continuous hyperparameters to tune.

Parameters:

• name (str)

• min_value (str)

• max_value (str)

• scaling_type (str | None)

name

Type:

The name of the continuous hyperparameter to tune.

min_value

Type:

The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

max_value

Type:

The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

scaling_type

Type:

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values: Auto SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. Linear Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. ReverseLogarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0&lt;=x&lt;1.0.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ContinuousParameterRangeSpecification(*, min_value, max_value)

Defines the possible values for a continuous hyperparameter.

Parameters:

• min_value (str)

• max_value (str)

min_value

Type:

The minimum floating-point value allowed.

max_value

Type:

The maximum floating-point value allowed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ConvergenceDetected(*, complete_on_convergence=<sagemaker_core.main.utils.Unassigned object>)

A flag to indicating that automatic model tuning (AMT) has detected model convergence, defined as a lack of significant improvement (1% or less) against an objective metric.

Parameters:

complete_on_convergence (str | None)

complete_on_convergence

Type:

A flag to stop a tuning job once AMT has detected that the job has converged.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CustomFileSystem(*, efs_file_system=<sagemaker_core.main.utils.Unassigned object>, f_sx_lustre_file_system=<sagemaker_core.main.utils.Unassigned object>)

A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

Parameters:

• efs_file_system (EFSFileSystem | None)

• f_sx_lustre_file_system (FSxLustreFileSystem | None)

efs_file_system

Type:

A custom file system in Amazon EFS.

f_sx_lustre_file_system

Type:

A custom file system in Amazon FSx for Lustre.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CustomFileSystemConfig(*, efs_file_system_config=<sagemaker_core.main.utils.Unassigned object>, f_sx_lustre_file_system_config=<sagemaker_core.main.utils.Unassigned object>)

The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

Parameters:

• efs_file_system_config (EFSFileSystemConfig | None)

• f_sx_lustre_file_system_config (FSxLustreFileSystemConfig | None)

efs_file_system_config

Type:

The settings for a custom Amazon EFS file system.

f_sx_lustre_file_system_config

Type:

The settings for a custom Amazon FSx for Lustre file system.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CustomImage(*, image_name, app_image_config_name, image_version_number=<sagemaker_core.main.utils.Unassigned object>)

A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.

Parameters:

• image_name (str | object)

• app_image_config_name (str | object)

• image_version_number (int | None)

image_name

Type:

The name of the CustomImage. Must be unique to your account.

image_version_number

Type:

The version number of the CustomImage.

app_image_config_name

Type:

The name of the AppImageConfig.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CustomPosixUserConfig(*, uid, gid)

Details about the POSIX identity that is used for file system operations.

Parameters:

• uid (int)

• gid (int)

uid

Type:

The POSIX user ID.

gid

Type:

The POSIX group ID.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.CustomizedMetricSpecification(*, metric_name=<sagemaker_core.main.utils.Unassigned object>, namespace=<sagemaker_core.main.utils.Unassigned object>, statistic=<sagemaker_core.main.utils.Unassigned object>)

A customized metric.

Parameters:

• metric_name (str | None)

• namespace (str | None)

• statistic (str | None)

metric_name

Type:

The name of the customized metric.

namespace

Type:

The namespace of the customized metric.

statistic

Type:

The statistic of the customized metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataCaptureConfig(*, initial_sampling_percentage, destination_s3_uri, capture_options, enable_capture=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, capture_content_type_header=<sagemaker_core.main.utils.Unassigned object>)

Configuration to control how SageMaker AI captures inference data.

Parameters:

• initial_sampling_percentage (int)

• destination_s3_uri (str)

• capture_options (List[CaptureOption])

• enable_capture (bool | None)

• kms_key_id (str | None)

• capture_content_type_header (CaptureContentTypeHeader | None)

enable_capture

Type:

Whether data capture should be enabled or disabled (defaults to enabled).

initial_sampling_percentage

Type:

The percentage of requests SageMaker AI will capture. A lower value is recommended for Endpoints with high traffic.

destination_s3_uri

Type:

The Amazon S3 location used to capture the data.

kms_key_id

Type:

The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker AI uses to encrypt the captured data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

capture_options

Type:

Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

capture_content_type_header

Type:

Configuration specifying how to treat different headers. If no headers are specified SageMaker AI will by default base64 encode when capturing the data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataCaptureConfigSummary(*, enable_capture, capture_status, current_sampling_percentage, destination_s3_uri, kms_key_id)

The currently active data capture configuration used by your Endpoint.

Parameters:

• enable_capture (bool)

• capture_status (str)

• current_sampling_percentage (int)

• destination_s3_uri (str)

• kms_key_id (str)

enable_capture

Type:

Whether data capture is enabled or disabled.

capture_status

Type:

Whether data capture is currently functional.

current_sampling_percentage

Type:

The percentage of requests being captured by your Endpoint.

destination_s3_uri

Type:

The Amazon S3 location being used to capture the data.

kms_key_id

Type:

The KMS key being used to encrypt the data in Amazon S3.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataCatalogConfig(*, table_name, catalog, database)

The meta data of the Glue table which serves as data catalog for the OfflineStore.

Parameters:

• table_name (str)

• catalog (str)

• database (str)

table_name

Type:

The name of the Glue table.

catalog

Type:

The name of the Glue table catalog.

database

Type:

The name of the Glue table database.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataProcessing(*, input_filter=<sagemaker_core.main.utils.Unassigned object>, output_filter=<sagemaker_core.main.utils.Unassigned object>, join_source=<sagemaker_core.main.utils.Unassigned object>)

The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

Parameters:

• input_filter (str | None)

• output_filter (str | None)

• join_source (str | None)

input_filter

Type:

A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $. Examples: “$”, “$[1:]”, “$.features”

output_filter

Type:

A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren’t within the dimension size of the joined dataset, you get an error. Examples: “$”, “$[0,5:]”, “$[‘id’,’SageMakerOutput’]”

join_source

Type:

Specifies the source of the data to join with the transformed data. The valid values are None and Input. The default value is None, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input. You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file. For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput. For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file. For information on how joining in applied, see Workflow for Associating Inferences with Input Records.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataQualityAppSpecification(*, image_uri, container_entrypoint=<sagemaker_core.main.utils.Unassigned object>, container_arguments=<sagemaker_core.main.utils.Unassigned object>, record_preprocessor_source_uri=<sagemaker_core.main.utils.Unassigned object>, post_analytics_processor_source_uri=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>)

Information about the container that a data quality monitoring job runs.

Parameters:

• image_uri (str)

• container_entrypoint (List[str] | None)

• container_arguments (List[str] | None)

• record_preprocessor_source_uri (str | None)

• post_analytics_processor_source_uri (str | None)

• environment (Dict[str, str] | None)

image_uri

Type:

The container image that the data quality monitoring job runs.

container_entrypoint

Type:

The entrypoint for a container used to run a monitoring job.

container_arguments

Type:

The arguments to send to the container that the monitoring job runs.

record_preprocessor_source_uri

Type:

An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

post_analytics_processor_source_uri

Type:

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

environment

Type:

Sets the environment variables in the container that the monitoring job runs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataQualityBaselineConfig(*, baselining_job_name=<sagemaker_core.main.utils.Unassigned object>, constraints_resource=<sagemaker_core.main.utils.Unassigned object>, statistics_resource=<sagemaker_core.main.utils.Unassigned object>)

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Parameters:

• baselining_job_name (str | None)

• constraints_resource (MonitoringConstraintsResource | None)

• statistics_resource (MonitoringStatisticsResource | None)

baselining_job_name

Type:

The name of the job that performs baselining for the data quality monitoring job.

constraints_resource

Type:

sagemaker_core.main.shapes.MonitoringConstraintsResource | None

statistics_resource

Type:

sagemaker_core.main.shapes.MonitoringStatisticsResource | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataQualityJobInput(*, endpoint_input=<sagemaker_core.main.utils.Unassigned object>, batch_transform_input=<sagemaker_core.main.utils.Unassigned object>)

The input for the data quality monitoring job. Currently endpoints are supported for input.

Parameters:

• endpoint_input (EndpointInput | None)

• batch_transform_input (BatchTransformInput | None)

endpoint_input

Type:

sagemaker_core.main.shapes.EndpointInput | None

batch_transform_input

Type:

Input object for the batch transform job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DataSource(*, s3_data_source=<sagemaker_core.main.utils.Unassigned object>, file_system_data_source=<sagemaker_core.main.utils.Unassigned object>)

Describes the location of the channel data.

Parameters:

• s3_data_source (S3DataSource | None)

• file_system_data_source (FileSystemDataSource | None)

s3_data_source

Type:

The S3 location of the data source that is associated with a channel.

file_system_data_source

Type:

The file system that is associated with a channel.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DatasetDefinition(*, athena_dataset_definition=<sagemaker_core.main.utils.Unassigned object>, redshift_dataset_definition=<sagemaker_core.main.utils.Unassigned object>, local_path=<sagemaker_core.main.utils.Unassigned object>, data_distribution_type=<sagemaker_core.main.utils.Unassigned object>, input_mode=<sagemaker_core.main.utils.Unassigned object>)

Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.

Parameters:

• athena_dataset_definition (AthenaDatasetDefinition | None)

• redshift_dataset_definition (RedshiftDatasetDefinition | None)

• local_path (str | None)

• data_distribution_type (str | None)

• input_mode (str | None)

athena_dataset_definition

Type:

sagemaker_core.main.shapes.AthenaDatasetDefinition | None

redshift_dataset_definition

Type:

sagemaker_core.main.shapes.RedshiftDatasetDefinition | None

local_path

Type:

The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).

data_distribution_type

Type:

Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).

input_mode

Type:

Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DebugHookConfig(*, s3_output_path, local_path=<sagemaker_core.main.utils.Unassigned object>, hook_parameters=<sagemaker_core.main.utils.Unassigned object>, collection_configurations=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

Parameters:

• s3_output_path (str)

• local_path (str | None)

• hook_parameters (Dict[str, str] | None)

• collection_configurations (List[CollectionConfiguration] | None)

local_path

Type:

Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.

s3_output_path

Type:

Path to Amazon S3 storage location for metrics and tensors.

hook_parameters

Type:

Configuration information for the Amazon SageMaker Debugger hook parameters.

collection_configurations

Type:

Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DebugRuleConfiguration(*, rule_configuration_name, rule_evaluator_image, local_path=<sagemaker_core.main.utils.Unassigned object>, s3_output_path=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, rule_parameters=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

Parameters:

• rule_configuration_name (str)

• rule_evaluator_image (str)

• local_path (str | None)

• s3_output_path (str | None)

• instance_type (str | None)

• volume_size_in_gb (int | None)

• rule_parameters (Dict[str, str] | None)

rule_configuration_name

Type:

The name of the rule configuration. It must be unique relative to other rule configuration names.

local_path

Type:

Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.

s3_output_path

Type:

Path to Amazon S3 storage location for rules.

rule_evaluator_image

Type:

The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

instance_type

Type:

The instance type to deploy a custom rule for debugging a training job.

volume_size_in_gb

Type:

The size, in GB, of the ML storage volume attached to the processing instance.

rule_parameters

Type:

Runtime configuration for rule container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DebugRuleEvaluationStatus(*, rule_configuration_name=<sagemaker_core.main.utils.Unassigned object>, rule_evaluation_job_arn=<sagemaker_core.main.utils.Unassigned object>, rule_evaluation_status=<sagemaker_core.main.utils.Unassigned object>, status_details=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Information about the status of the rule evaluation.

Parameters:

• rule_configuration_name (str | None)

• rule_evaluation_job_arn (str | None)

• rule_evaluation_status (str | None)

• status_details (str | None)

• last_modified_time (datetime | None)

rule_configuration_name

Type:

The name of the rule configuration.

rule_evaluation_job_arn

Type:

The Amazon Resource Name (ARN) of the rule evaluation job.

rule_evaluation_status

Type:

Status of the rule evaluation.

status_details

Type:

Details from the rule evaluation.

last_modified_time

Type:

Timestamp when the rule evaluation status was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DefaultEbsStorageSettings(*, default_ebs_volume_size_in_gb, maximum_ebs_volume_size_in_gb)

A collection of default EBS storage settings that apply to spaces created within a domain or user profile.

Parameters:

• default_ebs_volume_size_in_gb (int)

• maximum_ebs_volume_size_in_gb (int)

default_ebs_volume_size_in_gb

Type:

The default size of the EBS storage volume for a space.

maximum_ebs_volume_size_in_gb

Type:

The maximum size of the EBS storage volume for a space.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DefaultSpaceSettings(*, execution_role=<sagemaker_core.main.utils.Unassigned object>, security_groups=<sagemaker_core.main.utils.Unassigned object>, jupyter_server_app_settings=<sagemaker_core.main.utils.Unassigned object>, kernel_gateway_app_settings=<sagemaker_core.main.utils.Unassigned object>, jupyter_lab_app_settings=<sagemaker_core.main.utils.Unassigned object>, space_storage_settings=<sagemaker_core.main.utils.Unassigned object>, custom_posix_user_config=<sagemaker_core.main.utils.Unassigned object>, custom_file_system_configs=<sagemaker_core.main.utils.Unassigned object>)

The default settings for shared spaces that users create in the domain. SageMaker applies these settings only to shared spaces. It doesn’t apply them to private spaces.

Parameters:

• execution_role (str | None)

• security_groups (List[str] | None)

• jupyter_server_app_settings (JupyterServerAppSettings | None)

• kernel_gateway_app_settings (KernelGatewayAppSettings | None)

• jupyter_lab_app_settings (JupyterLabAppSettings | None)

• space_storage_settings (DefaultSpaceStorageSettings | None)

• custom_posix_user_config (CustomPosixUserConfig | None)

• custom_file_system_configs (List[CustomFileSystemConfig] | None)

execution_role

Type:

The ARN of the execution role for the space.

security_groups

Type:

The security group IDs for the Amazon VPC that the space uses for communication.

jupyter_server_app_settings

Type:

sagemaker_core.main.shapes.JupyterServerAppSettings | None

kernel_gateway_app_settings

Type:

sagemaker_core.main.shapes.KernelGatewayAppSettings | None

jupyter_lab_app_settings

Type:

sagemaker_core.main.shapes.JupyterLabAppSettings | None

space_storage_settings

Type:

sagemaker_core.main.shapes.DefaultSpaceStorageSettings | None

custom_posix_user_config

Type:

sagemaker_core.main.shapes.CustomPosixUserConfig | None

custom_file_system_configs

Type:

The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DefaultSpaceStorageSettings(*, default_ebs_storage_settings=<sagemaker_core.main.utils.Unassigned object>)

The default storage settings for a space.

Parameters:

default_ebs_storage_settings (DefaultEbsStorageSettings | None)

default_ebs_storage_settings

Type:

The default EBS storage settings for a space.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeployedImage(*, specified_image=<sagemaker_core.main.utils.Unassigned object>, resolved_image=<sagemaker_core.main.utils.Unassigned object>, resolution_time=<sagemaker_core.main.utils.Unassigned object>)

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant. If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

Parameters:

• specified_image (str | None)

• resolved_image (str | None)

• resolution_time (datetime | None)

specified_image

Type:

The image path you specified when you created the model.

resolved_image

Type:

The specific digest path of the image hosted in this ProductionVariant.

resolution_time

Type:

The date and time when the image path for the model resolved to the ResolvedImage

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeploymentConfig(*, blue_green_update_policy=<sagemaker_core.main.utils.Unassigned object>, rolling_update_policy=<sagemaker_core.main.utils.Unassigned object>, auto_rollback_configuration=<sagemaker_core.main.utils.Unassigned object>)

The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

Parameters:

• blue_green_update_policy (BlueGreenUpdatePolicy | None)

• rolling_update_policy (RollingUpdatePolicy | None)

• auto_rollback_configuration (AutoRollbackConfig | None)

blue_green_update_policy

Type:

Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

rolling_update_policy

Type:

Specifies a rolling deployment strategy for updating a SageMaker endpoint.

auto_rollback_configuration

Type:

Automatic rollback configuration for handling endpoint deployment failures and recovery.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeploymentRecommendation(*, recommendation_status, real_time_inference_recommendations=<sagemaker_core.main.utils.Unassigned object>)

A set of recommended deployment configurations for the model. To get more advanced recommendations, see CreateInferenceRecommendationsJob to create an inference recommendation job.

Parameters:

• recommendation_status (str)

• real_time_inference_recommendations (List[RealTimeInferenceRecommendation] | None)

recommendation_status

Type:

Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS, retry your API call after a few seconds to get a COMPLETED deployment recommendation.

real_time_inference_recommendations

Type:

A list of RealTimeInferenceRecommendation items.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeploymentStage(*, stage_name, device_selection_config, deployment_config=<sagemaker_core.main.utils.Unassigned object>)

Contains information about a stage in an edge deployment plan.

Parameters:

• stage_name (str)

• device_selection_config (DeviceSelectionConfig)

• deployment_config (EdgeDeploymentConfig | None)

stage_name

Type:

The name of the stage.

device_selection_config

Type:

Configuration of the devices in the stage.

deployment_config

Type:

Configuration of the deployment details.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeploymentStageStatusSummary(*, stage_name, device_selection_config, deployment_config, deployment_status)

Contains information summarizing the deployment stage results.

Parameters:

• stage_name (str)

• device_selection_config (DeviceSelectionConfig)

• deployment_config (EdgeDeploymentConfig)

• deployment_status (EdgeDeploymentStatus)

stage_name

Type:

The name of the stage.

device_selection_config

Type:

Configuration of the devices in the stage.

deployment_config

Type:

Configuration of the deployment details.

deployment_status

Type:

General status of the current state.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DerivedInformation(*, derived_data_input_config=<sagemaker_core.main.utils.Unassigned object>)

Information that SageMaker Neo automatically derived about the model.

Parameters:

derived_data_input_config (str | None)

derived_data_input_config

Type:

The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don’t need to specify the data input configuration when you create a compilation job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DescribePipelineDefinitionForExecutionResponse(*, pipeline_definition=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• pipeline_definition (str | None)

• creation_time (datetime | None)

pipeline_definition

Type:

The JSON pipeline definition.

creation_time

Type:

The time when the pipeline was created.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DesiredWeightAndCapacity(*, variant_name, desired_weight=<sagemaker_core.main.utils.Unassigned object>, desired_instance_count=<sagemaker_core.main.utils.Unassigned object>, serverless_update_config=<sagemaker_core.main.utils.Unassigned object>)

Specifies weight and capacity values for a production variant.

Parameters:

• variant_name (str)

• desired_weight (float | None)

• desired_instance_count (int | None)

• serverless_update_config (ProductionVariantServerlessUpdateConfig | None)

variant_name

Type:

The name of the variant to update.

desired_weight

Type:

The variant’s weight.

desired_instance_count

Type:

The variant’s capacity.

serverless_update_config

Type:

Specifies the serverless update concurrency configuration for an endpoint variant.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Device(*, device_name, description=<sagemaker_core.main.utils.Unassigned object>, iot_thing_name=<sagemaker_core.main.utils.Unassigned object>)

Information of a particular device.

Parameters:

• device_name (str | object)

• description (str | None)

• iot_thing_name (str | None)

device_name

Type:

The name of the device.

description

Type:

Description of the device.

iot_thing_name

Type:

Amazon Web Services Internet of Things (IoT) object name.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeviceDeploymentSummary(*, edge_deployment_plan_arn, edge_deployment_plan_name, stage_name, device_name, device_arn, deployed_stage_name=<sagemaker_core.main.utils.Unassigned object>, device_fleet_name=<sagemaker_core.main.utils.Unassigned object>, device_deployment_status=<sagemaker_core.main.utils.Unassigned object>, device_deployment_status_message=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, deployment_start_time=<sagemaker_core.main.utils.Unassigned object>)

Contains information summarizing device details and deployment status.

Parameters:

• edge_deployment_plan_arn (str)

• edge_deployment_plan_name (str | object)

• stage_name (str)

• device_name (str | object)

• device_arn (str)

• deployed_stage_name (str | None)

• device_fleet_name (str | object | None)

• device_deployment_status (str | None)

• device_deployment_status_message (str | None)

• description (str | None)

• deployment_start_time (datetime | None)

edge_deployment_plan_arn

Type:

The ARN of the edge deployment plan.

edge_deployment_plan_name

Type:

The name of the edge deployment plan.

stage_name

Type:

The name of the stage in the edge deployment plan.

deployed_stage_name

Type:

The name of the deployed stage.

device_fleet_name

Type:

The name of the fleet to which the device belongs to.

device_name

Type:

The name of the device.

device_arn

Type:

The ARN of the device.

device_deployment_status

Type:

The deployment status of the device.

device_deployment_status_message

Type:

The detailed error message for the deployoment status result.

description

Type:

The description of the device.

deployment_start_time

Type:

The time when the deployment on the device started.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeviceFleetSummary(*, device_fleet_arn, device_fleet_name, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Summary of the device fleet.

Parameters:

• device_fleet_arn (str)

• device_fleet_name (str | object)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

device_fleet_arn

Type:

Amazon Resource Name (ARN) of the device fleet.

device_fleet_name

Type:

Name of the device fleet.

creation_time

Type:

Timestamp of when the device fleet was created.

last_modified_time

Type:

Timestamp of when the device fleet was last updated.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeviceSelectionConfig(*, device_subset_type, percentage=<sagemaker_core.main.utils.Unassigned object>, device_names=<sagemaker_core.main.utils.Unassigned object>, device_name_contains=<sagemaker_core.main.utils.Unassigned object>)

Contains information about the configurations of selected devices.

Parameters:

• device_subset_type (str)

• percentage (int | None)

• device_names (List[str] | None)

• device_name_contains (str | None)

device_subset_type

Type:

Type of device subsets to deploy to the current stage.

percentage

Type:

Percentage of devices in the fleet to deploy to the current stage.

device_names

Type:

List of devices chosen to deploy.

device_name_contains

Type:

A filter to select devices with names containing this name.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeviceStats(*, connected_device_count, registered_device_count)

Status of devices.

Parameters:

• connected_device_count (int)

• registered_device_count (int)

connected_device_count

Type:

The number of devices connected with a heartbeat.

registered_device_count

Type:

The number of registered devices.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DeviceSummary(*, device_name, device_arn, description=<sagemaker_core.main.utils.Unassigned object>, device_fleet_name=<sagemaker_core.main.utils.Unassigned object>, iot_thing_name=<sagemaker_core.main.utils.Unassigned object>, registration_time=<sagemaker_core.main.utils.Unassigned object>, latest_heartbeat=<sagemaker_core.main.utils.Unassigned object>, models=<sagemaker_core.main.utils.Unassigned object>, agent_version=<sagemaker_core.main.utils.Unassigned object>)

Summary of the device.

Parameters:

• device_name (str | object)

• device_arn (str)

• description (str | None)

• device_fleet_name (str | object | None)

• iot_thing_name (str | None)

• registration_time (datetime | None)

• latest_heartbeat (datetime | None)

• models (List[EdgeModelSummary] | None)

• agent_version (str | None)

device_name

Type:

The unique identifier of the device.

device_arn

Type:

Amazon Resource Name (ARN) of the device.

description

Type:

A description of the device.

device_fleet_name

Type:

The name of the fleet the device belongs to.

iot_thing_name

Type:

The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..

registration_time

Type:

The timestamp of the last registration or de-reregistration.

latest_heartbeat

Type:

The last heartbeat received from the device.

models

Type:

Models on the device.

agent_version

Type:

Edge Manager agent version.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DirectDeploySettings(*, status=<sagemaker_core.main.utils.Unassigned object>)

The model deployment settings for the SageMaker Canvas application. In order to enable model deployment for Canvas, the SageMaker Domain’s or user profile’s Amazon Web Services IAM execution role must have the AmazonSageMakerCanvasDirectDeployAccess policy attached. You can also turn on model deployment permissions through the SageMaker Domain’s or user profile’s settings in the SageMaker console.

Parameters:

status (str | None)

status

Type:

Describes whether model deployment permissions are enabled or disabled in the Canvas application.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DockerSettings(*, enable_docker_access=<sagemaker_core.main.utils.Unassigned object>, vpc_only_trusted_accounts=<sagemaker_core.main.utils.Unassigned object>)

A collection of settings that configure the domain’s Docker interaction.

Parameters:

• enable_docker_access (str | None)

• vpc_only_trusted_accounts (List[str] | None)

enable_docker_access

Type:

Indicates whether the domain can access Docker.

vpc_only_trusted_accounts

Type:

The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DomainDetails(*, domain_arn=<sagemaker_core.main.utils.Unassigned object>, domain_id=<sagemaker_core.main.utils.Unassigned object>, domain_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, url=<sagemaker_core.main.utils.Unassigned object>)

The domain’s details.

Parameters:

• domain_arn (str | None)

• domain_id (str | None)

• domain_name (str | object | None)

• status (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• url (str | None)

domain_arn

Type:

The domain’s Amazon Resource Name (ARN).

domain_id

Type:

The domain ID.

domain_name

Type:

The domain name.

status

Type:

The status.

creation_time

Type:

The creation time.

last_modified_time

Type:

The last modified time.

url

Type:

The domain’s URL.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DomainSettings(*, security_group_ids=<sagemaker_core.main.utils.Unassigned object>, r_studio_server_pro_domain_settings=<sagemaker_core.main.utils.Unassigned object>, execution_role_identity_config=<sagemaker_core.main.utils.Unassigned object>, docker_settings=<sagemaker_core.main.utils.Unassigned object>, amazon_q_settings=<sagemaker_core.main.utils.Unassigned object>)

A collection of settings that apply to the SageMaker Domain. These settings are specified through the CreateDomain API call.

Parameters:

• security_group_ids (List[str] | None)

• r_studio_server_pro_domain_settings (RStudioServerProDomainSettings | None)

• execution_role_identity_config (str | None)

• docker_settings (DockerSettings | None)

• amazon_q_settings (AmazonQSettings | None)

security_group_ids

Type:

The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

r_studio_server_pro_domain_settings

Type:

A collection of settings that configure the RStudioServerPro Domain-level app.

execution_role_identity_config

Type:

The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key.

docker_settings

Type:

A collection of settings that configure the domain’s Docker interaction.

amazon_q_settings

Type:

A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DomainSettingsForUpdate(*, r_studio_server_pro_domain_settings_for_update=<sagemaker_core.main.utils.Unassigned object>, execution_role_identity_config=<sagemaker_core.main.utils.Unassigned object>, security_group_ids=<sagemaker_core.main.utils.Unassigned object>, docker_settings=<sagemaker_core.main.utils.Unassigned object>, amazon_q_settings=<sagemaker_core.main.utils.Unassigned object>)

A collection of Domain configuration settings to update.

Parameters:

• r_studio_server_pro_domain_settings_for_update (RStudioServerProDomainSettingsForUpdate | None)

• execution_role_identity_config (str | None)

• security_group_ids (List[str] | None)

• docker_settings (DockerSettings | None)

• amazon_q_settings (AmazonQSettings | None)

r_studio_server_pro_domain_settings_for_update

Type:

A collection of RStudioServerPro Domain-level app settings to update. A single RStudioServerPro application is created for a domain.

execution_role_identity_config

Type:

The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the InService or Pending state.

security_group_ids

Type:

The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

docker_settings

Type:

A collection of settings that configure the domain’s Docker interaction.

amazon_q_settings

Type:

A collection of settings that configure the Amazon Q experience within the domain.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DriftCheckBaselines(*, bias=<sagemaker_core.main.utils.Unassigned object>, explainability=<sagemaker_core.main.utils.Unassigned object>, model_quality=<sagemaker_core.main.utils.Unassigned object>, model_data_quality=<sagemaker_core.main.utils.Unassigned object>)

Represents the drift check baselines that can be used when the model monitor is set using the model package.

Parameters:

• bias (DriftCheckBias | None)

• explainability (DriftCheckExplainability | None)

• model_quality (DriftCheckModelQuality | None)

• model_data_quality (DriftCheckModelDataQuality | None)

bias

Type:

Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

explainability

Type:

Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

model_quality

Type:

Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

model_data_quality

Type:

Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DriftCheckBias(*, config_file=<sagemaker_core.main.utils.Unassigned object>, pre_training_constraints=<sagemaker_core.main.utils.Unassigned object>, post_training_constraints=<sagemaker_core.main.utils.Unassigned object>)

Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

Parameters:

• config_file (FileSource | None)

• pre_training_constraints (MetricsSource | None)

• post_training_constraints (MetricsSource | None)

config_file

Type:

The bias config file for a model.

pre_training_constraints

Type:

The pre-training constraints.

post_training_constraints

Type:

The post-training constraints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DriftCheckExplainability(*, constraints=<sagemaker_core.main.utils.Unassigned object>, config_file=<sagemaker_core.main.utils.Unassigned object>)

Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

Parameters:

• constraints (MetricsSource | None)

• config_file (FileSource | None)

constraints

Type:

The drift check explainability constraints.

config_file

Type:

The explainability config file for the model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DriftCheckModelDataQuality(*, statistics=<sagemaker_core.main.utils.Unassigned object>, constraints=<sagemaker_core.main.utils.Unassigned object>)

Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.

Parameters:

• statistics (MetricsSource | None)

• constraints (MetricsSource | None)

statistics

Type:

The drift check model data quality statistics.

constraints

Type:

The drift check model data quality constraints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DriftCheckModelQuality(*, statistics=<sagemaker_core.main.utils.Unassigned object>, constraints=<sagemaker_core.main.utils.Unassigned object>)

Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

Parameters:

• statistics (MetricsSource | None)

• constraints (MetricsSource | None)

statistics

Type:

The drift check model quality statistics.

constraints

Type:

The drift check model quality constraints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.DynamicScalingConfiguration(*, min_capacity=<sagemaker_core.main.utils.Unassigned object>, max_capacity=<sagemaker_core.main.utils.Unassigned object>, scale_in_cooldown=<sagemaker_core.main.utils.Unassigned object>, scale_out_cooldown=<sagemaker_core.main.utils.Unassigned object>, scaling_policies=<sagemaker_core.main.utils.Unassigned object>)

An object with the recommended values for you to specify when creating an autoscaling policy.

Parameters:

• min_capacity (int | None)

• max_capacity (int | None)

• scale_in_cooldown (int | None)

• scale_out_cooldown (int | None)

• scaling_policies (List[ScalingPolicy] | None)

min_capacity

Type:

The recommended minimum capacity to specify for your autoscaling policy.

max_capacity

Type:

The recommended maximum capacity to specify for your autoscaling policy.

scale_in_cooldown

Type:

The recommended scale in cooldown time for your autoscaling policy.

scale_out_cooldown

Type:

The recommended scale out cooldown time for your autoscaling policy.

scaling_policies

Type:

An object of the scaling policies for each metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EFSFileSystem(*, file_system_id)

A file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

Parameters:

file_system_id (str)

file_system_id

Type:

The ID of your Amazon EFS file system.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EFSFileSystemConfig(*, file_system_id, file_system_path=<sagemaker_core.main.utils.Unassigned object>)

The settings for assigning a custom Amazon EFS file system to a user profile or space for an Amazon SageMaker AI Domain.

Parameters:

• file_system_id (str)

• file_system_path (str | None)

file_system_id

Type:

The ID of your Amazon EFS file system.

file_system_path

Type:

The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EMRStepMetadata(*, cluster_id=<sagemaker_core.main.utils.Unassigned object>, step_id=<sagemaker_core.main.utils.Unassigned object>, step_name=<sagemaker_core.main.utils.Unassigned object>, log_file_path=<sagemaker_core.main.utils.Unassigned object>)

The configurations and outcomes of an Amazon EMR step execution.

Parameters:

• cluster_id (str | None)

• step_id (str | None)

• step_name (str | None)

• log_file_path (str | None)

cluster_id

Type:

The identifier of the EMR cluster.

step_id

Type:

The identifier of the EMR cluster step.

step_name

Type:

The name of the EMR cluster step.

log_file_path

Type:

The path to the log file where the cluster step’s failure root cause is recorded.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EbsStorageSettings(*, ebs_volume_size_in_gb)

A collection of EBS storage settings that apply to both private and shared spaces.

Parameters:

ebs_volume_size_in_gb (int)

ebs_volume_size_in_gb

Type:

The size of an EBS storage volume for a space.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Edge(*, source_arn=<sagemaker_core.main.utils.Unassigned object>, destination_arn=<sagemaker_core.main.utils.Unassigned object>, association_type=<sagemaker_core.main.utils.Unassigned object>)

A directed edge connecting two lineage entities.

Parameters:

• source_arn (str | None)

• destination_arn (str | None)

• association_type (str | None)

source_arn

Type:

The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.

destination_arn

Type:

The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.

association_type

Type:

The type of the Association(Edge) between the source and destination. For example ContributedTo, Produced, or DerivedFrom.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeDeploymentConfig(*, failure_handling_policy)

Contains information about the configuration of a deployment.

Parameters:

failure_handling_policy (str)

failure_handling_policy

Type:

Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeDeploymentModelConfig(*, model_handle, edge_packaging_job_name)

Contains information about the configuration of a model in a deployment.

Parameters:

• model_handle (str)

• edge_packaging_job_name (str | object)

model_handle

Type:

The name the device application uses to reference this model.

edge_packaging_job_name

Type:

The edge packaging job associated with this deployment.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeDeploymentPlanSummary(*, edge_deployment_plan_arn, edge_deployment_plan_name, device_fleet_name, edge_deployment_success, edge_deployment_pending, edge_deployment_failed, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Contains information summarizing an edge deployment plan.

Parameters:

• edge_deployment_plan_arn (str)

• edge_deployment_plan_name (str | object)

• device_fleet_name (str | object)

• edge_deployment_success (int)

• edge_deployment_pending (int)

• edge_deployment_failed (int)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

edge_deployment_plan_arn

Type:

The ARN of the edge deployment plan.

edge_deployment_plan_name

Type:

The name of the edge deployment plan.

device_fleet_name

Type:

The name of the device fleet used for the deployment.

edge_deployment_success

Type:

The number of edge devices with the successful deployment.

edge_deployment_pending

Type:

The number of edge devices yet to pick up the deployment, or in progress.

edge_deployment_failed

Type:

The number of edge devices that failed the deployment.

creation_time

Type:

The time when the edge deployment plan was created.

last_modified_time

Type:

The time when the edge deployment plan was last updated.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeDeploymentStatus(*, stage_status, edge_deployment_success_in_stage, edge_deployment_pending_in_stage, edge_deployment_failed_in_stage, edge_deployment_status_message=<sagemaker_core.main.utils.Unassigned object>, edge_deployment_stage_start_time=<sagemaker_core.main.utils.Unassigned object>)

Contains information summarizing the deployment stage results.

Parameters:

• stage_status (str)

• edge_deployment_success_in_stage (int)

• edge_deployment_pending_in_stage (int)

• edge_deployment_failed_in_stage (int)

• edge_deployment_status_message (str | None)

• edge_deployment_stage_start_time (datetime | None)

stage_status

Type:

The general status of the current stage.

edge_deployment_success_in_stage

Type:

The number of edge devices with the successful deployment in the current stage.

edge_deployment_pending_in_stage

Type:

The number of edge devices yet to pick up the deployment in current stage, or in progress.

edge_deployment_failed_in_stage

Type:

The number of edge devices that failed the deployment in current stage.

edge_deployment_status_message

Type:

A detailed message about deployment status in current stage.

edge_deployment_stage_start_time

Type:

The time when the deployment API started.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeModel(*, model_name, model_version, latest_sample_time=<sagemaker_core.main.utils.Unassigned object>, latest_inference=<sagemaker_core.main.utils.Unassigned object>)

The model on the edge device.

Parameters:

• model_name (str | object)

• model_version (str)

• latest_sample_time (datetime | None)

• latest_inference (datetime | None)

model_name

Type:

The name of the model.

model_version

Type:

The model version.

latest_sample_time

Type:

The timestamp of the last data sample taken.

latest_inference

Type:

The timestamp of the last inference that was made.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeModelStat(*, model_name, model_version, offline_device_count, connected_device_count, active_device_count, sampling_device_count)

Status of edge devices with this model.

Parameters:

• model_name (str | object)

• model_version (str)

• offline_device_count (int)

• connected_device_count (int)

• active_device_count (int)

• sampling_device_count (int)

model_name

Type:

The name of the model.

model_version

Type:

The model version.

offline_device_count

Type:

The number of devices that have this model version and do not have a heart beat.

connected_device_count

Type:

The number of devices that have this model version and have a heart beat.

active_device_count

Type:

The number of devices that have this model version, a heart beat, and are currently running.

sampling_device_count

Type:

The number of devices with this model version and are producing sample data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeModelSummary(*, model_name, model_version)

Summary of model on edge device.

Parameters:

• model_name (str | object)

• model_version (str)

model_name

Type:

The name of the model.

model_version

Type:

The version model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgeOutputConfig(*, s3_output_location, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, preset_deployment_type=<sagemaker_core.main.utils.Unassigned object>, preset_deployment_config=<sagemaker_core.main.utils.Unassigned object>)

The output configuration.

Parameters:

• s3_output_location (str)

• kms_key_id (str | None)

• preset_deployment_type (str | None)

• preset_deployment_config (str | None)

s3_output_location

Type:

The Amazon Simple Storage (S3) bucker URI.

kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account.

preset_deployment_type

Type:

The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

preset_deployment_config

Type:

The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are: ComponentName (optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of “SagemakerEdgeManager” and the name of your SageMaker Edge Manager packaging job. ComponentDescription (optional) - Description of the component. ComponentVersion (optional) - The version of the component. Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. PlatformOS (optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. PlatformArchitecture (optional) - The processor architecture for the platform. Supported architectures Windows include: Windows32_x86, Windows64_x64. Supported architectures for Linux include: Linux x86_64, Linux ARMV8.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgePackagingJobSummary(*, edge_packaging_job_arn, edge_packaging_job_name, edge_packaging_job_status, compilation_job_name=<sagemaker_core.main.utils.Unassigned object>, model_name=<sagemaker_core.main.utils.Unassigned object>, model_version=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Summary of edge packaging job.

Parameters:

• edge_packaging_job_arn (str)

• edge_packaging_job_name (str | object)

• edge_packaging_job_status (str)

• compilation_job_name (str | object | None)

• model_name (str | object | None)

• model_version (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

edge_packaging_job_arn

Type:

The Amazon Resource Name (ARN) of the edge packaging job.

edge_packaging_job_name

Type:

The name of the edge packaging job.

edge_packaging_job_status

Type:

The status of the edge packaging job.

compilation_job_name

Type:

The name of the SageMaker Neo compilation job.

model_name

Type:

The name of the model.

model_version

Type:

The version of the model.

creation_time

Type:

The timestamp of when the job was created.

last_modified_time

Type:

The timestamp of when the edge packaging job was last updated.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EdgePresetDeploymentOutput(*, type, artifact=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, status_message=<sagemaker_core.main.utils.Unassigned object>)

The output of a SageMaker Edge Manager deployable resource.

Parameters:

• type (str)

• artifact (str | None)

• status (str | None)

• status_message (str | None)

type

Type:

The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

artifact

Type:

The Amazon Resource Name (ARN) of the generated deployable resource.

status

Type:

The status of the deployable resource.

status_message

Type:

Returns a message describing the status of the deployed resource.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EmrServerlessComputeConfig(*, execution_role_arn)

This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment. Specifies the compute configuration for the EMR Serverless job.

Parameters:

execution_role_arn (str)

execution_role_arn

Type:

The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see “How to configure AutoML to initiate a remote job on EMR Serverless for large datasets” in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EmrServerlessSettings(*, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>)

The settings for running Amazon EMR Serverless jobs in SageMaker Canvas.

Parameters:

• execution_role_arn (str | None)

• status (str | None)

execution_role_arn

Type:

The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.

status

Type:

Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EmrSettings(*, assumable_role_arns=<sagemaker_core.main.utils.Unassigned object>, execution_role_arns=<sagemaker_core.main.utils.Unassigned object>)

The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.

Parameters:

• assumable_role_arns (List[str] | None)

• execution_role_arns (List[str] | None)

assumable_role_arns

Type:

An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.

execution_role_arns

Type:

An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Endpoint(*, endpoint_name, endpoint_arn, endpoint_config_name, endpoint_status, creation_time, last_modified_time, production_variants=<sagemaker_core.main.utils.Unassigned object>, data_capture_config=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedules=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, shadow_production_variants=<sagemaker_core.main.utils.Unassigned object>)

A hosted endpoint for real-time inference.

Parameters:

• endpoint_name (str | object)

• endpoint_arn (str)

• endpoint_config_name (str | object)

• endpoint_status (str)

• creation_time (datetime)

• last_modified_time (datetime)

• production_variants (List[ProductionVariantSummary] | None)

• data_capture_config (DataCaptureConfigSummary | None)

• failure_reason (str | None)

• monitoring_schedules (List[MonitoringSchedule] | None)

• tags (List[Tag] | None)

• shadow_production_variants (List[ProductionVariantSummary] | None)

endpoint_name

Type:

The name of the endpoint.

endpoint_arn

Type:

The Amazon Resource Name (ARN) of the endpoint.

endpoint_config_name

Type:

The endpoint configuration associated with the endpoint.

production_variants

Type:

A list of the production variants hosted on the endpoint. Each production variant is a model.

data_capture_config

Type:

sagemaker_core.main.shapes.DataCaptureConfigSummary | None

endpoint_status

Type:

The status of the endpoint.

failure_reason

Type:

If the endpoint failed, the reason it failed.

creation_time

Type:

The time that the endpoint was created.

last_modified_time

Type:

The last time the endpoint was modified.

monitoring_schedules

Type:

A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.

tags

Type:

A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

shadow_production_variants

Type:

A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointConfigStepMetadata(*, arn=<sagemaker_core.main.utils.Unassigned object>)

Metadata for an endpoint configuration step.

Parameters:

arn (str | None)

arn

Type:

The Amazon Resource Name (ARN) of the endpoint configuration used in the step.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointConfigSummary(*, endpoint_config_name, endpoint_config_arn, creation_time)

Provides summary information for an endpoint configuration.

Parameters:

• endpoint_config_name (str | object)

• endpoint_config_arn (str)

• creation_time (datetime)

endpoint_config_name

Type:

The name of the endpoint configuration.

endpoint_config_arn

Type:

The Amazon Resource Name (ARN) of the endpoint configuration.

creation_time

Type:

A timestamp that shows when the endpoint configuration was created.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointInfo(*, endpoint_name=<sagemaker_core.main.utils.Unassigned object>)

Details about a customer endpoint that was compared in an Inference Recommender job.

Parameters:

endpoint_name (str | object | None)

endpoint_name

Type:

The name of a customer’s endpoint.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointInput(*, endpoint_name, local_path, s3_input_mode=<sagemaker_core.main.utils.Unassigned object>, s3_data_distribution_type=<sagemaker_core.main.utils.Unassigned object>, features_attribute=<sagemaker_core.main.utils.Unassigned object>, inference_attribute=<sagemaker_core.main.utils.Unassigned object>, probability_attribute=<sagemaker_core.main.utils.Unassigned object>, probability_threshold_attribute=<sagemaker_core.main.utils.Unassigned object>, start_time_offset=<sagemaker_core.main.utils.Unassigned object>, end_time_offset=<sagemaker_core.main.utils.Unassigned object>, exclude_features_attribute=<sagemaker_core.main.utils.Unassigned object>)

Input object for the endpoint

Parameters:

• endpoint_name (str | object)

• local_path (str)

• s3_input_mode (str | None)

• s3_data_distribution_type (str | None)

• features_attribute (str | None)

• inference_attribute (str | None)

• probability_attribute (str | None)

• probability_threshold_attribute (float | None)

• start_time_offset (str | None)

• end_time_offset (str | None)

• exclude_features_attribute (str | None)

endpoint_name

Type:

An endpoint in customer’s account which has enabled DataCaptureConfig enabled.

local_path

Type:

Path to the filesystem where the endpoint data is available to the container.

s3_input_mode

Type:

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

s3_data_distribution_type

Type:

Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated

features_attribute

Type:

The attributes of the input data that are the input features.

inference_attribute

Type:

The attribute of the input data that represents the ground truth label.

probability_attribute

Type:

In a classification problem, the attribute that represents the class probability.

probability_threshold_attribute

Type:

The threshold for the class probability to be evaluated as a positive result.

start_time_offset

Type:

If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

end_time_offset

Type:

If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

exclude_features_attribute

Type:

The attributes of the input data to exclude from the analysis.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointInputConfiguration(*, instance_type=<sagemaker_core.main.utils.Unassigned object>, serverless_config=<sagemaker_core.main.utils.Unassigned object>, inference_specification_name=<sagemaker_core.main.utils.Unassigned object>, environment_parameter_ranges=<sagemaker_core.main.utils.Unassigned object>)

The endpoint configuration for the load test.

Parameters:

• instance_type (str | None)

• serverless_config (ProductionVariantServerlessConfig | None)

• inference_specification_name (str | None)

• environment_parameter_ranges (EnvironmentParameterRanges | None)

instance_type

Type:

The instance types to use for the load test.

serverless_config

Type:

sagemaker_core.main.shapes.ProductionVariantServerlessConfig | None

inference_specification_name

Type:

The inference specification name in the model package version.

environment_parameter_ranges

Type:

The parameter you want to benchmark against.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointMetadata(*, endpoint_name, endpoint_config_name=<sagemaker_core.main.utils.Unassigned object>, endpoint_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

The metadata of the endpoint.

Parameters:

• endpoint_name (str | object)

• endpoint_config_name (str | object | None)

• endpoint_status (str | None)

• failure_reason (str | None)

endpoint_name

Type:

The name of the endpoint.

endpoint_config_name

Type:

The name of the endpoint configuration.

endpoint_status

Type:

The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary.

failure_reason

Type:

If the status of the endpoint is Failed, or the status is InService but update operation fails, this provides the reason why it failed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointOutputConfiguration(*, endpoint_name, variant_name, instance_type=<sagemaker_core.main.utils.Unassigned object>, initial_instance_count=<sagemaker_core.main.utils.Unassigned object>, serverless_config=<sagemaker_core.main.utils.Unassigned object>)

The endpoint configuration made by Inference Recommender during a recommendation job.

Parameters:

• endpoint_name (str | object)

• variant_name (str)

• instance_type (str | None)

• initial_instance_count (int | None)

• serverless_config (ProductionVariantServerlessConfig | None)

endpoint_name

Type:

The name of the endpoint made during a recommendation job.

variant_name

Type:

The name of the production variant (deployed model) made during a recommendation job.

instance_type

Type:

The instance type recommended by Amazon SageMaker Inference Recommender.

initial_instance_count

Type:

The number of instances recommended to launch initially.

serverless_config

Type:

sagemaker_core.main.shapes.ProductionVariantServerlessConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointPerformance(*, metrics, endpoint_info)

The performance results from running an Inference Recommender job on an existing endpoint.

Parameters:

• metrics (InferenceMetrics)

• endpoint_info (EndpointInfo)

metrics

Type:

The metrics for an existing endpoint.

endpoint_info

Type:

sagemaker_core.main.shapes.EndpointInfo

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointStepMetadata(*, arn=<sagemaker_core.main.utils.Unassigned object>)

Metadata for an endpoint step.

Parameters:

arn (str | None)

arn

Type:

The Amazon Resource Name (ARN) of the endpoint in the step.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EndpointSummary(*, endpoint_name, endpoint_arn, creation_time, last_modified_time, endpoint_status)

Provides summary information for an endpoint.

Parameters:

• endpoint_name (str | object)

• endpoint_arn (str)

• creation_time (datetime)

• last_modified_time (datetime)

• endpoint_status (str)

endpoint_name

Type:

The name of the endpoint.

endpoint_arn

Type:

The Amazon Resource Name (ARN) of the endpoint.

creation_time

Type:

A timestamp that shows when the endpoint was created.

last_modified_time

Type:

A timestamp that shows when the endpoint was last modified.

endpoint_status

Type:

The status of the endpoint. OutOfService: Endpoint is not available to take incoming requests. Creating: CreateEndpoint is executing. Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. InService: Endpoint is available to process incoming requests. Deleting: DeleteEndpoint is executing. Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. To get a list of endpoints with a specified status, use the StatusEquals filter with a call to ListEndpoints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EnvironmentParameter(*, key, value_type, value)

A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.

Parameters:

• key (str)

• value_type (str)

• value (str)

key

Type:

The environment key suggested by the Amazon SageMaker Inference Recommender.

value_type

Type:

The value type suggested by the Amazon SageMaker Inference Recommender.

value

Type:

The value suggested by the Amazon SageMaker Inference Recommender.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.EnvironmentParameterRanges(*, categorical_parameter_ranges=<sagemaker_core.main.utils.Unassigned object>)

Specifies the range of environment parameters

Parameters:

categorical_parameter_ranges (List[CategoricalParameter] | None)

categorical_parameter_ranges

Type:

Specified a list of parameters for each category.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ErrorInfo(*, code=<sagemaker_core.main.utils.Unassigned object>, reason=<sagemaker_core.main.utils.Unassigned object>)

This is an error field object that contains the error code and the reason for an operation failure.

Parameters:

• code (str | None)

• reason (str | None)

code

Type:

The error code for an invalid or failed operation.

reason

Type:

The failure reason for the operation.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Experiment(*, experiment_name=<sagemaker_core.main.utils.Unassigned object>, experiment_arn=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, source=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

The properties of an experiment as returned by the Search API. For information about experiments, see the CreateExperiment API.

Parameters:

• experiment_name (str | object | None)

• experiment_arn (str | None)

• display_name (str | None)

• source (ExperimentSource | None)

• description (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• tags (List[Tag] | None)

experiment_name

Type:

The name of the experiment.

experiment_arn

Type:

The Amazon Resource Name (ARN) of the experiment.

display_name

Type:

The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

source

Type:

sagemaker_core.main.shapes.ExperimentSource | None

description

Type:

The description of the experiment.

creation_time

Type:

When the experiment was created.

created_by

Type:

Who created the experiment.

last_modified_time

Type:

When the experiment was last modified.

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

tags

Type:

The list of tags that are associated with the experiment. You can use Search API to search on the tags.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ExperimentConfig(*, experiment_name=<sagemaker_core.main.utils.Unassigned object>, trial_name=<sagemaker_core.main.utils.Unassigned object>, trial_component_display_name=<sagemaker_core.main.utils.Unassigned object>, run_name=<sagemaker_core.main.utils.Unassigned object>)

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: CreateProcessingJob CreateTrainingJob CreateTransformJob

Parameters:

• experiment_name (str | object | None)

• trial_name (str | object | None)

• trial_component_display_name (str | None)

• run_name (str | None)

experiment_name

Type:

The name of an existing experiment to associate with the trial component.

trial_name

Type:

The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

trial_component_display_name

Type:

The display name for the trial component. If this key isn’t specified, the display name is the trial component name.

run_name

Type:

The name of the experiment run to associate with the trial component.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ExperimentSource(*, source_arn, source_type=<sagemaker_core.main.utils.Unassigned object>)

The source of the experiment.

Parameters:

• source_arn (str)

• source_type (str | None)

source_arn

Type:

The Amazon Resource Name (ARN) of the source.

source_type

Type:

The source type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ExperimentSummary(*, experiment_arn=<sagemaker_core.main.utils.Unassigned object>, experiment_name=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, experiment_source=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName.

Parameters:

• experiment_arn (str | None)

• experiment_name (str | object | None)

• display_name (str | None)

• experiment_source (ExperimentSource | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

experiment_arn

Type:

The Amazon Resource Name (ARN) of the experiment.

experiment_name

Type:

The name of the experiment.

display_name

Type:

The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

experiment_source

Type:

sagemaker_core.main.shapes.ExperimentSource | None

creation_time

Type:

When the experiment was created.

last_modified_time

Type:

When the experiment was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Explainability(*, report=<sagemaker_core.main.utils.Unassigned object>)

Contains explainability metrics for a model.

Parameters:

report (MetricsSource | None)

report

Type:

The explainability report for a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ExplainerConfig(*, clarify_explainer_config=<sagemaker_core.main.utils.Unassigned object>)

A parameter to activate explainers.

Parameters:

clarify_explainer_config (ClarifyExplainerConfig | None)

clarify_explainer_config

Type:

A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FSxLustreFileSystem(*, file_system_id)

A custom file system in Amazon FSx for Lustre.

Parameters:

file_system_id (str)

file_system_id

Type:

Amazon FSx for Lustre file system ID.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FSxLustreFileSystemConfig(*, file_system_id, file_system_path=<sagemaker_core.main.utils.Unassigned object>)

The settings for assigning a custom Amazon FSx for Lustre file system to a user profile or space for an Amazon SageMaker Domain.

Parameters:

• file_system_id (str)

• file_system_path (str | None)

file_system_id

Type:

The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.

file_system_path

Type:

The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FailStepMetadata(*, error_message=<sagemaker_core.main.utils.Unassigned object>)

The container for the metadata for Fail step.

Parameters:

error_message (str | None)

error_message

Type:

A message that you define and then is processed and rendered by the Fail step when the error occurs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FeatureDefinition(*, feature_name, feature_type, collection_type=<sagemaker_core.main.utils.Unassigned object>, collection_config=<sagemaker_core.main.utils.Unassigned object>)

A list of features. You must include FeatureName and FeatureType. Valid feature FeatureTypes are Integral, Fractional and String.

Parameters:

• feature_name (str)

• feature_type (str)

• collection_type (str | None)

• collection_config (CollectionConfig | None)

feature_name

Type:

The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted, write_time, api_invocation_time. The name: Must start with an alphanumeric character. Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.

feature_type

Type:

The value type of a feature. Valid values are Integral, Fractional, or String.

collection_type

Type:

A grouping of elements where each element within the collection must have the same feature type (String, Integral, or Fractional). List: An ordered collection of elements. Set: An unordered collection of unique elements. Vector: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.

collection_config

Type:

Configuration for your collection.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FeatureGroup(*, feature_group_arn=<sagemaker_core.main.utils.Unassigned object>, feature_group_name=<sagemaker_core.main.utils.Unassigned object>, record_identifier_feature_name=<sagemaker_core.main.utils.Unassigned object>, event_time_feature_name=<sagemaker_core.main.utils.Unassigned object>, feature_definitions=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, online_store_config=<sagemaker_core.main.utils.Unassigned object>, offline_store_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, feature_group_status=<sagemaker_core.main.utils.Unassigned object>, offline_store_status=<sagemaker_core.main.utils.Unassigned object>, last_update_status=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.

Parameters:

• feature_group_arn (str | None)

• feature_group_name (str | object | None)

• record_identifier_feature_name (str | None)

• event_time_feature_name (str | None)

• feature_definitions (List[FeatureDefinition] | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• online_store_config (OnlineStoreConfig | None)

• offline_store_config (OfflineStoreConfig | None)

• role_arn (str | None)

• feature_group_status (str | None)

• offline_store_status (OfflineStoreStatus | None)

• last_update_status (LastUpdateStatus | None)

• failure_reason (str | None)

• description (str | None)

• tags (List[Tag] | None)

feature_group_arn

Type:

The Amazon Resource Name (ARN) of a FeatureGroup.

feature_group_name

Type:

The name of the FeatureGroup.

record_identifier_feature_name

Type:

The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions.

event_time_feature_name

Type:

The name of the feature that stores the EventTime of a Record in a FeatureGroup. A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime.

feature_definitions

Type:

A list of Features. Each Feature must include a FeatureName and a FeatureType. Valid FeatureTypes are Integral, Fractional and String. FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time. You can create up to 2,500 FeatureDefinitions per FeatureGroup.

creation_time

Type:

The time a FeatureGroup was created.

last_modified_time

Type:

A timestamp indicating the last time you updated the feature group.

online_store_config

Type:

sagemaker_core.main.shapes.OnlineStoreConfig | None

offline_store_config

Type:

sagemaker_core.main.shapes.OfflineStoreConfig | None

role_arn

Type:

The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.

feature_group_status

Type:

A FeatureGroup status.

offline_store_status

Type:

sagemaker_core.main.shapes.OfflineStoreStatus | None

last_update_status

Type:

A value that indicates whether the feature group was updated successfully.

failure_reason

Type:

The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore.

description

Type:

A free form description of a FeatureGroup.

tags

Type:

Tags used to define a FeatureGroup.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FeatureGroupSummary(*, feature_group_name, feature_group_arn, creation_time, feature_group_status=<sagemaker_core.main.utils.Unassigned object>, offline_store_status=<sagemaker_core.main.utils.Unassigned object>)

The name, ARN, CreationTime, FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup.

Parameters:

• feature_group_name (str | object)

• feature_group_arn (str)

• creation_time (datetime)

• feature_group_status (str | None)

• offline_store_status (OfflineStoreStatus | None)

feature_group_name

Type:

The name of FeatureGroup.

feature_group_arn

Type:

Unique identifier for the FeatureGroup.

creation_time

Type:

A timestamp indicating the time of creation time of the FeatureGroup.

feature_group_status

Type:

The status of a FeatureGroup. The status can be any of the following: Creating, Created, CreateFail, Deleting or DetailFail.

offline_store_status

Type:

Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FeatureMetadata(*, feature_group_arn=<sagemaker_core.main.utils.Unassigned object>, feature_group_name=<sagemaker_core.main.utils.Unassigned object>, feature_name=<sagemaker_core.main.utils.Unassigned object>, feature_type=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, parameters=<sagemaker_core.main.utils.Unassigned object>)

The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.

Parameters:

• feature_group_arn (str | None)

• feature_group_name (str | object | None)

• feature_name (str | None)

• feature_type (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• description (str | None)

• parameters (List[FeatureParameter] | None)

feature_group_arn

Type:

The Amazon Resource Number (ARN) of the feature group.

feature_group_name

Type:

The name of the feature group containing the feature.

feature_name

Type:

The name of feature.

feature_type

Type:

The data type of the feature.

creation_time

Type:

A timestamp indicating when the feature was created.

last_modified_time

Type:

A timestamp indicating when the feature was last modified.

description

Type:

An optional description that you specify to better describe the feature.

parameters

Type:

Optional key-value pairs that you specify to better describe the feature.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FeatureParameter(*, key=<sagemaker_core.main.utils.Unassigned object>, value=<sagemaker_core.main.utils.Unassigned object>)

A key-value pair that you specify to describe the feature.

Parameters:

• key (str | None)

• value (str | None)

key

Type:

A key that must contain a value to describe the feature.

value

Type:

The value that belongs to a key.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FeatureValue(*, feature_name, value_as_string=<sagemaker_core.main.utils.Unassigned object>, value_as_string_list=<sagemaker_core.main.utils.Unassigned object>)

The value associated with a feature.

Parameters:

• feature_name (str)

• value_as_string (str | None)

• value_as_string_list (List[str] | None)

feature_name

Type:

The name of a feature that a feature value corresponds to.

value_as_string

Type:

The value in string format associated with a feature. Used when your CollectionType is None. Note that features types can be String, Integral, or Fractional. This value represents all three types as a string.

value_as_string_list

Type:

The list of values in string format associated with a feature. Used when your CollectionType is a List, Set, or Vector. Note that features types can be String, Integral, or Fractional. These values represents all three types as a string.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FileSource(*, s3_uri, content_type=<sagemaker_core.main.utils.Unassigned object>, content_digest=<sagemaker_core.main.utils.Unassigned object>)

Contains details regarding the file source.

Parameters:

• s3_uri (str)

• content_type (str | None)

• content_digest (str | None)

content_type

Type:

The type of content stored in the file source.

content_digest

Type:

The digest of the file source.

s3_uri

Type:

The Amazon S3 URI for the file source.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FileSystemConfig(*, mount_path=<sagemaker_core.main.utils.Unassigned object>, default_uid=<sagemaker_core.main.utils.Unassigned object>, default_gid=<sagemaker_core.main.utils.Unassigned object>)

The Amazon Elastic File System storage configuration for a SageMaker AI image.

Parameters:

• mount_path (str | None)

• default_uid (int | None)

• default_gid (int | None)

mount_path

Type:

The path within the image to mount the user’s EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.

default_uid

Type:

The default POSIX user ID (UID). If not specified, defaults to 1000.

default_gid

Type:

The default POSIX group ID (GID). If not specified, defaults to 100.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FileSystemDataSource(*, file_system_id, file_system_access_mode, file_system_type, directory_path)

Specifies a file system data source for a channel.

Parameters:

• file_system_id (str)

• file_system_access_mode (str)

• file_system_type (str)

• directory_path (str)

file_system_id

Type:

The file system id.

file_system_access_mode

Type:

The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

file_system_type

Type:

The file system type.

directory_path

Type:

The full path to the directory to associate with the channel.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Filter(*, name, operator=<sagemaker_core.main.utils.Unassigned object>, value=<sagemaker_core.main.utils.Unassigned object>)

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a Value, but not an Operator, SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form “Metrics.&lt;name&gt;”, where &lt;name&gt; is a metric name. For example, the following filter searches for training jobs with an “accuracy” metric greater than “0.9”: { “Name”: “Metrics.accuracy”, “Operator”: “GreaterThan”, “Value”: “0.9” } HyperParameters To define a hyperparameter filter, enter a value with the form “HyperParameters.&lt;name&gt;”. Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a “learning_rate” hyperparameter that is less than “0.5”: { “Name”: “HyperParameters.learning_rate”, “Operator”: “LessThan”, “Value”: “0.5” } Tags To define a tag filter, enter a value with the form Tags.&lt;key&gt;.

Parameters:

• name (str)

• operator (str | None)

• value (str | None)

name

Type:

A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.

operator

Type:

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values: Equals The value of Name equals Value. NotEquals The value of Name doesn’t equal Value. Exists The Name property exists. NotExists The Name property does not exist. GreaterThan The value of Name is greater than Value. Not supported for text properties. GreaterThanOrEqualTo The value of Name is greater than or equal to Value. Not supported for text properties. LessThan The value of Name is less than Value. Not supported for text properties. LessThanOrEqualTo The value of Name is less than or equal to Value. Not supported for text properties. In The value of Name is one of the comma delimited strings in Value. Only supported for text properties. Contains The value of Name contains the string Value. Only supported for text properties. A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following: Experiment.DisplayName Experiment.ExperimentName Experiment.Tags Trial.DisplayName Trial.TrialName Trial.Tags TrialComponent.DisplayName TrialComponent.TrialComponentName TrialComponent.Tags TrialComponent.InputArtifacts TrialComponent.OutputArtifacts A SearchExpression can include only one Contains operator for all other values of Name. In these cases, if you include multiple Contains operators in the SearchExpression, the result is the following error message: “‘CONTAINS’ operator usage limit of 1 exceeded.”

value

Type:

A value used with Name and Operator to determine which resources satisfy the filter’s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd’T’HH:MM:SS.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FinalAutoMLJobObjectiveMetric(*, metric_name, value, type=<sagemaker_core.main.utils.Unassigned object>, standard_metric_name=<sagemaker_core.main.utils.Unassigned object>)

The best candidate result from an AutoML training job.

Parameters:

• metric_name (str)

• value (float)

• type (str | None)

• standard_metric_name (str | None)

type

Type:

The type of metric with the best result.

metric_name

Type:

The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.

value

Type:

The value of the metric with the best result.

standard_metric_name

Type:

The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FinalHyperParameterTuningJobObjectiveMetric(*, metric_name, value, type=<sagemaker_core.main.utils.Unassigned object>)

Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

Parameters:

• metric_name (str)

• value (float)

• type (str | None)

type

Type:

Select if you want to minimize or maximize the objective metric during hyperparameter tuning.

metric_name

Type:

The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.

value

Type:

The value of the objective metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FlowDefinitionOutputConfig(*, s3_output_path, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Contains information about where human output will be stored.

Parameters:

• s3_output_path (str)

• kms_key_id (str | None)

s3_output_path

Type:

The Amazon S3 path where the object containing human output will be made available. To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.

kms_key_id

Type:

The Amazon Key Management Service (KMS) key ID for server-side encryption.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.FlowDefinitionSummary(*, flow_definition_name, flow_definition_arn, flow_definition_status, creation_time, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

Contains summary information about the flow definition.

Parameters:

• flow_definition_name (str | object)

• flow_definition_arn (str)

• flow_definition_status (str)

• creation_time (datetime)

• failure_reason (str | None)

flow_definition_name

Type:

The name of the flow definition.

flow_definition_arn

Type:

The Amazon Resource Name (ARN) of the flow definition.

flow_definition_status

Type:

The status of the flow definition. Valid values:

creation_time

Type:

The timestamp when SageMaker created the flow definition.

failure_reason

Type:

The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.GenerativeAiSettings(*, amazon_bedrock_role_arn=<sagemaker_core.main.utils.Unassigned object>)

The generative AI settings for the SageMaker Canvas application. Configure these settings for Canvas users starting chats with generative AI foundation models. For more information, see Use generative AI with foundation models.

Parameters:

amazon_bedrock_role_arn (str | None)

amazon_bedrock_role_arn

Type:

The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.GetDeviceFleetReportResponse(*, device_fleet_arn, device_fleet_name, output_config=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, report_generated=<sagemaker_core.main.utils.Unassigned object>, device_stats=<sagemaker_core.main.utils.Unassigned object>, agent_versions=<sagemaker_core.main.utils.Unassigned object>, model_stats=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• device_fleet_arn (str)

• device_fleet_name (str | object)

• output_config (EdgeOutputConfig | None)

• description (str | None)

• report_generated (datetime | None)

• device_stats (DeviceStats | None)

• agent_versions (List[AgentVersion] | None)

• model_stats (List[EdgeModelStat] | None)

device_fleet_arn

Type:

The Amazon Resource Name (ARN) of the device.

device_fleet_name

Type:

The name of the fleet.

output_config

Type:

The output configuration for storing sample data collected by the fleet.

description

Type:

Description of the fleet.

report_generated

Type:

Timestamp of when the report was generated.

device_stats

Type:

Status of devices.

agent_versions

Type:

The versions of Edge Manager agent deployed on the fleet.

model_stats

Type:

Status of model on device.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.GetLineageGroupPolicyResponse(*, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>, resource_policy=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• lineage_group_arn (str | None)

• resource_policy (str | None)

lineage_group_arn

Type:

The Amazon Resource Name (ARN) of the lineage group.

resource_policy

Type:

The resource policy that gives access to the lineage group in another account.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.GetRecordResponse(*, record=<sagemaker_core.main.utils.Unassigned object>, expires_at=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• record (List[FeatureValue] | None)

• expires_at (str | None)

record

Type:

The record you requested. A list of FeatureValues.

expires_at

Type:

The ExpiresAt ISO string of the requested record.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.GitConfig(*, repository_url, branch=<sagemaker_core.main.utils.Unassigned object>, secret_arn=<sagemaker_core.main.utils.Unassigned object>)

Specifies configuration details for a Git repository in your Amazon Web Services account.

Parameters:

• repository_url (str)

• branch (str | None)

• secret_arn (str | None)

repository_url

Type:

The URL where the Git repository is located.

branch

Type:

The default branch for the Git repository.

secret_arn

Type:

The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format: {“username”: UserName, “password”: Password}

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.GitConfigForUpdate(*, secret_arn=<sagemaker_core.main.utils.Unassigned object>)

Specifies configuration details for a Git repository when the repository is updated.

Parameters:

secret_arn (str | None)

secret_arn

Type:

The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format: {“username”: UserName, “password”: Password}

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HiddenSageMakerImage(*, sage_maker_image_name=<sagemaker_core.main.utils.Unassigned object>, version_aliases=<sagemaker_core.main.utils.Unassigned object>)

The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.

Parameters:

• sage_maker_image_name (str | None)

• version_aliases (List[str] | None)

sage_maker_image_name

Type:

The SageMaker image name that you are hiding from the Studio user interface.

version_aliases

Type:

The version aliases you are hiding from the Studio user interface.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HolidayConfigAttributes(*, country_code=<sagemaker_core.main.utils.Unassigned object>)

Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.

Parameters:

country_code (str | None)

country_code

Type:

The country code for the holiday calendar. For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HubAccessConfig(*, hub_content_arn)

The configuration for a private hub model reference that points to a public SageMaker JumpStart model. For more information about private hubs, see Private curated hubs for foundation model access control in JumpStart.

Parameters:

hub_content_arn (str)

hub_content_arn

Type:

The ARN of your private model hub content. This should be a ModelReference resource type that points to a SageMaker JumpStart public hub model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HubContentDependency(*, dependency_origin_path=<sagemaker_core.main.utils.Unassigned object>, dependency_copy_path=<sagemaker_core.main.utils.Unassigned object>)

Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks.

Parameters:

• dependency_origin_path (str | None)

• dependency_copy_path (str | None)

dependency_origin_path

Type:

The hub content dependency origin path.

dependency_copy_path

Type:

The hub content dependency copy path.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HubContentInfo(*, hub_content_name, hub_content_arn, hub_content_version, hub_content_type, document_schema_version, hub_content_status, creation_time, sage_maker_public_hub_content_arn=<sagemaker_core.main.utils.Unassigned object>, hub_content_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_content_description=<sagemaker_core.main.utils.Unassigned object>, support_status=<sagemaker_core.main.utils.Unassigned object>, hub_content_search_keywords=<sagemaker_core.main.utils.Unassigned object>, original_creation_time=<sagemaker_core.main.utils.Unassigned object>)

Information about hub content.

Parameters:

• hub_content_name (str | object)

• hub_content_arn (str)

• hub_content_version (str)

• hub_content_type (str)

• document_schema_version (str)

• hub_content_status (str)

• creation_time (datetime)

• sage_maker_public_hub_content_arn (str | None)

• hub_content_display_name (str | None)

• hub_content_description (str | None)

• support_status (str | None)

• hub_content_search_keywords (List[str] | None)

• original_creation_time (datetime | None)

hub_content_name

Type:

The name of the hub content.

hub_content_arn

Type:

The Amazon Resource Name (ARN) of the hub content.

sage_maker_public_hub_content_arn

Type:

The ARN of the public hub content.

hub_content_version

Type:

The version of the hub content.

hub_content_type

Type:

The type of hub content.

document_schema_version

Type:

The version of the hub content document schema.

hub_content_display_name

Type:

The display name of the hub content.

hub_content_description

Type:

A description of the hub content.

support_status

Type:

The support status of the hub content.

hub_content_search_keywords

Type:

The searchable keywords for the hub content.

hub_content_status

Type:

The status of the hub content.

creation_time

Type:

The date and time that the hub content was created.

original_creation_time

Type:

The date and time when the hub content was originally created, before any updates or revisions.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HubInfo(*, hub_name, hub_arn, hub_status, creation_time, last_modified_time, hub_display_name=<sagemaker_core.main.utils.Unassigned object>, hub_description=<sagemaker_core.main.utils.Unassigned object>, hub_search_keywords=<sagemaker_core.main.utils.Unassigned object>)

Information about a hub.

Parameters:

• hub_name (str | object)

• hub_arn (str)

• hub_status (str)

• creation_time (datetime)

• last_modified_time (datetime)

• hub_display_name (str | None)

• hub_description (str | None)

• hub_search_keywords (List[str] | None)

hub_name

Type:

The name of the hub.

hub_arn

Type:

The Amazon Resource Name (ARN) of the hub.

hub_display_name

Type:

The display name of the hub.

hub_description

Type:

A description of the hub.

hub_search_keywords

Type:

The searchable keywords for the hub.

hub_status

Type:

The status of the hub.

creation_time

Type:

The date and time that the hub was created.

last_modified_time

Type:

The date and time that the hub was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HubS3StorageConfig(*, s3_output_path=<sagemaker_core.main.utils.Unassigned object>)

The Amazon S3 storage configuration of a hub.

Parameters:

s3_output_path (str | None)

s3_output_path

Type:

The Amazon S3 bucket prefix for hosting hub content.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HumanLoopActivationConditionsConfig(*, human_loop_activation_conditions)

Defines under what conditions SageMaker creates a human loop. Used within CreateFlowDefinition. See HumanLoopActivationConditionsConfig for the required format of activation conditions.

Parameters:

human_loop_activation_conditions (str)

human_loop_activation_conditions

Type:

JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HumanLoopActivationConfig(*, human_loop_activation_conditions_config)

Provides information about how and under what conditions SageMaker creates a human loop. If HumanLoopActivationConfig is not given, then all requests go to humans.

Parameters:

human_loop_activation_conditions_config (HumanLoopActivationConditionsConfig)

human_loop_activation_conditions_config

Type:

Container structure for defining under what conditions SageMaker creates a human loop.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HumanLoopConfig(*, workteam_arn, human_task_ui_arn, task_title, task_description, task_count, task_availability_lifetime_in_seconds=<sagemaker_core.main.utils.Unassigned object>, task_time_limit_in_seconds=<sagemaker_core.main.utils.Unassigned object>, task_keywords=<sagemaker_core.main.utils.Unassigned object>, public_workforce_task_price=<sagemaker_core.main.utils.Unassigned object>)

Describes the work to be performed by human workers.

Parameters:

• workteam_arn (str)

• human_task_ui_arn (str)

• task_title (str)

• task_description (str)

• task_count (int)

• task_availability_lifetime_in_seconds (int | None)

• task_time_limit_in_seconds (int | None)

• task_keywords (List[str] | None)

• public_workforce_task_price (PublicWorkforceTaskPrice | None)

workteam_arn

Type:

Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.

human_task_ui_arn

Type:

The Amazon Resource Name (ARN) of the human task user interface. You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI. To learn how to create a custom HTML template, see Create Custom Worker Task Template. To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.

task_title

Type:

A title for the human worker task.

task_description

Type:

A description for the human worker task.

task_count

Type:

The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.

task_availability_lifetime_in_seconds

Type:

The length of time that a task remains available for review by human workers.

task_time_limit_in_seconds

Type:

The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).

task_keywords

Type:

Keywords used to describe the task so that workers can discover the task.

public_workforce_task_price

Type:

sagemaker_core.main.shapes.PublicWorkforceTaskPrice | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HumanLoopRequestSource(*, aws_managed_human_loop_request_source)

Container for configuring the source of human task requests.

Parameters:

aws_managed_human_loop_request_source (str)

aws_managed_human_loop_request_source

Type:

Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HumanTaskConfig(*, workteam_arn, ui_config, task_title, task_description, number_of_human_workers_per_data_object, task_time_limit_in_seconds, pre_human_task_lambda_arn=<sagemaker_core.main.utils.Unassigned object>, task_keywords=<sagemaker_core.main.utils.Unassigned object>, task_availability_lifetime_in_seconds=<sagemaker_core.main.utils.Unassigned object>, max_concurrent_task_count=<sagemaker_core.main.utils.Unassigned object>, annotation_consolidation_config=<sagemaker_core.main.utils.Unassigned object>, public_workforce_task_price=<sagemaker_core.main.utils.Unassigned object>)

Information required for human workers to complete a labeling task.

Parameters:

• workteam_arn (str)

• ui_config (UiConfig)

• task_title (str)

• task_description (str)

• number_of_human_workers_per_data_object (int)

• task_time_limit_in_seconds (int)

• pre_human_task_lambda_arn (str | None)

• task_keywords (List[str] | None)

• task_availability_lifetime_in_seconds (int | None)

• max_concurrent_task_count (int | None)

• annotation_consolidation_config (AnnotationConsolidationConfig | None)

• public_workforce_task_price (PublicWorkforceTaskPrice | None)

workteam_arn

Type:

The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.

ui_config

Type:

Information about the user interface that workers use to complete the labeling task.

pre_human_task_lambda_arn

Type:

The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn. For custom labeling workflows, see Pre-annotation Lambda. Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes. arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as “votes” for the correct label. arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label. arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video. arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking 3D Point Cloud Modalities Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more. 3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians. arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection 3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames. arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking 3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify. arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation Use the following ARNs for Label Verification and Adjustment Jobs Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels . Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations. arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers. arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as “votes” for the correct label. arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames. arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames. arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking 3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame. arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection 3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames. arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking 3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud. arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation

task_keywords

Type:

Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.

task_title

Type:

A title for the task for your human workers.

task_description

Type:

A description of the task for your human workers.

number_of_human_workers_per_data_object

Type:

The number of human workers that will label an object.

task_time_limit_in_seconds

Type:

The amount of time that a worker has to complete a task. If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds). If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use: For image and text labeling jobs, the maximum is 8 hours (28,800 seconds). For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.

task_availability_lifetime_in_seconds

Type:

The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use. If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds). The default is 6 hours (21,600 seconds). If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.

max_concurrent_task_count

Type:

Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.

annotation_consolidation_config

Type:

Configures how labels are consolidated across human workers.

public_workforce_task_price

Type:

The price that you pay for each task performed by an Amazon Mechanical Turk worker.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HumanTaskUiSummary(*, human_task_ui_name, human_task_ui_arn, creation_time)

Container for human task user interface information.

Parameters:

• human_task_ui_name (str | object)

• human_task_ui_arn (str)

• creation_time (datetime)

human_task_ui_name

Type:

The name of the human task user interface.

human_task_ui_arn

Type:

The Amazon Resource Name (ARN) of the human task user interface.

creation_time

Type:

A timestamp when SageMaker created the human task user interface.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterAlgorithmSpecification(*, training_input_mode, training_image=<sagemaker_core.main.utils.Unassigned object>, algorithm_name=<sagemaker_core.main.utils.Unassigned object>, metric_definitions=<sagemaker_core.main.utils.Unassigned object>)

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

Parameters:

• training_input_mode (str)

• training_image (str | None)

• algorithm_name (str | object | None)

• metric_definitions (List[MetricDefinition] | None)

training_image

Type:

The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

training_input_mode

Type:

str

algorithm_name

Type:

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

metric_definitions

Type:

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterSpecification(*, name, type, description=<sagemaker_core.main.utils.Unassigned object>, range=<sagemaker_core.main.utils.Unassigned object>, is_tunable=<sagemaker_core.main.utils.Unassigned object>, is_required=<sagemaker_core.main.utils.Unassigned object>, default_value=<sagemaker_core.main.utils.Unassigned object>)

Defines a hyperparameter to be used by an algorithm.

Parameters:

• name (str)

• type (str)

• description (str | None)

• range (ParameterRange | None)

• is_tunable (bool | None)

• is_required (bool | None)

• default_value (str | None)

name

Type:

The name of this hyperparameter. The name must be unique.

description

Type:

A brief description of the hyperparameter.

type

Type:

The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.

range

Type:

The allowed range for this hyperparameter.

is_tunable

Type:

Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

is_required

Type:

Indicates whether this hyperparameter is required.

default_value

Type:

The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTrainingJobDefinition(*, algorithm_specification, role_arn, output_data_config, stopping_condition, definition_name=<sagemaker_core.main.utils.Unassigned object>, tuning_objective=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_ranges=<sagemaker_core.main.utils.Unassigned object>, static_hyper_parameters=<sagemaker_core.main.utils.Unassigned object>, input_data_config=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, resource_config=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_resource_config=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, enable_inter_container_traffic_encryption=<sagemaker_core.main.utils.Unassigned object>, enable_managed_spot_training=<sagemaker_core.main.utils.Unassigned object>, checkpoint_config=<sagemaker_core.main.utils.Unassigned object>, retry_strategy=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>)

Defines the training jobs launched by a hyperparameter tuning job.

Parameters:

• algorithm_specification (HyperParameterAlgorithmSpecification)

• role_arn (str)

• output_data_config (OutputDataConfig)

• stopping_condition (StoppingCondition)

• definition_name (str | None)

• tuning_objective (HyperParameterTuningJobObjective | None)

• hyper_parameter_ranges (ParameterRanges | None)

• static_hyper_parameters (Dict[str, str] | None)

• input_data_config (List[Channel] | None)

• vpc_config (VpcConfig | None)

• resource_config (ResourceConfig | None)

• hyper_parameter_tuning_resource_config (HyperParameterTuningResourceConfig | None)

• enable_network_isolation (bool | None)

• enable_inter_container_traffic_encryption (bool | None)

• enable_managed_spot_training (bool | None)

• checkpoint_config (CheckpointConfig | None)

• retry_strategy (RetryStrategy | None)

• environment (Dict[str, str] | None)

definition_name

Type:

The job definition name.

tuning_objective

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobObjective | None

hyper_parameter_ranges

Type:

sagemaker_core.main.shapes.ParameterRanges | None

static_hyper_parameters

Type:

Specifies the values of hyperparameters that do not change for the tuning job.

algorithm_specification

Type:

The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

role_arn

Type:

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

input_data_config

Type:

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

vpc_config

Type:

The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

output_data_config

Type:

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

resource_config

Type:

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches. Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1. If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

hyper_parameter_tuning_resource_config

Type:

The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).

stopping_condition

Type:

Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

enable_network_isolation

Type:

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

enable_inter_container_traffic_encryption

Type:

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

enable_managed_spot_training

Type:

A Boolean indicating whether managed spot training is enabled (True) or not (False).

checkpoint_config

Type:

sagemaker_core.main.shapes.CheckpointConfig | None

retry_strategy

Type:

The number of times to retry the job when the job fails due to an InternalServerError.

environment

Type:

An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information. The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can’t exceed the maximum number specified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTrainingJobSummary(*, training_job_name, training_job_arn, creation_time, training_job_status, tuned_hyper_parameters, training_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, tuning_job_name=<sagemaker_core.main.utils.Unassigned object>, training_start_time=<sagemaker_core.main.utils.Unassigned object>, training_end_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, final_hyper_parameter_tuning_job_objective_metric=<sagemaker_core.main.utils.Unassigned object>, objective_status=<sagemaker_core.main.utils.Unassigned object>)

The container for the summary information about a training job.

Parameters:

• training_job_name (str | object)

• training_job_arn (str)

• creation_time (datetime)

• training_job_status (str)

• tuned_hyper_parameters (Dict[str, str])

• training_job_definition_name (str | None)

• tuning_job_name (str | None)

• training_start_time (datetime | None)

• training_end_time (datetime | None)

• failure_reason (str | None)

• final_hyper_parameter_tuning_job_objective_metric (FinalHyperParameterTuningJobObjectiveMetric | None)

• objective_status (str | None)

training_job_definition_name

Type:

The training job definition name.

training_job_name

Type:

The name of the training job.

training_job_arn

Type:

The Amazon Resource Name (ARN) of the training job.

tuning_job_name

Type:

The HyperParameter tuning job that launched the training job.

creation_time

Type:

The date and time that the training job was created.

training_start_time

Type:

The date and time that the training job started.

training_end_time

Type:

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.

training_job_status

Type:

The status of the training job.

tuned_hyper_parameters

Type:

A list of the hyperparameters for which you specified ranges to search.

failure_reason

Type:

The reason that the training job failed.

final_hyper_parameter_tuning_job_objective_metric

Type:

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

objective_status

Type:

The status of the objective metric for the training job: Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process. Pending: The training job is in progress and evaluation of its final objective metric is pending. Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningInstanceConfig(*, instance_type, instance_count, volume_size_in_gb)

The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

Parameters:

• instance_type (str)

• instance_count (int)

• volume_size_in_gb (int)

instance_type

Type:

The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.

instance_count

Type:

The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.

volume_size_in_gb

Type:

The volume size in GB of the data to be processed for hyperparameter optimization (optional).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobCompletionDetails(*, number_of_training_jobs_objective_not_improving=<sagemaker_core.main.utils.Unassigned object>, convergence_detected_time=<sagemaker_core.main.utils.Unassigned object>)

A structure that contains runtime information about both current and completed hyperparameter tuning jobs.

Parameters:

• number_of_training_jobs_objective_not_improving (int | None)

• convergence_detected_time (datetime | None)

number_of_training_jobs_objective_not_improving

Type:

The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.

convergence_detected_time

Type:

The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobConfig(*, strategy, resource_limits, strategy_config=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_job_objective=<sagemaker_core.main.utils.Unassigned object>, parameter_ranges=<sagemaker_core.main.utils.Unassigned object>, training_job_early_stopping_type=<sagemaker_core.main.utils.Unassigned object>, tuning_job_completion_criteria=<sagemaker_core.main.utils.Unassigned object>, random_seed=<sagemaker_core.main.utils.Unassigned object>)

Configures a hyperparameter tuning job.

Parameters:

• strategy (str)

• resource_limits (ResourceLimits)

• strategy_config (HyperParameterTuningJobStrategyConfig | None)

• hyper_parameter_tuning_job_objective (HyperParameterTuningJobObjective | None)

• parameter_ranges (ParameterRanges | None)

• training_job_early_stopping_type (str | None)

• tuning_job_completion_criteria (TuningJobCompletionCriteria | None)

• random_seed (int | None)

strategy

Type:

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.

strategy_config

Type:

The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.

hyper_parameter_tuning_job_objective

Type:

The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.

resource_limits

Type:

The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.

parameter_ranges

Type:

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.

training_job_early_stopping_type

Type:

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF): OFF Training jobs launched by the hyperparameter tuning job do not use early stopping. AUTO SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

tuning_job_completion_criteria

Type:

The tuning job’s completion criteria.

random_seed

Type:

A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobConsumedResources(*, runtime_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

The total resources consumed by your hyperparameter tuning job.

Parameters:

runtime_in_seconds (int | None)

runtime_in_seconds

Type:

The wall clock runtime in seconds used by your hyperparameter tuning job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobObjective(*, type, metric_name)

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables.

Parameters:

• type (str)

• metric_name (str)

type

Type:

Whether to minimize or maximize the objective metric.

metric_name

Type:

The name of the metric to use for the objective metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobSearchEntity(*, hyper_parameter_tuning_job_name=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_job_arn=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_job_config=<sagemaker_core.main.utils.Unassigned object>, training_job_definition=<sagemaker_core.main.utils.Unassigned object>, training_job_definitions=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_job_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, hyper_parameter_tuning_end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, training_job_status_counters=<sagemaker_core.main.utils.Unassigned object>, objective_status_counters=<sagemaker_core.main.utils.Unassigned object>, best_training_job=<sagemaker_core.main.utils.Unassigned object>, overall_best_training_job=<sagemaker_core.main.utils.Unassigned object>, warm_start_config=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, tuning_job_completion_details=<sagemaker_core.main.utils.Unassigned object>, consumed_resources=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

An entity returned by the SearchRecord API containing the properties of a hyperparameter tuning job.

Parameters:

• hyper_parameter_tuning_job_name (str | object | None)

• hyper_parameter_tuning_job_arn (str | None)

• hyper_parameter_tuning_job_config (HyperParameterTuningJobConfig | None)

• training_job_definition (HyperParameterTrainingJobDefinition | None)

• training_job_definitions (List[HyperParameterTrainingJobDefinition] | None)

• hyper_parameter_tuning_job_status (str | None)

• creation_time (datetime | None)

• hyper_parameter_tuning_end_time (datetime | None)

• last_modified_time (datetime | None)

• training_job_status_counters (TrainingJobStatusCounters | None)

• objective_status_counters (ObjectiveStatusCounters | None)

• best_training_job (HyperParameterTrainingJobSummary | None)

• overall_best_training_job (HyperParameterTrainingJobSummary | None)

• warm_start_config (HyperParameterTuningJobWarmStartConfig | None)

• failure_reason (str | None)

• tuning_job_completion_details (HyperParameterTuningJobCompletionDetails | None)

• consumed_resources (HyperParameterTuningJobConsumedResources | None)

• tags (List[Tag] | None)

hyper_parameter_tuning_job_name

Type:

The name of a hyperparameter tuning job.

hyper_parameter_tuning_job_arn

Type:

The Amazon Resource Name (ARN) of a hyperparameter tuning job.

hyper_parameter_tuning_job_config

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobConfig | None

training_job_definition

Type:

sagemaker_core.main.shapes.HyperParameterTrainingJobDefinition | None

training_job_definitions

Type:

The job definitions included in a hyperparameter tuning job.

hyper_parameter_tuning_job_status

Type:

The status of a hyperparameter tuning job.

creation_time

Type:

The time that a hyperparameter tuning job was created.

hyper_parameter_tuning_end_time

Type:

The time that a hyperparameter tuning job ended.

last_modified_time

Type:

The time that a hyperparameter tuning job was last modified.

training_job_status_counters

Type:

sagemaker_core.main.shapes.TrainingJobStatusCounters | None

objective_status_counters

Type:

sagemaker_core.main.shapes.ObjectiveStatusCounters | None

best_training_job

Type:

sagemaker_core.main.shapes.HyperParameterTrainingJobSummary | None

overall_best_training_job

Type:

sagemaker_core.main.shapes.HyperParameterTrainingJobSummary | None

warm_start_config

Type:

sagemaker_core.main.shapes.HyperParameterTuningJobWarmStartConfig | None

failure_reason

Type:

The error that was created when a hyperparameter tuning job failed.

tuning_job_completion_details

Type:

Information about either a current or completed hyperparameter tuning job.

consumed_resources

Type:

The total amount of resources consumed by a hyperparameter tuning job.

tags

Type:

The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobStrategyConfig(*, hyperband_strategy_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration for a training job launched by a hyperparameter tuning job. Choose Bayesian for Bayesian optimization, and Random for random search optimization. For more advanced use cases, use Hyperband, which evaluates objective metrics for training jobs after every epoch. For more information about strategies, see How Hyperparameter Tuning Works.

Parameters:

hyperband_strategy_config (HyperbandStrategyConfig | None)

hyperband_strategy_config

Type:

The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobSummary(*, hyper_parameter_tuning_job_name, hyper_parameter_tuning_job_arn, hyper_parameter_tuning_job_status, strategy, creation_time, training_job_status_counters, objective_status_counters, hyper_parameter_tuning_end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, resource_limits=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information about a hyperparameter tuning job.

Parameters:

• hyper_parameter_tuning_job_name (str | object)

• hyper_parameter_tuning_job_arn (str)

• hyper_parameter_tuning_job_status (str)

• strategy (str)

• creation_time (datetime)

• training_job_status_counters (TrainingJobStatusCounters)

• objective_status_counters (ObjectiveStatusCounters)

• hyper_parameter_tuning_end_time (datetime | None)

• last_modified_time (datetime | None)

• resource_limits (ResourceLimits | None)

hyper_parameter_tuning_job_name

Type:

The name of the tuning job.

hyper_parameter_tuning_job_arn

Type:

The Amazon Resource Name (ARN) of the tuning job.

hyper_parameter_tuning_job_status

Type:

The status of the tuning job.

strategy

Type:

Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration.

creation_time

Type:

The date and time that the tuning job was created.

hyper_parameter_tuning_end_time

Type:

The date and time that the tuning job ended.

last_modified_time

Type:

The date and time that the tuning job was modified.

training_job_status_counters

Type:

The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.

objective_status_counters

Type:

The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.

resource_limits

Type:

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningJobWarmStartConfig(*, parent_hyper_parameter_tuning_jobs, warm_start_type)

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job. All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Parameters:

• parent_hyper_parameter_tuning_jobs (List[ParentHyperParameterTuningJob])

• warm_start_type (str)

parent_hyper_parameter_tuning_jobs

Type:

An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point. Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.

warm_start_type

Type:

Specifies one of the following: IDENTICAL_DATA_AND_ALGORITHM The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs. TRANSFER_LEARNING The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperParameterTuningResourceConfig(*, instance_type=<sagemaker_core.main.utils.Unassigned object>, instance_count=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, volume_kms_key_id=<sagemaker_core.main.utils.Unassigned object>, allocation_strategy=<sagemaker_core.main.utils.Unassigned object>, instance_configs=<sagemaker_core.main.utils.Unassigned object>)

The configuration of resources, including compute instances and storage volumes for use in training jobs launched by hyperparameter tuning jobs. HyperParameterTuningResourceConfig is similar to ResourceConfig, but has the additional InstanceConfigs and AllocationStrategy fields to allow for flexible instance management. Specify one or more instance types, count, and the allocation strategy for instance selection. HyperParameterTuningResourceConfig supports the capabilities of ResourceConfig with the exception of KeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools by default, which reuse clusters between training jobs.

Parameters:

• instance_type (str | None)

• instance_count (int | None)

• volume_size_in_gb (int | None)

• volume_kms_key_id (str | None)

• allocation_strategy (str | None)

• instance_configs (List[HyperParameterTuningInstanceConfig] | None)

instance_type

Type:

The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

instance_count

Type:

The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.

volume_size_in_gb

Type:

The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified. Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes. SageMaker supports only the General Purpose SSD (gp2) storage volume type.

volume_kms_key_id

Type:

A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key. KMS Key ID: “1234abcd-12ab-34cd-56ef-1234567890ab” Amazon Resource Name (ARN) of a KMS key: “arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab” Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

allocation_strategy

Type:

The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

instance_configs

Type:

A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used. If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.HyperbandStrategyConfig(*, min_resource=<sagemaker_core.main.utils.Unassigned object>, max_resource=<sagemaker_core.main.utils.Unassigned object>)

The configuration for Hyperband, a multi-fidelity based hyperparameter tuning strategy. Hyperband uses the final and intermediate results of a training job to dynamically allocate resources to utilized hyperparameter configurations while automatically stopping under-performing configurations. This parameter should be provided only if Hyperband is selected as the StrategyConfig under the HyperParameterTuningJobConfig API.

Parameters:

• min_resource (int | None)

• max_resource (int | None)

min_resource

Type:

The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband.

max_resource

Type:

The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTraining attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters: epochs numepochs n-epochs n_epochs num_epochs If HyperbandStrategyConfig is unable to infer a value for MaxResource, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.IamIdentity(*, arn=<sagemaker_core.main.utils.Unassigned object>, principal_id=<sagemaker_core.main.utils.Unassigned object>, source_identity=<sagemaker_core.main.utils.Unassigned object>)

The IAM Identity details associated with the user. These details are associated with model package groups, model packages and project entities only.

Parameters:

• arn (str | None)

• principal_id (str | None)

• source_identity (str | None)

arn

Type:

The Amazon Resource Name (ARN) of the IAM identity.

principal_id

Type:

The ID of the principal that assumes the IAM identity.

source_identity

Type:

The person or application which assumes the IAM identity.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.IamPolicyConstraints(*, source_ip=<sagemaker_core.main.utils.Unassigned object>, vpc_source_ip=<sagemaker_core.main.utils.Unassigned object>)

Use this parameter to specify a supported global condition key that is added to the IAM policy.

Parameters:

• source_ip (str | None)

• vpc_source_ip (str | None)

source_ip

Type:

When SourceIp is Enabled the worker’s IP address when a task is rendered in the worker portal is added to the IAM policy as a Condition used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal.

vpc_source_ip

Type:

When VpcSourceIp is Enabled the worker’s IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as a Condition used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker’s IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.IdentityProviderOAuthSetting(*, data_source_name=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, secret_arn=<sagemaker_core.main.utils.Unassigned object>)

The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

Parameters:

• data_source_name (str | None)

• status (str | None)

• secret_arn (str | None)

data_source_name

Type:

The name of the data source that you’re connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.

status

Type:

Describes whether OAuth for a data source is enabled or disabled in the Canvas application.

secret_arn

Type:

The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.IdleSettings(*, lifecycle_management=<sagemaker_core.main.utils.Unassigned object>, idle_timeout_in_minutes=<sagemaker_core.main.utils.Unassigned object>, min_idle_timeout_in_minutes=<sagemaker_core.main.utils.Unassigned object>, max_idle_timeout_in_minutes=<sagemaker_core.main.utils.Unassigned object>)

Settings related to idle shutdown of Studio applications.

Parameters:

• lifecycle_management (str | None)

• idle_timeout_in_minutes (int | None)

• min_idle_timeout_in_minutes (int | None)

• max_idle_timeout_in_minutes (int | None)

lifecycle_management

Type:

Indicates whether idle shutdown is activated for the application type.

idle_timeout_in_minutes

Type:

The time that SageMaker waits after the application becomes idle before shutting it down.

min_idle_timeout_in_minutes

Type:

The minimum value in minutes that custom idle shutdown can be set to by the user.

max_idle_timeout_in_minutes

Type:

The maximum value in minutes that custom idle shutdown can be set to by the user.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Image(*, creation_time, image_arn, image_name, image_status, last_modified_time, description=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

A SageMaker AI image. A SageMaker AI image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker AI ImageVersion.

Parameters:

• creation_time (datetime)

• image_arn (str)

• image_name (str | object)

• image_status (str)

• last_modified_time (datetime)

• description (str | None)

• display_name (str | None)

• failure_reason (str | None)

creation_time

Type:

When the image was created.

description

Type:

The description of the image.

display_name

Type:

The name of the image as displayed.

failure_reason

Type:

When a create, update, or delete operation fails, the reason for the failure.

image_arn

Type:

The ARN of the image.

image_name

Type:

The name of the image.

image_status

Type:

The status of the image.

last_modified_time

Type:

When the image was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ImageClassificationJobConfig(*, completion_criteria=<sagemaker_core.main.utils.Unassigned object>)

The collection of settings used by an AutoML job V2 for the image classification problem type.

Parameters:

completion_criteria (AutoMLJobCompletionCriteria | None)

completion_criteria

Type:

How long a job is allowed to run, or how many candidates a job is allowed to generate.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ImageConfig(*, repository_access_mode, repository_auth_config=<sagemaker_core.main.utils.Unassigned object>)

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).

Parameters:

• repository_access_mode (str)

• repository_auth_config (RepositoryAuthConfig | None)

repository_access_mode

Type:

Set this to one of the following values: Platform - The model image is hosted in Amazon ECR. Vpc - The model image is hosted in a private Docker registry in your VPC.

repository_auth_config

Type:

(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ImageVersion(*, creation_time, image_arn, image_version_arn, image_version_status, last_modified_time, version, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

A version of a SageMaker AI Image. A version represents an existing container image.

Parameters:

• creation_time (datetime)

• image_arn (str)

• image_version_arn (str)

• image_version_status (str)

• last_modified_time (datetime)

• version (int)

• failure_reason (str | None)

creation_time

Type:

When the version was created.

failure_reason

Type:

When a create or delete operation fails, the reason for the failure.

image_arn

Type:

The ARN of the image the version is based on.

image_version_arn

Type:

The ARN of the version.

image_version_status

Type:

The status of the version.

last_modified_time

Type:

When the version was last modified.

version

Type:

The version number.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentCapacitySize(*, type, value)

Specifies the type and size of the endpoint capacity to activate for a rolling deployment or a rollback strategy. You can specify your batches as either of the following: A count of inference component copies The overall percentage or your fleet For a rollback strategy, if you don’t specify the fields in this object, or if you set the Value parameter to 100%, then SageMaker AI uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.

Parameters:

• type (str)

• value (int)

type

Type:

Specifies the endpoint capacity type. COPY_COUNT The endpoint activates based on the number of inference component copies. CAPACITY_PERCENT The endpoint activates based on the specified percentage of capacity.

value

Type:

Defines the capacity size, either as a number of inference component copies or a capacity percentage.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentComputeResourceRequirements(*, min_memory_required_in_mb, number_of_cpu_cores_required=<sagemaker_core.main.utils.Unassigned object>, number_of_accelerator_devices_required=<sagemaker_core.main.utils.Unassigned object>, max_memory_required_in_mb=<sagemaker_core.main.utils.Unassigned object>)

Defines the compute resources to allocate to run a model, plus any adapter models, that you assign to an inference component. These resources include CPU cores, accelerators, and memory.

Parameters:

• min_memory_required_in_mb (int)

• number_of_cpu_cores_required (float | None)

• number_of_accelerator_devices_required (float | None)

• max_memory_required_in_mb (int | None)

number_of_cpu_cores_required

Type:

The number of CPU cores to allocate to run a model that you assign to an inference component.

number_of_accelerator_devices_required

Type:

The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.

min_memory_required_in_mb

Type:

The minimum MB of memory to allocate to run a model that you assign to an inference component.

max_memory_required_in_mb

Type:

The maximum MB of memory to allocate to run a model that you assign to an inference component.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentContainerSpecification(*, image=<sagemaker_core.main.utils.Unassigned object>, artifact_url=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>)

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

Parameters:

• image (str | None)

• artifact_url (str | None)

• environment (Dict[str, str] | None)

image

Type:

The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

artifact_url

Type:

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

environment

Type:

The environment variables to set in the Docker container. Each key and value in the Environment string-to-string map can have length of up to 1024. We support up to 16 entries in the map.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentContainerSpecificationSummary(*, deployed_image=<sagemaker_core.main.utils.Unassigned object>, artifact_url=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>)

Details about the resources that are deployed with this inference component.

Parameters:

• deployed_image (DeployedImage | None)

• artifact_url (str | None)

• environment (Dict[str, str] | None)

deployed_image

Type:

sagemaker_core.main.shapes.DeployedImage | None

artifact_url

Type:

The Amazon S3 path where the model artifacts are stored.

environment

Type:

The environment variables to set in the Docker container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentDeploymentConfig(*, rolling_update_policy, auto_rollback_configuration=<sagemaker_core.main.utils.Unassigned object>)

The deployment configuration for an endpoint that hosts inference components. The configuration includes the desired deployment strategy and rollback settings.

Parameters:

• rolling_update_policy (InferenceComponentRollingUpdatePolicy)

• auto_rollback_configuration (AutoRollbackConfig | None)

rolling_update_policy

Type:

Specifies a rolling deployment strategy for updating a SageMaker AI endpoint.

auto_rollback_configuration

Type:

sagemaker_core.main.shapes.AutoRollbackConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentRollingUpdatePolicy(*, maximum_batch_size, wait_interval_in_seconds, maximum_execution_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>, rollback_maximum_batch_size=<sagemaker_core.main.utils.Unassigned object>)

Specifies a rolling deployment strategy for updating a SageMaker AI inference component.

Parameters:

• maximum_batch_size (InferenceComponentCapacitySize)

• wait_interval_in_seconds (int)

• maximum_execution_timeout_in_seconds (int | None)

• rollback_maximum_batch_size (InferenceComponentCapacitySize | None)

maximum_batch_size

Type:

The batch size for each rolling step in the deployment process. For each step, SageMaker AI provisions capacity on the new endpoint fleet, routes traffic to that fleet, and terminates capacity on the old endpoint fleet. The value must be between 5% to 50% of the copy count of the inference component.

wait_interval_in_seconds

Type:

The length of the baking period, during which SageMaker AI monitors alarms for each batch on the new fleet.

maximum_execution_timeout_in_seconds

Type:

The time limit for the total deployment. Exceeding this limit causes a timeout.

rollback_maximum_batch_size

Type:

The batch size for a rollback to the old endpoint fleet. If this field is absent, the value is set to the default, which is 100% of the total capacity. When the default is used, SageMaker AI provisions the entire capacity of the old fleet at once during rollback.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentRuntimeConfig(*, copy_count)

Runtime settings for a model that is deployed with an inference component.

Parameters:

copy_count (int)

copy_count

Type:

The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentRuntimeConfigSummary(*, desired_copy_count=<sagemaker_core.main.utils.Unassigned object>, current_copy_count=<sagemaker_core.main.utils.Unassigned object>)

Details about the runtime settings for the model that is deployed with the inference component.

Parameters:

• desired_copy_count (int | None)

• current_copy_count (int | None)

desired_copy_count

Type:

The number of runtime copies of the model container that you requested to deploy with the inference component.

current_copy_count

Type:

The number of runtime copies of the model container that are currently deployed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentSpecification(*, model_name=<sagemaker_core.main.utils.Unassigned object>, container=<sagemaker_core.main.utils.Unassigned object>, startup_parameters=<sagemaker_core.main.utils.Unassigned object>, compute_resource_requirements=<sagemaker_core.main.utils.Unassigned object>, base_inference_component_name=<sagemaker_core.main.utils.Unassigned object>)

Details about the resources to deploy with this inference component, including the model, container, and compute resources.

Parameters:

• model_name (str | object | None)

• container (InferenceComponentContainerSpecification | None)

• startup_parameters (InferenceComponentStartupParameters | None)

• compute_resource_requirements (InferenceComponentComputeResourceRequirements | None)

• base_inference_component_name (str | None)

model_name

Type:

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

container

Type:

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

startup_parameters

Type:

Settings that take effect while the model container starts up.

compute_resource_requirements

Type:

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component. Omit this parameter if your request is meant to create an adapter inference component. An adapter inference component is loaded by a base inference component, and it uses the compute resources of the base inference component.

base_inference_component_name

Type:

The name of an existing inference component that is to contain the inference component that you’re creating with your request. Specify this parameter only if your request is meant to create an adapter inference component. An adapter inference component contains the path to an adapter model. The purpose of the adapter model is to tailor the inference output of a base foundation model, which is hosted by the base inference component. The adapter inference component uses the compute resources that you assigned to the base inference component. When you create an adapter inference component, use the Container parameter to specify the location of the adapter artifacts. In the parameter value, use the ArtifactUrl parameter of the InferenceComponentContainerSpecification data type. Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentSpecificationSummary(*, model_name=<sagemaker_core.main.utils.Unassigned object>, container=<sagemaker_core.main.utils.Unassigned object>, startup_parameters=<sagemaker_core.main.utils.Unassigned object>, compute_resource_requirements=<sagemaker_core.main.utils.Unassigned object>, base_inference_component_name=<sagemaker_core.main.utils.Unassigned object>)

Details about the resources that are deployed with this inference component.

Parameters:

• model_name (str | object | None)

• container (InferenceComponentContainerSpecificationSummary | None)

• startup_parameters (InferenceComponentStartupParameters | None)

• compute_resource_requirements (InferenceComponentComputeResourceRequirements | None)

• base_inference_component_name (str | None)

model_name

Type:

The name of the SageMaker AI model object that is deployed with the inference component.

container

Type:

Details about the container that provides the runtime environment for the model that is deployed with the inference component.

startup_parameters

Type:

Settings that take effect while the model container starts up.

compute_resource_requirements

Type:

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

base_inference_component_name

Type:

The name of the base inference component that contains this inference component.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentStartupParameters(*, model_data_download_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>, container_startup_health_check_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

Settings that take effect while the model container starts up.

Parameters:

• model_data_download_timeout_in_seconds (int | None)

• container_startup_health_check_timeout_in_seconds (int | None)

model_data_download_timeout_in_seconds

Type:

The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

container_startup_health_check_timeout_in_seconds

Type:

The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceComponentSummary(*, creation_time, inference_component_arn, inference_component_name, endpoint_arn, endpoint_name, variant_name, last_modified_time, inference_component_status=<sagemaker_core.main.utils.Unassigned object>)

A summary of the properties of an inference component.

Parameters:

• creation_time (datetime)

• inference_component_arn (str)

• inference_component_name (str | object)

• endpoint_arn (str)

• endpoint_name (str | object)

• variant_name (str)

• last_modified_time (datetime)

• inference_component_status (str | None)

creation_time

Type:

The time when the inference component was created.

inference_component_arn

Type:

The Amazon Resource Name (ARN) of the inference component.

inference_component_name

Type:

The name of the inference component.

endpoint_arn

Type:

The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.

endpoint_name

Type:

The name of the endpoint that hosts the inference component.

variant_name

Type:

The name of the production variant that hosts the inference component.

inference_component_status

Type:

The status of the inference component.

last_modified_time

Type:

The time when the inference component was last updated.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceExecutionConfig(*, mode)

Specifies details about how containers in a multi-container endpoint are run.

Parameters:

mode (str)

mode

Type:

How containers in a multi-container are run. The following values are valid. SERIAL - Containers run as a serial pipeline. DIRECT - Only the individual container that you specify is run.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceExperimentDataStorageConfig(*, destination, kms_key=<sagemaker_core.main.utils.Unassigned object>, content_type=<sagemaker_core.main.utils.Unassigned object>)

The Amazon S3 location and configuration for storing inference request and response data.

Parameters:

• destination (str)

• kms_key (str | None)

• content_type (CaptureContentTypeHeader | None)

destination

Type:

The Amazon S3 bucket where the inference request and response data is stored.

kms_key

Type:

The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.

content_type

Type:

sagemaker_core.main.shapes.CaptureContentTypeHeader | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceExperimentSchedule(*, start_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>)

The start and end times of an inference experiment. The maximum duration that you can set for an inference experiment is 30 days.

Parameters:

• start_time (datetime | None)

• end_time (datetime | None)

start_time

Type:

The timestamp at which the inference experiment started or will start.

end_time

Type:

The timestamp at which the inference experiment ended or will end.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceExperimentSummary(*, name, type, status, creation_time, last_modified_time, schedule=<sagemaker_core.main.utils.Unassigned object>, status_reason=<sagemaker_core.main.utils.Unassigned object>, description=<sagemaker_core.main.utils.Unassigned object>, completion_time=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>)

Lists a summary of properties of an inference experiment.

Parameters:

• name (str)

• type (str)

• status (str)

• creation_time (datetime)

• last_modified_time (datetime)

• schedule (InferenceExperimentSchedule | None)

• status_reason (str | None)

• description (str | None)

• completion_time (datetime | None)

• role_arn (str | None)

name

Type:

The name of the inference experiment.

type

Type:

The type of the inference experiment.

schedule

Type:

The duration for which the inference experiment ran or will run. The maximum duration that you can set for an inference experiment is 30 days.

status

Type:

The status of the inference experiment.

status_reason

Type:

The error message for the inference experiment status result.

description

Type:

The description of the inference experiment.

creation_time

Type:

The timestamp at which the inference experiment was created.

completion_time

Type:

The timestamp at which the inference experiment was completed.

last_modified_time

Type:

The timestamp when you last modified the inference experiment.

role_arn

Type:

The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceHubAccessConfig(*, hub_content_arn)

Configuration information specifying which hub contents have accessible deployment options.

Parameters:

hub_content_arn (str)

hub_content_arn

Type:

The ARN of the hub content for which deployment access is allowed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceMetrics(*, max_invocations, model_latency)

The metrics for an existing endpoint compared in an Inference Recommender job.

Parameters:

• max_invocations (int)

• model_latency (int)

max_invocations

Type:

The expected maximum number of requests per minute for the instance.

model_latency

Type:

The expected model latency at maximum invocations per minute for the instance.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceRecommendation(*, endpoint_configuration, model_configuration, recommendation_id=<sagemaker_core.main.utils.Unassigned object>, metrics=<sagemaker_core.main.utils.Unassigned object>, invocation_end_time=<sagemaker_core.main.utils.Unassigned object>, invocation_start_time=<sagemaker_core.main.utils.Unassigned object>)

A list of recommendations made by Amazon SageMaker Inference Recommender.

Parameters:

• endpoint_configuration (EndpointOutputConfiguration)

• model_configuration (ModelConfiguration)

• recommendation_id (str | None)

• metrics (RecommendationMetrics | None)

• invocation_end_time (datetime | None)

• invocation_start_time (datetime | None)

recommendation_id

Type:

The recommendation ID which uniquely identifies each recommendation.

metrics

Type:

The metrics used to decide what recommendation to make.

endpoint_configuration

Type:

Defines the endpoint configuration parameters.

model_configuration

Type:

Defines the model configuration.

invocation_end_time

Type:

A timestamp that shows when the benchmark completed.

invocation_start_time

Type:

A timestamp that shows when the benchmark started.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceRecommendationsJob(*, job_name, job_description, job_type, job_arn, status, creation_time, role_arn, last_modified_time, completion_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, model_name=<sagemaker_core.main.utils.Unassigned object>, sample_payload_url=<sagemaker_core.main.utils.Unassigned object>, model_package_version_arn=<sagemaker_core.main.utils.Unassigned object>)

A structure that contains a list of recommendation jobs.

Parameters:

• job_name (str)

• job_description (str)

• job_type (str)

• job_arn (str)

• status (str)

• creation_time (datetime)

• role_arn (str)

• last_modified_time (datetime)

• completion_time (datetime | None)

• failure_reason (str | None)

• model_name (str | object | None)

• sample_payload_url (str | None)

• model_package_version_arn (str | None)

job_name

Type:

The name of the job.

job_description

Type:

The job description.

job_type

Type:

The recommendation job type.

job_arn

Type:

The Amazon Resource Name (ARN) of the recommendation job.

status

Type:

The status of the job.

creation_time

Type:

A timestamp that shows when the job was created.

completion_time

Type:

A timestamp that shows when the job completed.

role_arn

Type:

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

last_modified_time

Type:

A timestamp that shows when the job was last modified.

failure_reason

Type:

If the job fails, provides information why the job failed.

model_name

Type:

The name of the created model.

sample_payload_url

Type:

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

model_package_version_arn

Type:

The Amazon Resource Name (ARN) of a versioned model package.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceRecommendationsJobStep(*, step_type, job_name, status, inference_benchmark=<sagemaker_core.main.utils.Unassigned object>)

A returned array object for the Steps response field in the ListInferenceRecommendationsJobSteps API command.

Parameters:

• step_type (str)

• job_name (str)

• status (str)

• inference_benchmark (RecommendationJobInferenceBenchmark | None)

step_type

Type:

The type of the subtask. BENCHMARK: Evaluate the performance of your model on different instance types.

job_name

Type:

The name of the Inference Recommender job.

status

Type:

The current status of the benchmark.

inference_benchmark

Type:

The details for a specific benchmark.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InferenceSpecification(*, containers, supported_transform_instance_types=<sagemaker_core.main.utils.Unassigned object>, supported_realtime_inference_instance_types=<sagemaker_core.main.utils.Unassigned object>, supported_content_types=<sagemaker_core.main.utils.Unassigned object>, supported_response_mime_types=<sagemaker_core.main.utils.Unassigned object>)

Defines how to perform inference generation after a training job is run.

Parameters:

• containers (List[ModelPackageContainerDefinition])

• supported_transform_instance_types (List[str] | None)

• supported_realtime_inference_instance_types (List[str] | None)

• supported_content_types (List[str] | None)

• supported_response_mime_types (List[str] | None)

containers

Type:

The Amazon ECR registry path of the Docker image that contains the inference code.

supported_transform_instance_types

Type:

A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed. This parameter is required for unversioned models, and optional for versioned models.

supported_realtime_inference_instance_types

Type:

A list of the instance types that are used to generate inferences in real-time. This parameter is required for unversioned models, and optional for versioned models.

supported_content_types

Type:

The supported MIME types for the input data.

supported_response_mime_types

Type:

The supported MIME types for the output data.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InfraCheckConfig(*, enable_infra_check=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for the infrastructure health check of a training job. A SageMaker-provided health check tests the health of instance hardware and cluster network connectivity.

Parameters:

enable_infra_check (bool | None)

enable_infra_check

Type:

Enables an infrastructure health check.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InputConfig(*, s3_uri, framework, data_input_config=<sagemaker_core.main.utils.Unassigned object>, framework_version=<sagemaker_core.main.utils.Unassigned object>)

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

Parameters:

• s3_uri (str)

• framework (str)

• data_input_config (str | None)

• framework_version (str | None)

s3_uri

Type:

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

data_input_config

Type:

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific. TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different. Examples for one input: If using the console, {“input”:[1,1024,1024,3]} If using the CLI, {“input”:[1,1024,1024,3]} Examples for two inputs: If using the console, {“data1”: [1,28,28,1], “data2”:[1,28,28,1]} If using the CLI, {“data1”: [1,28,28,1], “data2”:[1,28,28,1]} KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different. Examples for one input: If using the console, {“input_1”:[1,3,224,224]} If using the CLI, {“input_1”:[1,3,224,224]} Examples for two inputs: If using the console, {“input_1”: [1,3,224,224], “input_2”:[1,3,224,224]} If using the CLI, {“input_1”: [1,3,224,224], “input_2”:[1,3,224,224]} MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different. Examples for one input: If using the console, {“data”:[1,3,1024,1024]} If using the CLI, {“data”:[1,3,1024,1024]} Examples for two inputs: If using the console, {“var1”: [1,1,28,28], “var2”:[1,1,28,28]} If using the CLI, {“var1”: [1,1,28,28], “var2”:[1,1,28,28]} PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same. Examples for one input in dictionary format: If using the console, {“input0”:[1,3,224,224]} If using the CLI, {“input0”:[1,3,224,224]} Example for one input in list format: [[1,3,224,224]] Examples for two inputs in dictionary format: If using the console, {“input0”:[1,3,224,224], “input1”:[1,3,224,224]} If using the CLI, {“input0”:[1,3,224,224], “input1”:[1,3,224,224]} Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]] XGBOOST: input data name and shape are not needed. DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format): shape: Input shape, for example {“input_1”: {“shape”: [1,224,224,3]}}. In addition to static input shapes, CoreML converter supports Flexible input shapes: Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {“input_1”: {“shape”: [“1..10”, 224, 224, 3]}} Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {“input_1”: {“shape”: [[1, 224, 224, 3], [1, 160, 160, 3]]}} default_shape: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {“input_1”: {“shape”: [“1..10”, 224, 224, 3], “default_shape”: [1, 224, 224, 3]}} type: Input type. Allowed values: Image and Tensor. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale. bias: If the input type is an Image, you need to provide the bias vector. scale: If the input type is an Image, you need to provide a scale factor. CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples: Tensor type input: “DataInputConfig”: {“input_1”: {“shape”: [[1,224,224,3], [1,160,160,3]], “default_shape”: [1,224,224,3]}} Tensor type input without input name (PyTorch): “DataInputConfig”: [{“shape”: [[1,3,224,224], [1,3,160,160]], “default_shape”: [1,3,224,224]}] Image type input: “DataInputConfig”: {“input_1”: {“shape”: [[1,224,224,3], [1,160,160,3]], “default_shape”: [1,224,224,3], “type”: “Image”, “bias”: [-1,-1,-1], “scale”: 0.007843137255}} “CompilerOptions”: {“class_labels”: “imagenet_labels_1000.txt”} Image type input without input name (PyTorch): “DataInputConfig”: [{“shape”: [[1,3,224,224], [1,3,160,160]], “default_shape”: [1,3,224,224], “type”: “Image”, “bias”: [-1,-1,-1], “scale”: 0.007843137255}] “CompilerOptions”: {“class_labels”: “imagenet_labels_1000.txt”} Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice. For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow’s default signature def key. For example: “DataInputConfig”: {“inputs”: [1, 224, 224, 3]} “CompilerOptions”: {“signature_def_key”: “serving_custom”} For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example: “DataInputConfig”: {“input_tensor:0”: [1, 224, 224, 3]} “CompilerOptions”: {“output_names”: [“output_tensor:0”]}

framework

Type:

Identifies the framework in which the model was trained. For example: TENSORFLOW.

framework_version

Type:

Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks. For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InstanceGroup(*, instance_type, instance_count, instance_group_name)

Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

Parameters:

• instance_type (str)

• instance_count (int)

• instance_group_name (str)

instance_type

Type:

Specifies the instance type of the instance group.

instance_count

Type:

Specifies the number of instances of the instance group.

instance_group_name

Type:

Specifies the name of the instance group.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InstanceMetadataServiceConfiguration(*, minimum_instance_metadata_service_version)

Information on the IMDS configuration of the notebook instance

Parameters:

minimum_instance_metadata_service_version (str)

minimum_instance_metadata_service_version

Type:

Indicates the minimum IMDS version that the notebook instance supports. When passed as part of CreateNotebookInstance, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part of UpdateNotebookInstance, there is no default.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.IntegerParameterRange(*, name, min_value, max_value, scaling_type=<sagemaker_core.main.utils.Unassigned object>)

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

Parameters:

• name (str)

• min_value (str)

• max_value (str)

• scaling_type (str | None)

name

Type:

The name of the hyperparameter to search.

min_value

Type:

The minimum value of the hyperparameter to search.

max_value

Type:

The maximum value of the hyperparameter to search.

scaling_type

Type:

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values: Auto SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. Linear Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.IntegerParameterRangeSpecification(*, min_value, max_value)

Defines the possible values for an integer hyperparameter.

Parameters:

• min_value (str)

• max_value (str)

min_value

Type:

The minimum integer value allowed.

max_value

Type:

The maximum integer value allowed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InternalDependencyException(*, message=<sagemaker_core.main.utils.Unassigned object>)

Your request caused an exception with an internal dependency. Contact customer support.

Parameters:

message (str | None)

message

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InternalFailure(*, message=<sagemaker_core.main.utils.Unassigned object>)

An internal failure occurred. Try your request again. If the problem persists, contact Amazon Web Services customer support.

Parameters:

message (str | None)

message

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InternalStreamFailure(*, message=<sagemaker_core.main.utils.Unassigned object>)

The stream processing failed because of an unknown error, exception or failure. Try your request again.

Parameters:

message (str | None)

message

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InvokeEndpointAsyncOutput(*, inference_id=<sagemaker_core.main.utils.Unassigned object>, output_location=<sagemaker_core.main.utils.Unassigned object>, failure_location=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• inference_id (str | None)

• output_location (str | None)

• failure_location (str | None)

inference_id

Type:

Identifier for an inference request. This will be the same as the InferenceId specified in the input. Amazon SageMaker will generate an identifier for you if you do not specify one.

output_location

Type:

The Amazon S3 URI where the inference response payload is stored.

failure_location

Type:

The Amazon S3 URI where the inference failure response payload is stored.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InvokeEndpointOutput(*, body, content_type=<sagemaker_core.main.utils.Unassigned object>, invoked_production_variant=<sagemaker_core.main.utils.Unassigned object>, custom_attributes=<sagemaker_core.main.utils.Unassigned object>, new_session_id=<sagemaker_core.main.utils.Unassigned object>, closed_session_id=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• body (Any)

• content_type (str | None)

• invoked_production_variant (str | None)

• custom_attributes (str | None)

• new_session_id (str | None)

• closed_session_id (str | None)

body

Type:

Includes the inference provided by the model. For information about the format of the response body, see Common Data Formats-Inference. If the explainer is activated, the body includes the explanations provided by the model. For more information, see the Response section under Invoke the Endpoint in the Developer Guide.

content_type

Type:

The MIME type of the inference returned from the model container.

invoked_production_variant

Type:

Identifies the production variant that was invoked.

custom_attributes

Type:

Provides additional information in the response about the inference returned by a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to return an ID received in the CustomAttributes header of a request or other metadata that a service endpoint was programmed to produce. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1). If the customer wants the custom attribute returned, the model must set the custom attribute to be included on the way back. The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function. This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

new_session_id

Type:

If you created a stateful session with your request, the ID and expiration time that the model assigns to that session.

closed_session_id

Type:

If you closed a stateful session with your request, the ID of that session.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.InvokeEndpointWithResponseStreamOutput(*, body, content_type=<sagemaker_core.main.utils.Unassigned object>, invoked_production_variant=<sagemaker_core.main.utils.Unassigned object>, custom_attributes=<sagemaker_core.main.utils.Unassigned object>)

Parameters:

• body (ResponseStream)

• content_type (str | None)

• invoked_production_variant (str | None)

• custom_attributes (str | None)

body

Type:

sagemaker_core.main.shapes.ResponseStream

content_type

Type:

The MIME type of the inference returned from the model container.

invoked_production_variant

Type:

Identifies the production variant that was invoked.

custom_attributes

Type:

Provides additional information in the response about the inference returned by a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to return an ID received in the CustomAttributes header of a request or other metadata that a service endpoint was programmed to produce. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1). If the customer wants the custom attribute returned, the model must set the custom attribute to be included on the way back. The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function. This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.JupyterLabAppImageConfig(*, file_system_config=<sagemaker_core.main.utils.Unassigned object>, container_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration for the file system and kernels in a SageMaker AI image running as a JupyterLab app. The FileSystemConfig object is not supported.

Parameters:

• file_system_config (FileSystemConfig | None)

• container_config (ContainerConfig | None)

file_system_config

Type:

sagemaker_core.main.shapes.FileSystemConfig | None

container_config

Type:

sagemaker_core.main.shapes.ContainerConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.JupyterLabAppSettings(*, default_resource_spec=<sagemaker_core.main.utils.Unassigned object>, custom_images=<sagemaker_core.main.utils.Unassigned object>, lifecycle_config_arns=<sagemaker_core.main.utils.Unassigned object>, code_repositories=<sagemaker_core.main.utils.Unassigned object>, app_lifecycle_management=<sagemaker_core.main.utils.Unassigned object>, emr_settings=<sagemaker_core.main.utils.Unassigned object>, built_in_lifecycle_config_arn=<sagemaker_core.main.utils.Unassigned object>)

The settings for the JupyterLab application.

Parameters:

• default_resource_spec (ResourceSpec | None)

• custom_images (List[CustomImage] | None)

• lifecycle_config_arns (List[str] | None)

• code_repositories (List[CodeRepository] | None)

• app_lifecycle_management (AppLifecycleManagement | None)

• emr_settings (EmrSettings | None)

• built_in_lifecycle_config_arn (str | None)

default_resource_spec

Type:

sagemaker_core.main.shapes.ResourceSpec | None

custom_images

Type:

A list of custom SageMaker images that are configured to run as a JupyterLab app.

lifecycle_config_arns

Type:

The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.

code_repositories

Type:

A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.

app_lifecycle_management

Type:

Indicates whether idle shutdown is activated for JupyterLab applications.

emr_settings

Type:

The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.

built_in_lifecycle_config_arn

Type:

The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.JupyterServerAppSettings(*, default_resource_spec=<sagemaker_core.main.utils.Unassigned object>, lifecycle_config_arns=<sagemaker_core.main.utils.Unassigned object>, code_repositories=<sagemaker_core.main.utils.Unassigned object>)

The JupyterServer app settings.

Parameters:

• default_resource_spec (ResourceSpec | None)

• lifecycle_config_arns (List[str] | None)

• code_repositories (List[CodeRepository] | None)

default_resource_spec

Type:

The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

lifecycle_config_arns

Type:

The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required. To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

code_repositories

Type:

A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.KendraSettings(*, status=<sagemaker_core.main.utils.Unassigned object>)

The Amazon SageMaker Canvas application setting where you configure document querying.

Parameters:

status (str | None)

status

Type:

Describes whether the document querying feature is enabled or disabled in the Canvas application.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.KernelGatewayAppSettings(*, default_resource_spec=<sagemaker_core.main.utils.Unassigned object>, custom_images=<sagemaker_core.main.utils.Unassigned object>, lifecycle_config_arns=<sagemaker_core.main.utils.Unassigned object>)

The KernelGateway app settings.

Parameters:

• default_resource_spec (ResourceSpec | None)

• custom_images (List[CustomImage] | None)

• lifecycle_config_arns (List[str] | None)

default_resource_spec

Type:

The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app. The Amazon SageMaker AI Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.

custom_images

Type:

A list of custom SageMaker AI images that are configured to run as a KernelGateway app. The maximum number of custom images are as follows. On a domain level: 200 On a space level: 5 On a user profile level: 5

lifecycle_config_arns

Type:

The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain. To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.KernelGatewayImageConfig(*, kernel_specs, file_system_config=<sagemaker_core.main.utils.Unassigned object>)

The configuration for the file system and kernels in a SageMaker AI image running as a KernelGateway app.

Parameters:

• kernel_specs (List[KernelSpec])

• file_system_config (FileSystemConfig | None)

kernel_specs

Type:

The specification of the Jupyter kernels in the image.

file_system_config

Type:

The Amazon Elastic File System storage configuration for a SageMaker AI image.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.KernelSpec(*, name, display_name=<sagemaker_core.main.utils.Unassigned object>)

The specification of a Jupyter kernel.

Parameters:

• name (str)

• display_name (str | None)

name

Type:

The name of the Jupyter kernel in the image. This value is case sensitive.

display_name

Type:

The display name of the kernel.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelCounters(*, total_labeled=<sagemaker_core.main.utils.Unassigned object>, human_labeled=<sagemaker_core.main.utils.Unassigned object>, machine_labeled=<sagemaker_core.main.utils.Unassigned object>, failed_non_retryable_error=<sagemaker_core.main.utils.Unassigned object>, unlabeled=<sagemaker_core.main.utils.Unassigned object>)

Provides a breakdown of the number of objects labeled.

Parameters:

• total_labeled (int | None)

• human_labeled (int | None)

• machine_labeled (int | None)

• failed_non_retryable_error (int | None)

• unlabeled (int | None)

total_labeled

Type:

The total number of objects labeled.

human_labeled

Type:

The total number of objects labeled by a human worker.

machine_labeled

Type:

The total number of objects labeled by automated data labeling.

failed_non_retryable_error

Type:

The total number of objects that could not be labeled due to an error.

unlabeled

Type:

The total number of objects not yet labeled.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelCountersForWorkteam(*, human_labeled=<sagemaker_core.main.utils.Unassigned object>, pending_human=<sagemaker_core.main.utils.Unassigned object>, total=<sagemaker_core.main.utils.Unassigned object>)

Provides counts for human-labeled tasks in the labeling job.

Parameters:

• human_labeled (int | None)

• pending_human (int | None)

• total (int | None)

human_labeled

Type:

The total number of data objects labeled by a human worker.

pending_human

Type:

The total number of data objects that need to be labeled by a human worker.

total

Type:

The total number of tasks in the labeling job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobAlgorithmsConfig(*, labeling_job_algorithm_specification_arn, initial_active_learning_model_arn=<sagemaker_core.main.utils.Unassigned object>, labeling_job_resource_config=<sagemaker_core.main.utils.Unassigned object>)

Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.

Parameters:

• labeling_job_algorithm_specification_arn (str)

• initial_active_learning_model_arn (str | None)

• labeling_job_resource_config (LabelingJobResourceConfig | None)

labeling_job_algorithm_specification_arn

Type:

Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs: Image classification arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification Text classification arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification Object detection arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection Semantic Segmentation arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation

initial_active_learning_model_arn

Type:

At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

labeling_job_resource_config

Type:

Provides configuration information for a labeling job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobDataAttributes(*, content_classifiers=<sagemaker_core.main.utils.Unassigned object>)

Attributes of the data specified by the customer. Use these to describe the data to be labeled.

Parameters:

content_classifiers (List[str] | None)

content_classifiers

Type:

Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobDataSource(*, s3_data_source=<sagemaker_core.main.utils.Unassigned object>, sns_data_source=<sagemaker_core.main.utils.Unassigned object>)

Provides information about the location of input data. You must specify at least one of the following: S3DataSource or SnsDataSource. Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job. Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.

Parameters:

• s3_data_source (LabelingJobS3DataSource | None)

• sns_data_source (LabelingJobSnsDataSource | None)

s3_data_source

Type:

The Amazon S3 location of the input data objects.

sns_data_source

Type:

An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobForWorkteamSummary(*, job_reference_code, work_requester_account_id, creation_time, labeling_job_name=<sagemaker_core.main.utils.Unassigned object>, label_counters=<sagemaker_core.main.utils.Unassigned object>, number_of_human_workers_per_data_object=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information for a work team.

Parameters:

• job_reference_code (str)

• work_requester_account_id (str)

• creation_time (datetime)

• labeling_job_name (str | object | None)

• label_counters (LabelCountersForWorkteam | None)

• number_of_human_workers_per_data_object (int | None)

labeling_job_name

Type:

The name of the labeling job that the work team is assigned to.

job_reference_code

Type:

A unique identifier for a labeling job. You can use this to refer to a specific labeling job.

work_requester_account_id

Type:

The Amazon Web Services account ID of the account used to start the labeling job.

creation_time

Type:

The date and time that the labeling job was created.

label_counters

Type:

Provides information about the progress of a labeling job.

number_of_human_workers_per_data_object

Type:

The configured number of workers per data object.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobInputConfig(*, data_source, data_attributes=<sagemaker_core.main.utils.Unassigned object>)

Input configuration information for a labeling job.

Parameters:

• data_source (LabelingJobDataSource)

• data_attributes (LabelingJobDataAttributes | None)

data_source

Type:

The location of the input data.

data_attributes

Type:

Attributes of the data specified by the customer.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobOutput(*, output_dataset_s3_uri, final_active_learning_model_arn=<sagemaker_core.main.utils.Unassigned object>)

Specifies the location of the output produced by the labeling job.

Parameters:

• output_dataset_s3_uri (str)

• final_active_learning_model_arn (str | None)

output_dataset_s3_uri

Type:

The Amazon S3 bucket location of the manifest file for labeled data.

final_active_learning_model_arn

Type:

The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobOutputConfig(*, s3_output_path, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, sns_topic_arn=<sagemaker_core.main.utils.Unassigned object>)

Output configuration information for a labeling job.

Parameters:

• s3_output_path (str)

• kms_key_id (str | None)

• sns_topic_arn (str | None)

s3_output_path

Type:

The Amazon S3 location to write output data.

kms_key_id

Type:

The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any. If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS. If you don’t provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role’s account to encrypt your output data. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to “aws:kms”. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

sns_topic_arn

Type:

An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker. If you provide an SnsTopicArn in OutputConfig, when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here. To learn more, see Receive Output Data from a Streaming Labeling Job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobResourceConfig(*, volume_kms_key_id=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>)

Configure encryption on the storage volume attached to the ML compute instance used to run automated data labeling model training and inference.

Parameters:

• volume_kms_key_id (str | None)

• vpc_config (VpcConfig | None)

volume_kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling. You can only specify a VolumeKmsKeyId when you create a labeling job with automated data labeling enabled using the API operation CreateLabelingJob. You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption. The VolumeKmsKeyId can be any of the following formats: KMS Key ID “1234abcd-12ab-34cd-56ef-1234567890ab” Amazon Resource Name (ARN) of a KMS Key “arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab”

vpc_config

Type:

sagemaker_core.main.shapes.VpcConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobS3DataSource(*, manifest_s3_uri)

The Amazon S3 location of the input data objects.

Parameters:

manifest_s3_uri (str)

manifest_s3_uri

Type:

The Amazon S3 location of the manifest file that describes the input data objects. The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source. The value of the keys are interpreted as follows: source-ref: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. source: The source of the object is the value. Use this value when the object is a text value. If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobSnsDataSource(*, sns_topic_arn)

An Amazon SNS data source used for streaming labeling jobs.

Parameters:

sns_topic_arn (str)

sns_topic_arn

Type:

The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobStoppingConditions(*, max_human_labeled_object_count=<sagemaker_core.main.utils.Unassigned object>, max_percentage_of_input_dataset_labeled=<sagemaker_core.main.utils.Unassigned object>)

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling. Labeling jobs fail after 30 days with an appropriate client error message.

Parameters:

• max_human_labeled_object_count (int | None)

• max_percentage_of_input_dataset_labeled (int | None)

max_human_labeled_object_count

Type:

The maximum number of objects that can be labeled by human workers.

max_percentage_of_input_dataset_labeled

Type:

The maximum number of input data objects that should be labeled.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LabelingJobSummary(*, labeling_job_name, labeling_job_arn, creation_time, last_modified_time, labeling_job_status, label_counters, workteam_arn, pre_human_task_lambda_arn=<sagemaker_core.main.utils.Unassigned object>, annotation_consolidation_lambda_arn=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, labeling_job_output=<sagemaker_core.main.utils.Unassigned object>, input_config=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information about a labeling job.

Parameters:

• labeling_job_name (str | object)

• labeling_job_arn (str)

• creation_time (datetime)

• last_modified_time (datetime)

• labeling_job_status (str)

• label_counters (LabelCounters)

• workteam_arn (str)

• pre_human_task_lambda_arn (str | None)

• annotation_consolidation_lambda_arn (str | None)

• failure_reason (str | None)

• labeling_job_output (LabelingJobOutput | None)

• input_config (LabelingJobInputConfig | None)

labeling_job_name

Type:

The name of the labeling job.

labeling_job_arn

Type:

The Amazon Resource Name (ARN) assigned to the labeling job when it was created.

creation_time

Type:

The date and time that the job was created (timestamp).

last_modified_time

Type:

The date and time that the job was last modified (timestamp).

labeling_job_status

Type:

The current status of the labeling job.

label_counters

Type:

Counts showing the progress of the labeling job.

workteam_arn

Type:

The Amazon Resource Name (ARN) of the work team assigned to the job.

pre_human_task_lambda_arn

Type:

The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.

annotation_consolidation_lambda_arn

Type:

The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.

failure_reason

Type:

If the LabelingJobStatus field is Failed, this field contains a description of the error.

labeling_job_output

Type:

The location of the output produced by the labeling job.

input_config

Type:

Input configuration for the labeling job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LambdaStepMetadata(*, arn=<sagemaker_core.main.utils.Unassigned object>, output_parameters=<sagemaker_core.main.utils.Unassigned object>)

Metadata for a Lambda step.

Parameters:

• arn (str | None)

• output_parameters (List[OutputParameter] | None)

arn

Type:

The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution.

output_parameters

Type:

A list of the output parameters of the Lambda step.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LastUpdateStatus(*, status, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

A value that indicates whether the update was successful.

Parameters:

• status (str)

• failure_reason (str | None)

status

Type:

A value that indicates whether the update was made successful.

failure_reason

Type:

If the update wasn’t successful, indicates the reason why it failed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.LineageGroupSummary(*, lineage_group_arn=<sagemaker_core.main.utils.Unassigned object>, lineage_group_name=<sagemaker_core.main.utils.Unassigned object>, display_name=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Lists a summary of the properties of a lineage group. A lineage group provides a group of shareable lineage entity resources.

Parameters:

• lineage_group_arn (str | None)

• lineage_group_name (str | object | None)

• display_name (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

lineage_group_arn

Type:

The Amazon Resource Name (ARN) of the lineage group resource.

lineage_group_name

Type:

The name or Amazon Resource Name (ARN) of the lineage group.

display_name

Type:

The display name of the lineage group summary.

creation_time

Type:

The creation time of the lineage group summary.

last_modified_time

Type:

The last modified time of the lineage group summary.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MemberDefinition(*, cognito_member_definition=<sagemaker_core.main.utils.Unassigned object>, oidc_member_definition=<sagemaker_core.main.utils.Unassigned object>)

Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.

Parameters:

• cognito_member_definition (CognitoMemberDefinition | None)

• oidc_member_definition (OidcMemberDefinition | None)

cognito_member_definition

Type:

The Amazon Cognito user group that is part of the work team.

oidc_member_definition

Type:

A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetadataProperties(*, commit_id=<sagemaker_core.main.utils.Unassigned object>, repository=<sagemaker_core.main.utils.Unassigned object>, generated_by=<sagemaker_core.main.utils.Unassigned object>, project_id=<sagemaker_core.main.utils.Unassigned object>)

Metadata properties of the tracking entity, trial, or trial component.

Parameters:

• commit_id (str | None)

• repository (str | None)

• generated_by (str | None)

• project_id (str | None)

commit_id

Type:

The commit ID.

repository

Type:

The repository.

generated_by

Type:

The entity this entity was generated by.

project_id

Type:

The project ID.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricData(*, metric_name=<sagemaker_core.main.utils.Unassigned object>, value=<sagemaker_core.main.utils.Unassigned object>, timestamp=<sagemaker_core.main.utils.Unassigned object>)

The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.

Parameters:

• metric_name (str | None)

• value (float | None)

• timestamp (datetime | None)

metric_name

Type:

The name of the metric.

value

Type:

The value of the metric.

timestamp

Type:

The date and time that the algorithm emitted the metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricDatum(*, metric_name=<sagemaker_core.main.utils.Unassigned object>, value=<sagemaker_core.main.utils.Unassigned object>, set=<sagemaker_core.main.utils.Unassigned object>, standard_metric_name=<sagemaker_core.main.utils.Unassigned object>)

Information about the metric for a candidate produced by an AutoML job.

Parameters:

• metric_name (str | None)

• value (float | None)

• set (str | None)

• standard_metric_name (str | None)

metric_name

Type:

The name of the metric.

value

Type:

The value of the metric.

set

Type:

The dataset split from which the AutoML job produced the metric.

standard_metric_name

Type:

The name of the standard metric. For definitions of the standard metrics, see Autopilot candidate metrics .

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricDefinition(*, name, regex)

Specifies a metric that the training algorithm writes to stderr or stdout. You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

Parameters:

• name (str)

• regex (str)

name

Type:

The name of the metric.

regex

Type:

A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricQuery(*, metric_name, resource_arn, metric_stat, period, x_axis_type, start=<sagemaker_core.main.utils.Unassigned object>, end=<sagemaker_core.main.utils.Unassigned object>)

Specifies a query to retrieve training metrics from SageMaker.

Parameters:

• metric_name (str)

• resource_arn (str)

• metric_stat (str)

• period (str)

• x_axis_type (str)

• start (int | None)

• end (int | None)

metric_name

Type:

The name of the metric to retrieve.

resource_arn

Type:

The ARN of the SageMaker resource to retrieve metrics for.

metric_stat

Type:

The metrics stat type of metrics to retrieve.

period

Type:

The time period of metrics to retrieve.

x_axis_type

Type:

The x-axis type of metrics to retrieve.

start

Type:

The start time of metrics to retrieve.

end

Type:

The end time of metrics to retrieve.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricQueryResult(*, status, x_axis_values, metric_values, message=<sagemaker_core.main.utils.Unassigned object>)

The result of a query to retrieve training metrics from SageMaker.

Parameters:

• status (str)

• x_axis_values (List[int])

• metric_values (List[float])

• message (str | None)

status

Type:

The status of the metric query.

message

Type:

A message describing the status of the metric query.

x_axis_values

Type:

The values for the x-axis of the metrics.

metric_values

Type:

The metric values retrieved by the query.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricSpecification(*, predefined=<sagemaker_core.main.utils.Unassigned object>, customized=<sagemaker_core.main.utils.Unassigned object>)

An object containing information about a metric.

Parameters:

• predefined (PredefinedMetricSpecification | None)

• customized (CustomizedMetricSpecification | None)

predefined

Type:

Information about a predefined metric.

customized

Type:

Information about a customized metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MetricsSource(*, content_type, s3_uri, content_digest=<sagemaker_core.main.utils.Unassigned object>)

Details about the metrics source.

Parameters:

• content_type (str)

• s3_uri (str)

• content_digest (str | None)

content_type

Type:

The metric source content type.

content_digest

Type:

The hash key used for the metrics source.

s3_uri

Type:

The S3 URI for the metrics source.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Model(*, model_name=<sagemaker_core.main.utils.Unassigned object>, primary_container=<sagemaker_core.main.utils.Unassigned object>, containers=<sagemaker_core.main.utils.Unassigned object>, inference_execution_config=<sagemaker_core.main.utils.Unassigned object>, execution_role_arn=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, model_arn=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, deployment_recommendation=<sagemaker_core.main.utils.Unassigned object>)

The properties of a model as returned by the Search API.

Parameters:

• model_name (str | object | None)

• primary_container (ContainerDefinition | None)

• containers (List[ContainerDefinition] | None)

• inference_execution_config (InferenceExecutionConfig | None)

• execution_role_arn (str | None)

• vpc_config (VpcConfig | None)

• creation_time (datetime | None)

• model_arn (str | None)

• enable_network_isolation (bool | None)

• tags (List[Tag] | None)

• deployment_recommendation (DeploymentRecommendation | None)

model_name

Type:

The name of the model.

primary_container

Type:

sagemaker_core.main.shapes.ContainerDefinition | None

containers

Type:

The containers in the inference pipeline.

inference_execution_config

Type:

sagemaker_core.main.shapes.InferenceExecutionConfig | None

execution_role_arn

Type:

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

vpc_config

Type:

sagemaker_core.main.shapes.VpcConfig | None

creation_time

Type:

A timestamp that indicates when the model was created.

model_arn

Type:

The Amazon Resource Name (ARN) of the model.

enable_network_isolation

Type:

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

tags

Type:

A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

deployment_recommendation

Type:

A set of recommended deployment configurations for the model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelAccessConfig(*, accept_eula)

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA. If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

Parameters:

accept_eula (bool)

accept_eula

Type:

Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelArtifacts(*, s3_model_artifacts)

Provides information about the location that is configured for storing model artifacts. Model artifacts are outputs that result from training a model. They typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata. A SageMaker container stores your trained model artifacts in the /opt/ml/model directory. After training has completed, by default, these artifacts are uploaded to your Amazon S3 bucket as compressed files.

Parameters:

s3_model_artifacts (str)

s3_model_artifacts

Type:

The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelBiasAppSpecification(*, image_uri, config_uri, environment=<sagemaker_core.main.utils.Unassigned object>)

Docker container image configuration object for the model bias job.

Parameters:

• image_uri (str)

• config_uri (str)

• environment (Dict[str, str] | None)

image_uri

Type:

The container image to be run by the model bias job.

config_uri

Type:

JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.

environment

Type:

Sets the environment variables in the Docker container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelBiasBaselineConfig(*, baselining_job_name=<sagemaker_core.main.utils.Unassigned object>, constraints_resource=<sagemaker_core.main.utils.Unassigned object>)

The configuration for a baseline model bias job.

Parameters:

• baselining_job_name (str | None)

• constraints_resource (MonitoringConstraintsResource | None)

baselining_job_name

Type:

The name of the baseline model bias job.

constraints_resource

Type:

sagemaker_core.main.shapes.MonitoringConstraintsResource | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelBiasJobInput(*, ground_truth_s3_input, endpoint_input=<sagemaker_core.main.utils.Unassigned object>, batch_transform_input=<sagemaker_core.main.utils.Unassigned object>)

Inputs for the model bias job.

Parameters:

• ground_truth_s3_input (MonitoringGroundTruthS3Input)

• endpoint_input (EndpointInput | None)

• batch_transform_input (BatchTransformInput | None)

endpoint_input

Type:

sagemaker_core.main.shapes.EndpointInput | None

batch_transform_input

Type:

Input object for the batch transform job.

ground_truth_s3_input

Type:

Location of ground truth labels to use in model bias job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCard(*, model_card_arn=<sagemaker_core.main.utils.Unassigned object>, model_card_name=<sagemaker_core.main.utils.Unassigned object>, model_card_version=<sagemaker_core.main.utils.Unassigned object>, content=<sagemaker_core.main.utils.Unassigned object>, model_card_status=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, model_id=<sagemaker_core.main.utils.Unassigned object>, risk_rating=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>)

An Amazon SageMaker Model Card.

Parameters:

• model_card_arn (str | None)

• model_card_name (str | object | None)

• model_card_version (int | None)

• content (str | None)

• model_card_status (str | None)

• security_config (ModelCardSecurityConfig | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• tags (List[Tag] | None)

• model_id (str | None)

• risk_rating (str | None)

• model_package_group_name (str | object | None)

model_card_arn

Type:

The Amazon Resource Name (ARN) of the model card.

model_card_name

Type:

The unique name of the model card.

model_card_version

Type:

The version of the model card.

content

Type:

The content of the model card. Content uses the model card JSON schema and provided as a string.

model_card_status

Type:

The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. Draft: The model card is a work in progress. PendingReview: The model card is pending review. Approved: The model card is approved. Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

security_config

Type:

The security configuration used to protect model card data.

creation_time

Type:

The date and time that the model card was created.

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

Type:

The date and time that the model card was last modified.

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

tags

Type:

Key-value pairs used to manage metadata for the model card.

model_id

Type:

The unique name (ID) of the model.

risk_rating

Type:

The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings.

model_package_group_name

Type:

The model package group that contains the model package. Only relevant for model cards created for model packages in the Amazon SageMaker Model Registry.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCardExportArtifacts(*, s3_export_artifacts)

The artifacts of the model card export job.

Parameters:

s3_export_artifacts (str)

s3_export_artifacts

Type:

The Amazon S3 URI of the exported model artifacts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCardExportJobSummary(*, model_card_export_job_name, model_card_export_job_arn, status, model_card_name, model_card_version, created_at, last_modified_at)

The summary of the Amazon SageMaker Model Card export job.

Parameters:

• model_card_export_job_name (str | object)

• model_card_export_job_arn (str)

• status (str)

• model_card_name (str | object)

• model_card_version (int)

• created_at (datetime)

• last_modified_at (datetime)

model_card_export_job_name

Type:

The name of the model card export job.

model_card_export_job_arn

Type:

The Amazon Resource Name (ARN) of the model card export job.

status

Type:

The completion status of the model card export job.

model_card_name

Type:

The name of the model card that the export job exports.

model_card_version

Type:

The version of the model card that the export job exports.

created_at

Type:

The date and time that the model card export job was created.

last_modified_at

Type:

The date and time that the model card export job was last modified..

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCardExportOutputConfig(*, s3_output_path)

Configure the export output details for an Amazon SageMaker Model Card.

Parameters:

s3_output_path (str)

s3_output_path

Type:

The Amazon S3 output path to export your model card PDF.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCardSecurityConfig(*, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Configure the security settings to protect model card data.

Parameters:

kms_key_id (str | None)

kms_key_id

Type:

A Key Management Service key ID to use for encrypting a model card.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCardSummary(*, model_card_name, model_card_arn, model_card_status, creation_time, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

A summary of the model card.

Parameters:

• model_card_name (str | object)

• model_card_arn (str)

• model_card_status (str)

• creation_time (datetime)

• last_modified_time (datetime | None)

model_card_name

Type:

The name of the model card.

model_card_arn

Type:

The Amazon Resource Name (ARN) of the model card.

model_card_status

Type:

The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. Draft: The model card is a work in progress. PendingReview: The model card is pending review. Approved: The model card is approved. Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

creation_time

Type:

The date and time that the model card was created.

last_modified_time

Type:

The date and time that the model card was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCardVersionSummary(*, model_card_name, model_card_arn, model_card_status, model_card_version, creation_time, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

A summary of a specific version of the model card.

Parameters:

• model_card_name (str | object)

• model_card_arn (str)

• model_card_status (str)

• model_card_version (int)

• creation_time (datetime)

• last_modified_time (datetime | None)

model_card_name

Type:

The name of the model card.

model_card_arn

Type:

The Amazon Resource Name (ARN) of the model card.

model_card_status

Type:

The approval status of the model card version within your organization. Different organizations might have different criteria for model card review and approval. Draft: The model card is a work in progress. PendingReview: The model card is pending review. Approved: The model card is approved. Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

model_card_version

Type:

A version of the model card.

creation_time

Type:

The date and time that the model card version was created.

last_modified_time

Type:

The time date and time that the model card version was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelClientConfig(*, invocations_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>, invocations_max_retries=<sagemaker_core.main.utils.Unassigned object>)

Configures the timeout and maximum number of retries for processing a transform job invocation.

Parameters:

• invocations_timeout_in_seconds (int | None)

• invocations_max_retries (int | None)

invocations_timeout_in_seconds

Type:

The timeout value in seconds for an invocation request. The default value is 600.

invocations_max_retries

Type:

The maximum number of retries when invocation requests are failing. The default value is 3.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelCompilationConfig(*, image=<sagemaker_core.main.utils.Unassigned object>, override_environment=<sagemaker_core.main.utils.Unassigned object>)

Settings for the model compilation technique that’s applied by a model optimization job.

Parameters:

• image (str | None)

• override_environment (Dict[str, str] | None)

image

Type:

The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.

override_environment

Type:

Environment variables that override the default ones in the model container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelConfiguration(*, inference_specification_name=<sagemaker_core.main.utils.Unassigned object>, environment_parameters=<sagemaker_core.main.utils.Unassigned object>, compilation_job_name=<sagemaker_core.main.utils.Unassigned object>)

Defines the model configuration. Includes the specification name and environment parameters.

Parameters:

• inference_specification_name (str | None)

• environment_parameters (List[EnvironmentParameter] | None)

• compilation_job_name (str | object | None)

inference_specification_name

Type:

The inference specification name in the model package version.

environment_parameters

Type:

Defines the environment parameters that includes key, value types, and values.

compilation_job_name

Type:

The name of the compilation job used to create the recommended model artifacts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDashboardEndpoint(*, endpoint_name, endpoint_arn, creation_time, last_modified_time, endpoint_status)

An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.

Parameters:

• endpoint_name (str | object)

• endpoint_arn (str)

• creation_time (datetime)

• last_modified_time (datetime)

• endpoint_status (str)

endpoint_name

Type:

The endpoint name.

endpoint_arn

Type:

The Amazon Resource Name (ARN) of the endpoint.

creation_time

Type:

A timestamp that indicates when the endpoint was created.

last_modified_time

Type:

The last time the endpoint was modified.

endpoint_status

Type:

The endpoint status.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDashboardIndicatorAction(*, enabled=<sagemaker_core.main.utils.Unassigned object>)

An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into InAlert status.

Parameters:

enabled (bool | None)

enabled

Type:

Indicates whether the alert action is turned on.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDashboardModel(*, model=<sagemaker_core.main.utils.Unassigned object>, endpoints=<sagemaker_core.main.utils.Unassigned object>, last_batch_transform_job=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedules=<sagemaker_core.main.utils.Unassigned object>, model_card=<sagemaker_core.main.utils.Unassigned object>)

A model displayed in the Amazon SageMaker Model Dashboard.

Parameters:

• model (Model | None)

• endpoints (List[ModelDashboardEndpoint] | None)

• last_batch_transform_job (TransformJob | None)

• monitoring_schedules (List[ModelDashboardMonitoringSchedule] | None)

• model_card (ModelDashboardModelCard | None)

model

Type:

A model displayed in the Model Dashboard.

endpoints

Type:

The endpoints that host a model.

last_batch_transform_job

Type:

sagemaker_core.main.shapes.TransformJob | None

monitoring_schedules

Type:

The monitoring schedules for a model.

model_card

Type:

The model card for a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDashboardModelCard(*, model_card_arn=<sagemaker_core.main.utils.Unassigned object>, model_card_name=<sagemaker_core.main.utils.Unassigned object>, model_card_version=<sagemaker_core.main.utils.Unassigned object>, model_card_status=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, model_id=<sagemaker_core.main.utils.Unassigned object>, risk_rating=<sagemaker_core.main.utils.Unassigned object>)

The model card for a model displayed in the Amazon SageMaker Model Dashboard.

Parameters:

• model_card_arn (str | None)

• model_card_name (str | object | None)

• model_card_version (int | None)

• model_card_status (str | None)

• security_config (ModelCardSecurityConfig | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• tags (List[Tag] | None)

• model_id (str | None)

• risk_rating (str | None)

model_card_arn

Type:

The Amazon Resource Name (ARN) for a model card.

model_card_name

Type:

The name of a model card.

model_card_version

Type:

The model card version.

model_card_status

Type:

The model card status.

security_config

Type:

The KMS Key ID (KMSKeyId) for encryption of model card information.

creation_time

Type:

A timestamp that indicates when the model card was created.

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_time

Type:

A timestamp that indicates when the model card was last updated.

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

tags

Type:

The tags associated with a model card.

model_id

Type:

For models created in SageMaker, this is the model ARN. For models created outside of SageMaker, this is a user-customized string.

risk_rating

Type:

A model card’s risk rating. Can be low, medium, or high.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDashboardMonitoringSchedule(*, monitoring_schedule_arn=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_status=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_config=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_alert_summaries=<sagemaker_core.main.utils.Unassigned object>, last_monitoring_execution_summary=<sagemaker_core.main.utils.Unassigned object>, batch_transform_input=<sagemaker_core.main.utils.Unassigned object>)

A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.

Parameters:

• monitoring_schedule_arn (str | None)

• monitoring_schedule_name (str | object | None)

• monitoring_schedule_status (str | None)

• monitoring_type (str | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• monitoring_schedule_config (MonitoringScheduleConfig | None)

• endpoint_name (str | object | None)

• monitoring_alert_summaries (List[MonitoringAlertSummary] | None)

• last_monitoring_execution_summary (MonitoringExecutionSummary | None)

• batch_transform_input (BatchTransformInput | None)

monitoring_schedule_arn

Type:

The Amazon Resource Name (ARN) of a monitoring schedule.

monitoring_schedule_name

Type:

The name of a monitoring schedule.

monitoring_schedule_status

Type:

The status of the monitoring schedule.

monitoring_type

Type:

The monitor type of a model monitor.

failure_reason

Type:

If a monitoring job failed, provides the reason.

creation_time

Type:

A timestamp that indicates when the monitoring schedule was created.

last_modified_time

Type:

A timestamp that indicates when the monitoring schedule was last updated.

monitoring_schedule_config

Type:

sagemaker_core.main.shapes.MonitoringScheduleConfig | None

endpoint_name

Type:

The endpoint which is monitored.

monitoring_alert_summaries

Type:

A JSON array where each element is a summary for a monitoring alert.

last_monitoring_execution_summary

Type:

sagemaker_core.main.shapes.MonitoringExecutionSummary | None

batch_transform_input

Type:

sagemaker_core.main.shapes.BatchTransformInput | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDataQuality(*, statistics=<sagemaker_core.main.utils.Unassigned object>, constraints=<sagemaker_core.main.utils.Unassigned object>)

Data quality constraints and statistics for a model.

Parameters:

• statistics (MetricsSource | None)

• constraints (MetricsSource | None)

statistics

Type:

Data quality statistics for a model.

constraints

Type:

Data quality constraints for a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDataSource(*, s3_data_source=<sagemaker_core.main.utils.Unassigned object>)

Specifies the location of ML model data to deploy. If specified, you must specify one and only one of the available data sources.

Parameters:

s3_data_source (S3ModelDataSource | None)

s3_data_source

Type:

Specifies the S3 location of ML model data to deploy.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDeployConfig(*, auto_generate_endpoint_name=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>)

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Parameters:

• auto_generate_endpoint_name (bool | None)

• endpoint_name (str | object | None)

auto_generate_endpoint_name

Type:

Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False. If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.

endpoint_name

Type:

Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically. Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDeployResult(*, endpoint_name=<sagemaker_core.main.utils.Unassigned object>)

Provides information about the endpoint of the model deployment.

Parameters:

endpoint_name (str | object | None)

endpoint_name

Type:

The name of the endpoint to which the model has been deployed. If model deployment fails, this field is omitted from the response.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelDigests(*, artifact_digest=<sagemaker_core.main.utils.Unassigned object>)

Provides information to verify the integrity of stored model artifacts.

Parameters:

artifact_digest (str | None)

artifact_digest

Type:

Provides a hash value that uniquely identifies the stored model artifacts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelError(*, message=<sagemaker_core.main.utils.Unassigned object>, original_status_code=<sagemaker_core.main.utils.Unassigned object>, original_message=<sagemaker_core.main.utils.Unassigned object>, log_stream_arn=<sagemaker_core.main.utils.Unassigned object>)

Model (owned by the customer in the container) returned 4xx or 5xx error code.

Parameters:

• message (str | None)

• original_status_code (int | None)

• original_message (str | None)

• log_stream_arn (str | None)

message

Type:

str | None

original_status_code

Type:

Original status code.

original_message

Type:

Original message.

log_stream_arn

Type:

The Amazon Resource Name (ARN) of the log stream.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelExplainabilityAppSpecification(*, image_uri, config_uri, environment=<sagemaker_core.main.utils.Unassigned object>)

Docker container image configuration object for the model explainability job.

Parameters:

• image_uri (str)

• config_uri (str)

• environment (Dict[str, str] | None)

image_uri

Type:

The container image to be run by the model explainability job.

config_uri

Type:

JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.

environment

Type:

Sets the environment variables in the Docker container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelExplainabilityBaselineConfig(*, baselining_job_name=<sagemaker_core.main.utils.Unassigned object>, constraints_resource=<sagemaker_core.main.utils.Unassigned object>)

The configuration for a baseline model explainability job.

Parameters:

• baselining_job_name (str | None)

• constraints_resource (MonitoringConstraintsResource | None)

baselining_job_name

Type:

The name of the baseline model explainability job.

constraints_resource

Type:

sagemaker_core.main.shapes.MonitoringConstraintsResource | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelExplainabilityJobInput(*, endpoint_input=<sagemaker_core.main.utils.Unassigned object>, batch_transform_input=<sagemaker_core.main.utils.Unassigned object>)

Inputs for the model explainability job.

Parameters:

• endpoint_input (EndpointInput | None)

• batch_transform_input (BatchTransformInput | None)

endpoint_input

Type:

sagemaker_core.main.shapes.EndpointInput | None

batch_transform_input

Type:

Input object for the batch transform job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelInfrastructureConfig(*, infrastructure_type, real_time_inference_config)

The configuration for the infrastructure that the model will be deployed to.

Parameters:

• infrastructure_type (str)

• real_time_inference_config (RealTimeInferenceConfig)

infrastructure_type

Type:

The inference option to which to deploy your model. Possible values are the following: RealTime: Deploy to real-time inference.

real_time_inference_config

Type:

The infrastructure configuration for deploying the model to real-time inference.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelInput(*, data_input_config)

Input object for the model.

Parameters:

data_input_config (str)

data_input_config

Type:

The input configuration object for the model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelLatencyThreshold(*, percentile=<sagemaker_core.main.utils.Unassigned object>, value_in_milliseconds=<sagemaker_core.main.utils.Unassigned object>)

The model latency threshold.

Parameters:

• percentile (str | None)

• value_in_milliseconds (int | None)

percentile

Type:

The model latency percentile threshold. Acceptable values are P95 and P99. For custom load tests, specify the value as P95.

value_in_milliseconds

Type:

The model latency percentile value in milliseconds.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelLifeCycle(*, stage, stage_status, stage_description=<sagemaker_core.main.utils.Unassigned object>)

A structure describing the current state of the model in its life cycle.

Parameters:

• stage (str)

• stage_status (str)

• stage_description (str | None)

stage

Type:

The current stage in the model life cycle.

stage_status

Type:

The current status of a stage in model life cycle.

stage_description

Type:

Describes the stage related details.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelMetadataFilter(*, name, value)

Part of the search expression. You can specify the name and value (domain, task, framework, framework version, task, and model).

Parameters:

• name (str)

• value (str)

name

Type:

The name of the of the model to filter by.

value

Type:

The value to filter the model metadata.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelMetadataSearchExpression(*, filters=<sagemaker_core.main.utils.Unassigned object>)

One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression’s condition are included in the search results

Parameters:

filters (List[ModelMetadataFilter] | None)

filters

Type:

A list of filter objects.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelMetadataSummary(*, domain, framework, task, model, framework_version)

A summary of the model metadata.

Parameters:

• domain (str)

• framework (str)

• task (str)

• model (str)

• framework_version (str)

domain

Type:

The machine learning domain of the model.

framework

Type:

The machine learning framework of the model.

task

Type:

The machine learning task of the model.

model

Type:

The name of the model.

framework_version

Type:

The framework version of the model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelMetrics(*, model_quality=<sagemaker_core.main.utils.Unassigned object>, model_data_quality=<sagemaker_core.main.utils.Unassigned object>, bias=<sagemaker_core.main.utils.Unassigned object>, explainability=<sagemaker_core.main.utils.Unassigned object>)

Contains metrics captured from a model.

Parameters:

• model_quality (ModelQuality | None)

• model_data_quality (ModelDataQuality | None)

• bias (Bias | None)

• explainability (Explainability | None)

model_quality

Type:

Metrics that measure the quality of a model.

model_data_quality

Type:

Metrics that measure the quality of the input data for a model.

bias

Type:

Metrics that measure bias in a model.

explainability

Type:

Metrics that help explain a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelNotReadyException(*, message=<sagemaker_core.main.utils.Unassigned object>)

Either a serverless endpoint variant’s resources are still being provisioned, or a multi-model endpoint is still downloading or loading the target model. Wait and try your request again.

Parameters:

message (str | None)

message

Type:

str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackage(*, model_package_name=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, model_package_version=<sagemaker_core.main.utils.Unassigned object>, model_package_arn=<sagemaker_core.main.utils.Unassigned object>, model_package_description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, inference_specification=<sagemaker_core.main.utils.Unassigned object>, source_algorithm_specification=<sagemaker_core.main.utils.Unassigned object>, validation_specification=<sagemaker_core.main.utils.Unassigned object>, model_package_status=<sagemaker_core.main.utils.Unassigned object>, model_package_status_details=<sagemaker_core.main.utils.Unassigned object>, certify_for_marketplace=<sagemaker_core.main.utils.Unassigned object>, model_approval_status=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, metadata_properties=<sagemaker_core.main.utils.Unassigned object>, model_metrics=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, approval_description=<sagemaker_core.main.utils.Unassigned object>, domain=<sagemaker_core.main.utils.Unassigned object>, task=<sagemaker_core.main.utils.Unassigned object>, sample_payload_url=<sagemaker_core.main.utils.Unassigned object>, additional_inference_specifications=<sagemaker_core.main.utils.Unassigned object>, source_uri=<sagemaker_core.main.utils.Unassigned object>, security_config=<sagemaker_core.main.utils.Unassigned object>, model_card=<sagemaker_core.main.utils.Unassigned object>, model_life_cycle=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, customer_metadata_properties=<sagemaker_core.main.utils.Unassigned object>, drift_check_baselines=<sagemaker_core.main.utils.Unassigned object>, skip_model_validation=<sagemaker_core.main.utils.Unassigned object>)

A container for your trained model that can be deployed for SageMaker inference. This can include inference code, artifacts, and metadata. The model package type can be one of the following. Versioned model: A part of a model package group in Model Registry. Unversioned model: Not part of a model package group and used in Amazon Web Services Marketplace. For more information, see CreateModelPackage .

Parameters:

• model_package_name (str | object | None)

• model_package_group_name (str | object | None)

• model_package_version (int | None)

• model_package_arn (str | None)

• model_package_description (str | None)

• creation_time (datetime | None)

• inference_specification (InferenceSpecification | None)

• source_algorithm_specification (SourceAlgorithmSpecification | None)

• validation_specification (ModelPackageValidationSpecification | None)

• model_package_status (str | None)

• model_package_status_details (ModelPackageStatusDetails | None)

• certify_for_marketplace (bool | None)

• model_approval_status (str | None)

• created_by (UserContext | None)

• metadata_properties (MetadataProperties | None)

• model_metrics (ModelMetrics | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

• approval_description (str | None)

• domain (str | None)

• task (str | None)

• sample_payload_url (str | None)

• additional_inference_specifications (List[AdditionalInferenceSpecificationDefinition] | None)

• source_uri (str | None)

• security_config (ModelPackageSecurityConfig | None)

• model_card (ModelPackageModelCard | None)

• model_life_cycle (ModelLifeCycle | None)

• tags (List[Tag] | None)

• customer_metadata_properties (Dict[str, str] | None)

• drift_check_baselines (DriftCheckBaselines | None)

• skip_model_validation (str | None)

model_package_name

Type:

The name of the model package. The name can be as follows: For a versioned model, the name is automatically generated by SageMaker Model Registry and follows the format ‘ModelPackageGroupName/ModelPackageVersion’. For an unversioned model, you must provide the name.

model_package_group_name

Type:

The model group to which the model belongs.

model_package_version

Type:

The version number of a versioned model.

model_package_arn

Type:

The Amazon Resource Name (ARN) of the model package.

model_package_description

Type:

The description of the model package.

creation_time

Type:

The time that the model package was created.

inference_specification

Type:

Defines how to perform inference generation after a training job is run.

source_algorithm_specification

Type:

A list of algorithms that were used to create a model package.

validation_specification

Type:

Specifies batch transform jobs that SageMaker runs to validate your model package.

model_package_status

Type:

The status of the model package. This can be one of the following values. PENDING - The model package is pending being created. IN_PROGRESS - The model package is in the process of being created. COMPLETED - The model package was successfully created. FAILED - The model package failed. DELETING - The model package is in the process of being deleted.

model_package_status_details

Type:

Specifies the validation and image scan statuses of the model package.

certify_for_marketplace

Type:

Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace.

model_approval_status

Type:

The approval status of the model. This can be one of the following values. APPROVED - The model is approved REJECTED - The model is rejected. PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.

created_by

Type:

Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

metadata_properties

Type:

Metadata properties of the tracking entity, trial, or trial component.

model_metrics

Type:

Metrics for the model.

last_modified_time

Type:

The last time the model package was modified.

last_modified_by

Type:

Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

approval_description

Type:

A description provided when the model approval is set.

domain

Type:

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

task

Type:

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.

sample_payload_url

Type:

The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

additional_inference_specifications

Type:

An array of additional Inference Specification objects.

source_uri

Type:

The URI of the source for the model package.

security_config

Type:

sagemaker_core.main.shapes.ModelPackageSecurityConfig | None

model_card

Type:

sagemaker_core.main.shapes.ModelPackageModelCard | None

model_life_cycle

Type:

A structure describing the current state of the model in its life cycle.

tags

Type:

A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

customer_metadata_properties

Type:

The metadata properties for the model package.

drift_check_baselines

Type:

Represents the drift check baselines that can be used when the model monitor is set using the model package.

skip_model_validation

Type:

Indicates if you want to skip model validation.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageContainerDefinition(*, image, container_hostname=<sagemaker_core.main.utils.Unassigned object>, image_digest=<sagemaker_core.main.utils.Unassigned object>, model_data_url=<sagemaker_core.main.utils.Unassigned object>, model_data_source=<sagemaker_core.main.utils.Unassigned object>, product_id=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, model_input=<sagemaker_core.main.utils.Unassigned object>, framework=<sagemaker_core.main.utils.Unassigned object>, framework_version=<sagemaker_core.main.utils.Unassigned object>, nearest_model_name=<sagemaker_core.main.utils.Unassigned object>, additional_s3_data_source=<sagemaker_core.main.utils.Unassigned object>, model_data_e_tag=<sagemaker_core.main.utils.Unassigned object>)

Describes the Docker container for the model package.

Parameters:

• image (str)

• container_hostname (str | None)

• image_digest (str | None)

• model_data_url (str | None)

• model_data_source (ModelDataSource | None)

• product_id (str | None)

• environment (Dict[str, str] | None)

• model_input (ModelInput | None)

• framework (str | None)

• framework_version (str | None)

• nearest_model_name (str | None)

• additional_s3_data_source (AdditionalS3DataSource | None)

• model_data_e_tag (str | None)

container_hostname

Type:

The DNS host name for the Docker container.

image

Type:

The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

image_digest

Type:

An MD5 hash of the training algorithm that identifies the Docker image used for training.

model_data_url

Type:

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The model artifacts must be in an S3 bucket that is in the same region as the model package.

model_data_source

Type:

Specifies the location of ML model data to deploy during endpoint creation.

product_id

Type:

The Amazon Web Services Marketplace product ID of the model package.

environment

Type:

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

model_input

Type:

A structure with Model Input details.

framework

Type:

The machine learning framework of the model package container image.

framework_version

Type:

The framework version of the Model Package Container Image.

nearest_model_name

Type:

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

additional_s3_data_source

Type:

The additional data source that is used during inference in the Docker container for your model package.

model_data_e_tag

Type:

The ETag associated with Model Data URL.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageGroup(*, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, model_package_group_arn=<sagemaker_core.main.utils.Unassigned object>, model_package_group_description=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, model_package_group_status=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

A group of versioned models in the Model Registry.

Parameters:

• model_package_group_name (str | object | None)

• model_package_group_arn (str | None)

• model_package_group_description (str | None)

• creation_time (datetime | None)

• created_by (UserContext | None)

• model_package_group_status (str | None)

• tags (List[Tag] | None)

model_package_group_name

Type:

The name of the model group.

model_package_group_arn

Type:

The Amazon Resource Name (ARN) of the model group.

model_package_group_description

Type:

The description for the model group.

creation_time

Type:

The time that the model group was created.

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

model_package_group_status

Type:

The status of the model group. This can be one of the following values. PENDING - The model group is pending being created. IN_PROGRESS - The model group is in the process of being created. COMPLETED - The model group was successfully created. FAILED - The model group failed. DELETING - The model group is in the process of being deleted. DELETE_FAILED - SageMaker failed to delete the model group.

tags

Type:

A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageGroupSummary(*, model_package_group_name, model_package_group_arn, creation_time, model_package_group_status, model_package_group_description=<sagemaker_core.main.utils.Unassigned object>)

Summary information about a model group.

Parameters:

• model_package_group_name (str | object)

• model_package_group_arn (str)

• creation_time (datetime)

• model_package_group_status (str)

• model_package_group_description (str | None)

model_package_group_name

Type:

The name of the model group.

model_package_group_arn

Type:

The Amazon Resource Name (ARN) of the model group.

model_package_group_description

Type:

A description of the model group.

creation_time

Type:

The time that the model group was created.

model_package_group_status

Type:

The status of the model group.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageModelCard(*, model_card_content=<sagemaker_core.main.utils.Unassigned object>, model_card_status=<sagemaker_core.main.utils.Unassigned object>)

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

Parameters:

• model_card_content (str | None)

• model_card_status (str | None)

model_card_content

Type:

The content of the model card. The content must follow the schema described in Model Package Model Card Schema.

model_card_status

Type:

The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. Draft: The model card is a work in progress. PendingReview: The model card is pending review. Approved: The model card is approved. Archived: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the message Model Card is in Archived state.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageSecurityConfig(*, kms_key_id)

An optional Key Management Service key to encrypt, decrypt, and re-encrypt model package information for regulated workloads with highly sensitive data.

Parameters:

kms_key_id (str)

kms_key_id

Type:

The KMS Key ID (KMSKeyId) used for encryption of model package information.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageStatusDetails(*, validation_statuses, image_scan_statuses=<sagemaker_core.main.utils.Unassigned object>)

Specifies the validation and image scan statuses of the model package.

Parameters:

• validation_statuses (List[ModelPackageStatusItem])

• image_scan_statuses (List[ModelPackageStatusItem] | None)

validation_statuses

Type:

The validation status of the model package.

image_scan_statuses

Type:

The status of the scan of the Docker image container for the model package.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageStatusItem(*, name, status, failure_reason=<sagemaker_core.main.utils.Unassigned object>)

Represents the overall status of a model package.

Parameters:

• name (str)

• status (str)

• failure_reason (str | None)

name

Type:

The name of the model package for which the overall status is being reported.

status

Type:

The current status.

failure_reason

Type:

if the overall status is Failed, the reason for the failure.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageSummary(*, model_package_arn, creation_time, model_package_status, model_package_name=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, model_package_version=<sagemaker_core.main.utils.Unassigned object>, model_package_description=<sagemaker_core.main.utils.Unassigned object>, model_approval_status=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information about a model package.

Parameters:

• model_package_arn (str)

• creation_time (datetime)

• model_package_status (str)

• model_package_name (str | object | None)

• model_package_group_name (str | object | None)

• model_package_version (int | None)

• model_package_description (str | None)

• model_approval_status (str | None)

model_package_name

Type:

The name of the model package.

model_package_group_name

Type:

If the model package is a versioned model, the model group that the versioned model belongs to.

model_package_version

Type:

If the model package is a versioned model, the version of the model.

model_package_arn

Type:

The Amazon Resource Name (ARN) of the model package.

model_package_description

Type:

A brief description of the model package.

creation_time

Type:

A timestamp that shows when the model package was created.

model_package_status

Type:

The overall status of the model package.

model_approval_status

Type:

The approval status of the model. This can be one of the following values. APPROVED - The model is approved REJECTED - The model is rejected. PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageValidationProfile(*, profile_name, transform_job_definition)

Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

Parameters:

• profile_name (str)

• transform_job_definition (TransformJobDefinition)

profile_name

Type:

The name of the profile for the model package.

transform_job_definition

Type:

The TransformJobDefinition object that describes the transform job used for the validation of the model package.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelPackageValidationSpecification(*, validation_role, validation_profiles)

Specifies batch transform jobs that SageMaker runs to validate your model package.

Parameters:

• validation_role (str)

• validation_profiles (List[ModelPackageValidationProfile])

validation_role

Type:

The IAM roles to be used for the validation of the model package.

validation_profiles

Type:

An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelQuality(*, statistics=<sagemaker_core.main.utils.Unassigned object>, constraints=<sagemaker_core.main.utils.Unassigned object>)

Model quality statistics and constraints.

Parameters:

• statistics (MetricsSource | None)

• constraints (MetricsSource | None)

statistics

Type:

Model quality statistics.

constraints

Type:

Model quality constraints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelQualityAppSpecification(*, image_uri, container_entrypoint=<sagemaker_core.main.utils.Unassigned object>, container_arguments=<sagemaker_core.main.utils.Unassigned object>, record_preprocessor_source_uri=<sagemaker_core.main.utils.Unassigned object>, post_analytics_processor_source_uri=<sagemaker_core.main.utils.Unassigned object>, problem_type=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>)

Container image configuration object for the monitoring job.

Parameters:

• image_uri (str)

• container_entrypoint (List[str] | None)

• container_arguments (List[str] | None)

• record_preprocessor_source_uri (str | None)

• post_analytics_processor_source_uri (str | None)

• problem_type (str | None)

• environment (Dict[str, str] | None)

image_uri

Type:

The address of the container image that the monitoring job runs.

container_entrypoint

Type:

Specifies the entrypoint for a container that the monitoring job runs.

container_arguments

Type:

An array of arguments for the container used to run the monitoring job.

record_preprocessor_source_uri

Type:

An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

post_analytics_processor_source_uri

Type:

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

problem_type

Type:

The machine learning problem type of the model that the monitoring job monitors.

environment

Type:

Sets the environment variables in the container that the monitoring job runs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelQualityBaselineConfig(*, baselining_job_name=<sagemaker_core.main.utils.Unassigned object>, constraints_resource=<sagemaker_core.main.utils.Unassigned object>)

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Parameters:

• baselining_job_name (str | None)

• constraints_resource (MonitoringConstraintsResource | None)

baselining_job_name

Type:

The name of the job that performs baselining for the monitoring job.

constraints_resource

Type:

sagemaker_core.main.shapes.MonitoringConstraintsResource | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelQualityJobInput(*, ground_truth_s3_input, endpoint_input=<sagemaker_core.main.utils.Unassigned object>, batch_transform_input=<sagemaker_core.main.utils.Unassigned object>)

The input for the model quality monitoring job. Currently endpoints are supported for input for model quality monitoring jobs.

Parameters:

• ground_truth_s3_input (MonitoringGroundTruthS3Input)

• endpoint_input (EndpointInput | None)

• batch_transform_input (BatchTransformInput | None)

endpoint_input

Type:

sagemaker_core.main.shapes.EndpointInput | None

batch_transform_input

Type:

Input object for the batch transform job.

ground_truth_s3_input

Type:

The ground truth label provided for the model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelQuantizationConfig(*, image=<sagemaker_core.main.utils.Unassigned object>, override_environment=<sagemaker_core.main.utils.Unassigned object>)

Settings for the model quantization technique that’s applied by a model optimization job.

Parameters:

• image (str | None)

• override_environment (Dict[str, str] | None)

image

Type:

The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.

override_environment

Type:

Environment variables that override the default ones in the model container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelRegisterSettings(*, status=<sagemaker_core.main.utils.Unassigned object>, cross_account_model_register_role_arn=<sagemaker_core.main.utils.Unassigned object>)

The model registry settings for the SageMaker Canvas application.

Parameters:

• status (str | None)

• cross_account_model_register_role_arn (str | None)

status

Type:

Describes whether the integration to the model registry is enabled or disabled in the Canvas application.

cross_account_model_register_role_arn

Type:

The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelShardingConfig(*, image=<sagemaker_core.main.utils.Unassigned object>, override_environment=<sagemaker_core.main.utils.Unassigned object>)

Settings for the model sharding technique that’s applied by a model optimization job.

Parameters:

• image (str | None)

• override_environment (Dict[str, str] | None)

image

Type:

The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.

override_environment

Type:

Environment variables that override the default ones in the model container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelStepMetadata(*, arn=<sagemaker_core.main.utils.Unassigned object>)

Metadata for Model steps.

Parameters:

arn (str | None)

arn

Type:

The Amazon Resource Name (ARN) of the created model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelStreamError(*, message=<sagemaker_core.main.utils.Unassigned object>, error_code=<sagemaker_core.main.utils.Unassigned object>)

An error occurred while streaming the response body. This error can have the following error codes: ModelInvocationTimeExceeded The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.

Parameters:

• message (str | None)

• error_code (str | None)

message

Type:

str | None

error_code

Type:

This error can have the following error codes: ModelInvocationTimeExceeded The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelSummary(*, model_name, model_arn, creation_time)

Provides summary information about a model.

Parameters:

• model_name (str | object)

• model_arn (str)

• creation_time (datetime)

model_name

Type:

The name of the model that you want a summary for.

model_arn

Type:

The Amazon Resource Name (ARN) of the model.

creation_time

Type:

A timestamp that indicates when the model was created.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelVariantConfig(*, model_name, variant_name, infrastructure_config)

Contains information about the deployment options of a model.

Parameters:

• model_name (str | object)

• variant_name (str)

• infrastructure_config (ModelInfrastructureConfig)

model_name

Type:

The name of the Amazon SageMaker Model entity.

variant_name

Type:

The name of the variant.

infrastructure_config

Type:

The configuration for the infrastructure that the model will be deployed to.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ModelVariantConfigSummary(*, model_name, variant_name, infrastructure_config, status)

Summary of the deployment configuration of a model.

Parameters:

• model_name (str | object)

• variant_name (str)

• infrastructure_config (ModelInfrastructureConfig)

• status (str)

model_name

Type:

The name of the Amazon SageMaker Model entity.

variant_name

Type:

The name of the variant.

infrastructure_config

Type:

The configuration of the infrastructure that the model has been deployed to.

status

Type:

The status of deployment for the model variant on the hosted inference endpoint. Creating - Amazon SageMaker is preparing the model variant on the hosted inference endpoint. InService - The model variant is running on the hosted inference endpoint. Updating - Amazon SageMaker is updating the model variant on the hosted inference endpoint. Deleting - Amazon SageMaker is deleting the model variant on the hosted inference endpoint. Deleted - The model variant has been deleted on the hosted inference endpoint. This can only happen after stopping the experiment.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringAlertActions(*, model_dashboard_indicator=<sagemaker_core.main.utils.Unassigned object>)

A list of alert actions taken in response to an alert going into InAlert status.

Parameters:

model_dashboard_indicator (ModelDashboardIndicatorAction | None)

model_dashboard_indicator

Type:

An alert action taken to light up an icon on the Model Dashboard when an alert goes into InAlert status.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringAlertHistorySummary(*, monitoring_schedule_name, monitoring_alert_name, creation_time, alert_status)

Provides summary information of an alert’s history.

Parameters:

• monitoring_schedule_name (str | object)

• monitoring_alert_name (str | object)

• creation_time (datetime)

• alert_status (str)

monitoring_schedule_name

Type:

The name of a monitoring schedule.

monitoring_alert_name

Type:

The name of a monitoring alert.

creation_time

Type:

A timestamp that indicates when the first alert transition occurred in an alert history. An alert transition can be from status InAlert to OK, or from OK to InAlert.

alert_status

Type:

The current alert status of an alert.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringAlertSummary(*, monitoring_alert_name, creation_time, last_modified_time, alert_status, datapoints_to_alert, evaluation_period, actions)

Provides summary information about a monitor alert.

Parameters:

• monitoring_alert_name (str | object)

• creation_time (datetime)

• last_modified_time (datetime)

• alert_status (str)

• datapoints_to_alert (int)

• evaluation_period (int)

• actions (MonitoringAlertActions)

monitoring_alert_name

Type:

The name of a monitoring alert.

creation_time

Type:

A timestamp that indicates when a monitor alert was created.

last_modified_time

Type:

A timestamp that indicates when a monitor alert was last updated.

alert_status

Type:

The current status of an alert.

datapoints_to_alert

Type:

Within EvaluationPeriod, how many execution failures will raise an alert.

evaluation_period

Type:

The number of most recent monitoring executions to consider when evaluating alert status.

actions

Type:

A list of alert actions taken in response to an alert going into InAlert status.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringAppSpecification(*, image_uri, container_entrypoint=<sagemaker_core.main.utils.Unassigned object>, container_arguments=<sagemaker_core.main.utils.Unassigned object>, record_preprocessor_source_uri=<sagemaker_core.main.utils.Unassigned object>, post_analytics_processor_source_uri=<sagemaker_core.main.utils.Unassigned object>)

Container image configuration object for the monitoring job.

Parameters:

• image_uri (str)

• container_entrypoint (List[str] | None)

• container_arguments (List[str] | None)

• record_preprocessor_source_uri (str | None)

• post_analytics_processor_source_uri (str | None)

image_uri

Type:

The container image to be run by the monitoring job.

container_entrypoint

Type:

Specifies the entrypoint for a container used to run the monitoring job.

container_arguments

Type:

An array of arguments for the container used to run the monitoring job.

record_preprocessor_source_uri

Type:

An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

post_analytics_processor_source_uri

Type:

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringBaselineConfig(*, baselining_job_name=<sagemaker_core.main.utils.Unassigned object>, constraints_resource=<sagemaker_core.main.utils.Unassigned object>, statistics_resource=<sagemaker_core.main.utils.Unassigned object>)

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Parameters:

• baselining_job_name (str | None)

• constraints_resource (MonitoringConstraintsResource | None)

• statistics_resource (MonitoringStatisticsResource | None)

baselining_job_name

Type:

The name of the job that performs baselining for the monitoring job.

constraints_resource

Type:

The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

statistics_resource

Type:

The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringClusterConfig(*, instance_count, instance_type, volume_size_in_gb, volume_kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Configuration for the cluster used to run model monitoring jobs.

Parameters:

• instance_count (int)

• instance_type (str)

• volume_size_in_gb (int)

• volume_kms_key_id (str | None)

instance_count

Type:

The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.

instance_type

Type:

The ML compute instance type for the processing job.

volume_size_in_gb

Type:

The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

volume_kms_key_id

Type:

The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringConstraintsResource(*, s3_uri=<sagemaker_core.main.utils.Unassigned object>)

The constraints resource for a monitoring job.

Parameters:

s3_uri (str | None)

s3_uri

Type:

The Amazon S3 URI for the constraints resource.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringCsvDatasetFormat(*, header=<sagemaker_core.main.utils.Unassigned object>)

Represents the CSV dataset format used when running a monitoring job.

Parameters:

header (bool | None)

header

Type:

Indicates if the CSV data has a header.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringDatasetFormat(*, csv=<sagemaker_core.main.utils.Unassigned object>, json=<sagemaker_core.main.utils.Unassigned object>, parquet=<sagemaker_core.main.utils.Unassigned object>)

Represents the dataset format used when running a monitoring job.

Parameters:

• csv (MonitoringCsvDatasetFormat | None)

• json (MonitoringJsonDatasetFormat | None)

• parquet (MonitoringParquetDatasetFormat | None)

csv

Type:

The CSV dataset used in the monitoring job.

json

Type:

The JSON dataset used in the monitoring job

parquet

Type:

The Parquet dataset used in the monitoring job

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringExecutionSummary(*, monitoring_schedule_name, scheduled_time, creation_time, last_modified_time, monitoring_execution_status, processing_job_arn=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>)

Summary of information about the last monitoring job to run.

Parameters:

• monitoring_schedule_name (str | object)

• scheduled_time (datetime)

• creation_time (datetime)

• last_modified_time (datetime)

• monitoring_execution_status (str)

• processing_job_arn (str | None)

• endpoint_name (str | object | None)

• failure_reason (str | None)

• monitoring_job_definition_name (str | None)

• monitoring_type (str | None)

monitoring_schedule_name

Type:

The name of the monitoring schedule.

scheduled_time

Type:

The time the monitoring job was scheduled.

creation_time

Type:

The time at which the monitoring job was created.

last_modified_time

Type:

A timestamp that indicates the last time the monitoring job was modified.

monitoring_execution_status

Type:

The status of the monitoring job.

processing_job_arn

Type:

The Amazon Resource Name (ARN) of the monitoring job.

endpoint_name

Type:

The name of the endpoint used to run the monitoring job.

failure_reason

Type:

Contains the reason a monitoring job failed, if it failed.

monitoring_job_definition_name

Type:

The name of the monitoring job.

monitoring_type

Type:

The type of the monitoring job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringGroundTruthS3Input(*, s3_uri=<sagemaker_core.main.utils.Unassigned object>)

The ground truth labels for the dataset used for the monitoring job.

Parameters:

s3_uri (str | None)

s3_uri

Type:

The address of the Amazon S3 location of the ground truth labels.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringInput(*, endpoint_input=<sagemaker_core.main.utils.Unassigned object>, batch_transform_input=<sagemaker_core.main.utils.Unassigned object>)

The inputs for a monitoring job.

Parameters:

• endpoint_input (EndpointInput | None)

• batch_transform_input (BatchTransformInput | None)

endpoint_input

Type:

The endpoint for a monitoring job.

batch_transform_input

Type:

Input object for the batch transform job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringJobDefinition(*, monitoring_inputs, monitoring_output_config, monitoring_resources, monitoring_app_specification, role_arn, baseline_config=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>)

Defines the monitoring job.

Parameters:

• monitoring_inputs (List[MonitoringInput])

• monitoring_output_config (MonitoringOutputConfig)

• monitoring_resources (MonitoringResources)

• monitoring_app_specification (MonitoringAppSpecification)

• role_arn (str)

• baseline_config (MonitoringBaselineConfig | None)

• stopping_condition (MonitoringStoppingCondition | None)

• environment (Dict[str, str] | None)

• network_config (NetworkConfig | None)

baseline_config

Type:

Baseline configuration used to validate that the data conforms to the specified constraints and statistics

monitoring_inputs

Type:

The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker AI Endpoint.

monitoring_output_config

Type:

The array of outputs from the monitoring job to be uploaded to Amazon S3.

monitoring_resources

Type:

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.

monitoring_app_specification

Type:

Configures the monitoring job to run a specified Docker container image.

stopping_condition

Type:

Specifies a time limit for how long the monitoring job is allowed to run.

environment

Type:

Sets the environment variables in the Docker container.

network_config

Type:

Specifies networking options for an monitoring job.

role_arn

Type:

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringJobDefinitionSummary(*, monitoring_job_definition_name, monitoring_job_definition_arn, creation_time, endpoint_name)

Summary information about a monitoring job.

Parameters:

• monitoring_job_definition_name (str)

• monitoring_job_definition_arn (str)

• creation_time (datetime)

• endpoint_name (str | object)

monitoring_job_definition_name

Type:

The name of the monitoring job.

monitoring_job_definition_arn

Type:

The Amazon Resource Name (ARN) of the monitoring job.

creation_time

Type:

The time that the monitoring job was created.

endpoint_name

Type:

The name of the endpoint that the job monitors.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringJsonDatasetFormat(*, line=<sagemaker_core.main.utils.Unassigned object>)

Represents the JSON dataset format used when running a monitoring job.

Parameters:

line (bool | None)

line

Type:

Indicates if the file should be read as a JSON object per line.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringNetworkConfig(*, enable_inter_container_traffic_encryption=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>)

The networking configuration for the monitoring job.

Parameters:

• enable_inter_container_traffic_encryption (bool | None)

• enable_network_isolation (bool | None)

• vpc_config (VpcConfig | None)

enable_inter_container_traffic_encryption

Type:

Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

enable_network_isolation

Type:

Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

vpc_config

Type:

sagemaker_core.main.shapes.VpcConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringOutput(*, s3_output)

The output object for a monitoring job.

Parameters:

s3_output (MonitoringS3Output)

s3_output

Type:

The Amazon S3 storage location where the results of a monitoring job are saved.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringOutputConfig(*, monitoring_outputs, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

The output configuration for monitoring jobs.

Parameters:

• monitoring_outputs (List[MonitoringOutput])

• kms_key_id (str | None)

monitoring_outputs

Type:

Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

kms_key_id

Type:

The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringParquetDatasetFormat

Represents the Parquet dataset format used when running a monitoring job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringResources(*, cluster_config)

Identifies the resources to deploy for a monitoring job.

Parameters:

cluster_config (MonitoringClusterConfig)

cluster_config

Type:

The configuration for the cluster resources used to run the processing job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringS3Output(*, s3_uri, local_path, s3_upload_mode=<sagemaker_core.main.utils.Unassigned object>)

Information about where and how you want to store the results of a monitoring job.

Parameters:

• s3_uri (str)

• local_path (str)

• s3_upload_mode (str | None)

s3_uri

Type:

A URI that identifies the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job.

local_path

Type:

The local path to the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. LocalPath is an absolute path for the output data.

s3_upload_mode

Type:

Whether to upload the results of the monitoring job continuously or after the job completes.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringSchedule(*, monitoring_schedule_arn=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_status=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_config=<sagemaker_core.main.utils.Unassigned object>, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, last_monitoring_execution_summary=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor.

Parameters:

• monitoring_schedule_arn (str | None)

• monitoring_schedule_name (str | object | None)

• monitoring_schedule_status (str | None)

• monitoring_type (str | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• monitoring_schedule_config (MonitoringScheduleConfig | None)

• endpoint_name (str | object | None)

• last_monitoring_execution_summary (MonitoringExecutionSummary | None)

• tags (List[Tag] | None)

monitoring_schedule_arn

Type:

The Amazon Resource Name (ARN) of the monitoring schedule.

monitoring_schedule_name

Type:

The name of the monitoring schedule.

monitoring_schedule_status

Type:

The status of the monitoring schedule. This can be one of the following values. PENDING - The schedule is pending being created. FAILED - The schedule failed. SCHEDULED - The schedule was successfully created. STOPPED - The schedule was stopped.

monitoring_type

Type:

The type of the monitoring job definition to schedule.

failure_reason

Type:

If the monitoring schedule failed, the reason it failed.

creation_time

Type:

The time that the monitoring schedule was created.

last_modified_time

Type:

The last time the monitoring schedule was changed.

monitoring_schedule_config

Type:

sagemaker_core.main.shapes.MonitoringScheduleConfig | None

endpoint_name

Type:

The endpoint that hosts the model being monitored.

last_monitoring_execution_summary

Type:

sagemaker_core.main.shapes.MonitoringExecutionSummary | None

tags

Type:

A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringScheduleConfig(*, schedule_config=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>)

Configures the monitoring schedule and defines the monitoring job.

Parameters:

• schedule_config (ScheduleConfig | None)

• monitoring_job_definition (MonitoringJobDefinition | None)

• monitoring_job_definition_name (str | None)

• monitoring_type (str | None)

schedule_config

Type:

Configures the monitoring schedule.

monitoring_job_definition

Type:

Defines the monitoring job.

monitoring_job_definition_name

Type:

The name of the monitoring job definition to schedule.

monitoring_type

Type:

The type of the monitoring job definition to schedule.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringScheduleSummary(*, monitoring_schedule_name, monitoring_schedule_arn, creation_time, last_modified_time, monitoring_schedule_status, endpoint_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_job_definition_name=<sagemaker_core.main.utils.Unassigned object>, monitoring_type=<sagemaker_core.main.utils.Unassigned object>)

Summarizes the monitoring schedule.

Parameters:

• monitoring_schedule_name (str | object)

• monitoring_schedule_arn (str)

• creation_time (datetime)

• last_modified_time (datetime)

• monitoring_schedule_status (str)

• endpoint_name (str | object | None)

• monitoring_job_definition_name (str | None)

• monitoring_type (str | None)

monitoring_schedule_name

Type:

The name of the monitoring schedule.

monitoring_schedule_arn

Type:

The Amazon Resource Name (ARN) of the monitoring schedule.

creation_time

Type:

The creation time of the monitoring schedule.

last_modified_time

Type:

The last time the monitoring schedule was modified.

monitoring_schedule_status

Type:

The status of the monitoring schedule.

endpoint_name

Type:

The name of the endpoint using the monitoring schedule.

monitoring_job_definition_name

Type:

The name of the monitoring job definition that the schedule is for.

monitoring_type

Type:

The type of the monitoring job definition that the schedule is for.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringStatisticsResource(*, s3_uri=<sagemaker_core.main.utils.Unassigned object>)

The statistics resource for a monitoring job.

Parameters:

s3_uri (str | None)

s3_uri

Type:

The Amazon S3 URI for the statistics resource.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MonitoringStoppingCondition(*, max_runtime_in_seconds)

A time limit for how long the monitoring job is allowed to run before stopping.

Parameters:

max_runtime_in_seconds (int)

max_runtime_in_seconds

Type:

The maximum runtime allowed in seconds. The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.MultiModelConfig(*, model_cache_setting=<sagemaker_core.main.utils.Unassigned object>)

Specifies additional configuration for hosting multi-model endpoints.

Parameters:

model_cache_setting (str | None)

model_cache_setting

Type:

Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NeoVpcConfig(*, security_group_ids, subnets)

The VpcConfig configuration object that specifies the VPC that you want the compilation jobs to connect to. For more information on controlling access to your Amazon S3 buckets used for compilation job, see Give Amazon SageMaker AI Compilation Jobs Access to Resources in Your Amazon VPC.

Parameters:

• security_group_ids (List[str])

• subnets (List[str])

security_group_ids

Type:

The VPC security group IDs. IDs have the form of sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

subnets

Type:

The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NestedFilters(*, nested_property_name, filters)

A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API. For example, to filter on a training job’s InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters: ‘{Name:”InputDataConfig.ChannelName”, “Operator”:”Equals”, “Value”:”train”}’, ‘{Name:”InputDataConfig.DataSource.S3DataSource.S3Uri”, “Operator”:”Contains”, “Value”:”mybucket/catdata”}’

Parameters:

• nested_property_name (str)

• filters (List[Filter])

nested_property_name

Type:

The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.

filters

Type:

A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NetworkConfig(*, enable_inter_container_traffic_encryption=<sagemaker_core.main.utils.Unassigned object>, enable_network_isolation=<sagemaker_core.main.utils.Unassigned object>, vpc_config=<sagemaker_core.main.utils.Unassigned object>)

Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.

Parameters:

• enable_inter_container_traffic_encryption (bool | None)

• enable_network_isolation (bool | None)

• vpc_config (VpcConfig | None)

enable_inter_container_traffic_encryption

Type:

Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

enable_network_isolation

Type:

Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

vpc_config

Type:

sagemaker_core.main.shapes.VpcConfig | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NotebookInstanceLifecycleConfigSummary(*, notebook_instance_lifecycle_config_name, notebook_instance_lifecycle_config_arn, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Provides a summary of a notebook instance lifecycle configuration.

Parameters:

• notebook_instance_lifecycle_config_name (str | object)

• notebook_instance_lifecycle_config_arn (str)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

notebook_instance_lifecycle_config_name

Type:

The name of the lifecycle configuration.

notebook_instance_lifecycle_config_arn

Type:

The Amazon Resource Name (ARN) of the lifecycle configuration.

creation_time

Type:

A timestamp that tells when the lifecycle configuration was created.

last_modified_time

Type:

A timestamp that tells when the lifecycle configuration was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NotebookInstanceLifecycleHook(*, content=<sagemaker_core.main.utils.Unassigned object>)

Contains the notebook instance lifecycle configuration script. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Parameters:

content (str | None)

content

Type:

A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NotebookInstanceSummary(*, notebook_instance_name, notebook_instance_arn, notebook_instance_status=<sagemaker_core.main.utils.Unassigned object>, url=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, notebook_instance_lifecycle_config_name=<sagemaker_core.main.utils.Unassigned object>, default_code_repository=<sagemaker_core.main.utils.Unassigned object>, additional_code_repositories=<sagemaker_core.main.utils.Unassigned object>)

Provides summary information for an SageMaker AI notebook instance.

Parameters:

• notebook_instance_name (str | object)

• notebook_instance_arn (str)

• notebook_instance_status (str | None)

• url (str | None)

• instance_type (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• notebook_instance_lifecycle_config_name (str | object | None)

• default_code_repository (str | None)

• additional_code_repositories (List[str] | None)

notebook_instance_name

Type:

The name of the notebook instance that you want a summary for.

notebook_instance_arn

Type:

The Amazon Resource Name (ARN) of the notebook instance.

notebook_instance_status

Type:

The status of the notebook instance.

url

Type:

The URL that you use to connect to the Jupyter notebook running in your notebook instance.

instance_type

Type:

The type of ML compute instance that the notebook instance is running on.

creation_time

Type:

A timestamp that shows when the notebook instance was created.

last_modified_time

Type:

A timestamp that shows when the notebook instance was last modified.

notebook_instance_lifecycle_config_name

Type:

The name of a notebook instance lifecycle configuration associated with this notebook instance. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

default_code_repository

Type:

The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

additional_code_repositories

Type:

An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.NotificationConfiguration(*, notification_topic_arn=<sagemaker_core.main.utils.Unassigned object>)

Configures Amazon SNS notifications of available or expiring work items for work teams.

Parameters:

notification_topic_arn (str | None)

notification_topic_arn

Type:

The ARN for the Amazon SNS topic to which notifications should be published.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ObjectiveStatusCounters(*, succeeded=<sagemaker_core.main.utils.Unassigned object>, pending=<sagemaker_core.main.utils.Unassigned object>, failed=<sagemaker_core.main.utils.Unassigned object>)

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

Parameters:

• succeeded (int | None)

• pending (int | None)

• failed (int | None)

succeeded

Type:

The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

pending

Type:

The number of training jobs that are in progress and pending evaluation of their final objective metric.

failed

Type:

The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OfflineStoreConfig(*, s3_storage_config, disable_glue_table_creation=<sagemaker_core.main.utils.Unassigned object>, data_catalog_config=<sagemaker_core.main.utils.Unassigned object>, table_format=<sagemaker_core.main.utils.Unassigned object>)

The configuration of an OfflineStore. Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore. To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId, in S3StorageConfig.

Parameters:

• s3_storage_config (S3StorageConfig)

• disable_glue_table_creation (bool | None)

• data_catalog_config (DataCatalogConfig | None)

• table_format (str | None)

s3_storage_config

Type:

The Amazon Simple Storage (Amazon S3) location of OfflineStore.

disable_glue_table_creation

Type:

Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore. If set to False, Feature Store will name the OfflineStore Glue table following Athena’s naming recommendations. The default value is False.

data_catalog_config

Type:

The meta data of the Glue table that is autogenerated when an OfflineStore is created.

table_format

Type:

Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OfflineStoreStatus(*, status, blocked_reason=<sagemaker_core.main.utils.Unassigned object>)

The status of OfflineStore.

Parameters:

• status (str)

• blocked_reason (str | None)

status

Type:

An OfflineStore status.

blocked_reason

Type:

The justification for why the OfflineStoreStatus is Blocked (if applicable).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OidcConfig(*, client_id, client_secret, issuer, authorization_endpoint, token_endpoint, user_info_endpoint, logout_endpoint, jwks_uri, scope=<sagemaker_core.main.utils.Unassigned object>, authentication_request_extra_params=<sagemaker_core.main.utils.Unassigned object>)

Use this parameter to configure your OIDC Identity Provider (IdP).

Parameters:

• client_id (str)

• client_secret (str)

• issuer (str)

• authorization_endpoint (str)

• token_endpoint (str)

• user_info_endpoint (str)

• logout_endpoint (str)

• jwks_uri (str)

• scope (str | None)

• authentication_request_extra_params (Dict[str, str] | None)

client_id

Type:

The OIDC IdP client ID used to configure your private workforce.

client_secret

Type:

The OIDC IdP client secret used to configure your private workforce.

issuer

Type:

The OIDC IdP issuer used to configure your private workforce.

authorization_endpoint

Type:

The OIDC IdP authorization endpoint used to configure your private workforce.

token_endpoint

Type:

The OIDC IdP token endpoint used to configure your private workforce.

user_info_endpoint

Type:

The OIDC IdP user information endpoint used to configure your private workforce.

logout_endpoint

Type:

The OIDC IdP logout endpoint used to configure your private workforce.

jwks_uri

Type:

The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.

scope

Type:

An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.

authentication_request_extra_params

Type:

A string to string map of identifiers specific to the custom identity provider (IdP) being used.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OidcConfigForResponse(*, client_id=<sagemaker_core.main.utils.Unassigned object>, issuer=<sagemaker_core.main.utils.Unassigned object>, authorization_endpoint=<sagemaker_core.main.utils.Unassigned object>, token_endpoint=<sagemaker_core.main.utils.Unassigned object>, user_info_endpoint=<sagemaker_core.main.utils.Unassigned object>, logout_endpoint=<sagemaker_core.main.utils.Unassigned object>, jwks_uri=<sagemaker_core.main.utils.Unassigned object>, scope=<sagemaker_core.main.utils.Unassigned object>, authentication_request_extra_params=<sagemaker_core.main.utils.Unassigned object>)

Your OIDC IdP workforce configuration.

Parameters:

• client_id (str | None)

• issuer (str | None)

• authorization_endpoint (str | None)

• token_endpoint (str | None)

• user_info_endpoint (str | None)

• logout_endpoint (str | None)

• jwks_uri (str | None)

• scope (str | None)

• authentication_request_extra_params (Dict[str, str] | None)

client_id

Type:

The OIDC IdP client ID used to configure your private workforce.

issuer

Type:

The OIDC IdP issuer used to configure your private workforce.

authorization_endpoint

Type:

The OIDC IdP authorization endpoint used to configure your private workforce.

token_endpoint

Type:

The OIDC IdP token endpoint used to configure your private workforce.

user_info_endpoint

Type:

The OIDC IdP user information endpoint used to configure your private workforce.

logout_endpoint

Type:

The OIDC IdP logout endpoint used to configure your private workforce.

jwks_uri

Type:

The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.

scope

Type:

An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.

authentication_request_extra_params

Type:

A string to string map of identifiers specific to the custom identity provider (IdP) being used.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OidcMemberDefinition(*, groups=<sagemaker_core.main.utils.Unassigned object>)

A list of user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.

Parameters:

groups (List[str] | None)

groups

Type:

A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OnlineStoreConfig(*, security_config=<sagemaker_core.main.utils.Unassigned object>, enable_online_store=<sagemaker_core.main.utils.Unassigned object>, ttl_duration=<sagemaker_core.main.utils.Unassigned object>, storage_type=<sagemaker_core.main.utils.Unassigned object>)

Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId, for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly. The default value is False.

Parameters:

• security_config (OnlineStoreSecurityConfig | None)

• enable_online_store (bool | None)

• ttl_duration (TtlDuration | None)

• storage_type (str | None)

security_config

Type:

Use to specify KMS Key ID (KMSKeyId) for at-rest encryption of your OnlineStore.

enable_online_store

Type:

Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag. The default value is False.

ttl_duration

Type:

Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.

storage_type

Type:

Option for different tiers of low latency storage for real-time data retrieval. Standard: A managed low latency data store for feature groups. InMemory: A managed data store for feature groups that supports very low latency retrieval.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OnlineStoreConfigUpdate(*, ttl_duration=<sagemaker_core.main.utils.Unassigned object>)

Updates the feature group online store configuration.

Parameters:

ttl_duration (TtlDuration | None)

ttl_duration

Type:

Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OnlineStoreSecurityConfig(*, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

The security configuration for OnlineStore.

Parameters:

kms_key_id (str | None)

kms_key_id

Type:

The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption. The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId: “kms:Encrypt” “kms:Decrypt” “kms:DescribeKey” “kms:CreateGrant” “kms:RetireGrant” “kms:ReEncryptFrom” “kms:ReEncryptTo” “kms:GenerateDataKey” “kms:ListAliases” “kms:ListGrants” “kms:RevokeGrant” The caller (either user or IAM role) to all DataPlane operations (PutRecord, GetRecord, DeleteRecord) must have the following permissions to the KmsKeyId: “kms:Decrypt”

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationConfig(*, model_quantization_config=<sagemaker_core.main.utils.Unassigned object>, model_compilation_config=<sagemaker_core.main.utils.Unassigned object>, model_sharding_config=<sagemaker_core.main.utils.Unassigned object>)

Settings for an optimization technique that you apply with a model optimization job.

Parameters:

• model_quantization_config (ModelQuantizationConfig | None)

• model_compilation_config (ModelCompilationConfig | None)

• model_sharding_config (ModelShardingConfig | None)

model_quantization_config

Type:

Settings for the model quantization technique that’s applied by a model optimization job.

model_compilation_config

Type:

Settings for the model compilation technique that’s applied by a model optimization job.

model_sharding_config

Type:

Settings for the model sharding technique that’s applied by a model optimization job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationJobModelSource(*, s3=<sagemaker_core.main.utils.Unassigned object>)

The location of the source model to optimize with an optimization job.

Parameters:

s3 (OptimizationJobModelSourceS3 | None)

s3

Type:

The Amazon S3 location of a source model to optimize with an optimization job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationJobModelSourceS3(*, s3_uri=<sagemaker_core.main.utils.Unassigned object>, model_access_config=<sagemaker_core.main.utils.Unassigned object>)

The Amazon S3 location of a source model to optimize with an optimization job.

Parameters:

• s3_uri (str | None)

• model_access_config (OptimizationModelAccessConfig | None)

s3_uri

Type:

An Amazon S3 URI that locates a source model to optimize with an optimization job.

model_access_config

Type:

The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationJobOutputConfig(*, s3_output_location, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Details for where to store the optimized model that you create with the optimization job.

Parameters:

• s3_output_location (str)

• kms_key_id (str | None)

kms_key_id

Type:

The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.

s3_output_location

Type:

The Amazon S3 URI for where to store the optimized model that you create with an optimization job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationJobSummary(*, optimization_job_name, optimization_job_arn, creation_time, optimization_job_status, deployment_instance_type, optimization_types, optimization_start_time=<sagemaker_core.main.utils.Unassigned object>, optimization_end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Summarizes an optimization job by providing some of its key properties.

Parameters:

• optimization_job_name (str | object)

• optimization_job_arn (str)

• creation_time (datetime)

• optimization_job_status (str)

• deployment_instance_type (str)

• optimization_types (List[str])

• optimization_start_time (datetime | None)

• optimization_end_time (datetime | None)

• last_modified_time (datetime | None)

optimization_job_name

Type:

The name that you assigned to the optimization job.

optimization_job_arn

Type:

The Amazon Resource Name (ARN) of the optimization job.

creation_time

Type:

The time when you created the optimization job.

optimization_job_status

Type:

The current status of the optimization job.

optimization_start_time

Type:

The time when the optimization job started.

optimization_end_time

Type:

The time when the optimization job finished processing.

last_modified_time

Type:

The time when the optimization job was last updated.

deployment_instance_type

Type:

The type of instance that hosts the optimized model that you create with the optimization job.

optimization_types

Type:

The optimization techniques that are applied by the optimization job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationModelAccessConfig(*, accept_eula)

The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).

Parameters:

accept_eula (bool)

accept_eula

Type:

Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationOutput(*, recommended_inference_image=<sagemaker_core.main.utils.Unassigned object>)

Output values produced by an optimization job.

Parameters:

recommended_inference_image (str | None)

recommended_inference_image

Type:

The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OptimizationVpcConfig(*, security_group_ids, subnets)

A VPC in Amazon VPC that’s accessible to an optimized that you create with an optimization job. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

Parameters:

• security_group_ids (List[str])

• subnets (List[str])

security_group_ids

Type:

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

subnets

Type:

The ID of the subnets in the VPC to which you want to connect your optimized model.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OutputConfig(*, s3_output_location, target_device=<sagemaker_core.main.utils.Unassigned object>, target_platform=<sagemaker_core.main.utils.Unassigned object>, compiler_options=<sagemaker_core.main.utils.Unassigned object>, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.

Parameters:

• s3_output_location (str)

• target_device (str | None)

• target_platform (TargetPlatform | None)

• compiler_options (str | None)

• kms_key_id (str | None)

s3_output_location

Type:

Identifies the S3 bucket where you want Amazon SageMaker AI to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

target_device

Type:

Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform. Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

target_platform

Type:

Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice. The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms: Raspberry Pi 3 Model B+ “TargetPlatform”: {“Os”: “LINUX”, “Arch”: “ARM_EABIHF”}, “CompilerOptions”: {‘mattr’: [‘+neon’]} Jetson TX2 “TargetPlatform”: {“Os”: “LINUX”, “Arch”: “ARM64”, “Accelerator”: “NVIDIA”}, “CompilerOptions”: {‘gpu-code’: ‘sm_62’, ‘trt-ver’: ‘6.0.1’, ‘cuda-ver’: ‘10.0’} EC2 m5.2xlarge instance OS “TargetPlatform”: {“Os”: “LINUX”, “Arch”: “X86_64”, “Accelerator”: “NVIDIA”}, “CompilerOptions”: {‘mcpu’: ‘skylake-avx512’} RK3399 “TargetPlatform”: {“Os”: “LINUX”, “Arch”: “ARM64”, “Accelerator”: “MALI”} ARMv7 phone (CPU) “TargetPlatform”: {“Os”: “ANDROID”, “Arch”: “ARM_EABI”}, “CompilerOptions”: {‘ANDROID_PLATFORM’: 25, ‘mattr’: [‘+neon’]} ARMv8 phone (CPU) “TargetPlatform”: {“Os”: “ANDROID”, “Arch”: “ARM64”}, “CompilerOptions”: {‘ANDROID_PLATFORM’: 29}

compiler_options

Type:

Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions. DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model’s input. “float32” is used if “DTYPE” is not specified. Options for data type are: float32: Use either “float” or “float32”. int64: Use either “int64” or “long”. For example, {“dtype” : “float32”}. CPU: Compilation for CPU supports the following compiler options. mcpu: CPU micro-architecture. For example, {‘mcpu’: ‘skylake-avx512’} mattr: CPU flags. For example, {‘mattr’: [‘+neon’, ‘+vfpv4’]} ARM: Details of ARM CPU compilations. NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {‘mattr’: [‘+neon’]} to the compiler options if compiling for ARM 32-bit platform with the NEON support. NVIDIA: Compilation for NVIDIA GPU supports the following compiler options. gpu_code: Specifies the targeted architecture. trt-ver: Specifies the TensorRT versions in x.y.z. format. cuda-ver: Specifies the CUDA version in x.y format. For example, {‘gpu-code’: ‘sm_72’, ‘trt-ver’: ‘6.0.1’, ‘cuda-ver’: ‘10.1’} ANDROID: Compilation for the Android OS supports the following compiler options: ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {‘ANDROID_PLATFORM’: 28}. mattr: Add {‘mattr’: [‘+neon’]} to compiler options if compiling for ARM 32-bit platform with NEON support. INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, “CompilerOptions”: “”–verbose 1 –num-neuroncores 2 -O2””. For information about supported compiler options, see Neuron Compiler CLI Reference Guide. CoreML: Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options: class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {“class_labels”: “imagenet_labels_1000.txt”}. Labels inside the txt file should be separated by newlines.

kms_key_id

Type:

The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker AI uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don’t provide a KMS key ID, Amazon SageMaker AI uses the default KMS key for Amazon S3 for your role’s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OutputDataConfig(*, s3_output_path, kms_key_id=<sagemaker_core.main.utils.Unassigned object>, compression_type=<sagemaker_core.main.utils.Unassigned object>)

Provides information about how to store model training results (model artifacts).

Parameters:

• s3_output_path (str)

• kms_key_id (str | None)

• compression_type (str | None)

kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats: // KMS Key ID “1234abcd-12ab-34cd-56ef-1234567890ab” // Amazon Resource Name (ARN) of a KMS Key “arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab” // KMS Key Alias “alias/ExampleAlias” // Amazon Resource Name (ARN) of a KMS Key Alias “arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias” If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don’t provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role’s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

s3_output_path

Type:

Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

compression_type

Type:

The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OutputParameter(*, name, value)

An output parameter of a pipeline step.

Parameters:

• name (str)

• value (str)

name

Type:

The name of the output parameter.

value

Type:

The value of the output parameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OwnershipSettings(*, owner_user_profile_name)

The collection of ownership settings for a space.

Parameters:

owner_user_profile_name (str)

owner_user_profile_name

Type:

The user profile who is the owner of the space.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.OwnershipSettingsSummary(*, owner_user_profile_name=<sagemaker_core.main.utils.Unassigned object>)

Specifies summary information about the ownership settings.

Parameters:

owner_user_profile_name (str | None)

owner_user_profile_name

Type:

The user profile who is the owner of the space.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ParallelismConfiguration(*, max_parallel_execution_steps)

Configuration that controls the parallelism of the pipeline. By default, the parallelism configuration specified applies to all executions of the pipeline unless overridden.

Parameters:

max_parallel_execution_steps (int)

max_parallel_execution_steps

Type:

The max number of steps that can be executed in parallel.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Parameter(*, name, value)

Assigns a value to a named Pipeline parameter.

Parameters:

• name (str)

• value (str)

name

Type:

The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.

value

Type:

The literal value for the parameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ParameterRange(*, integer_parameter_range_specification=<sagemaker_core.main.utils.Unassigned object>, continuous_parameter_range_specification=<sagemaker_core.main.utils.Unassigned object>, categorical_parameter_range_specification=<sagemaker_core.main.utils.Unassigned object>)

Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.

Parameters:

• integer_parameter_range_specification (IntegerParameterRangeSpecification | None)

• continuous_parameter_range_specification (ContinuousParameterRangeSpecification | None)

• categorical_parameter_range_specification (CategoricalParameterRangeSpecification | None)

integer_parameter_range_specification

Type:

A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

continuous_parameter_range_specification

Type:

A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

categorical_parameter_range_specification

Type:

A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ParameterRanges(*, integer_parameter_ranges=<sagemaker_core.main.utils.Unassigned object>, continuous_parameter_ranges=<sagemaker_core.main.utils.Unassigned object>, categorical_parameter_ranges=<sagemaker_core.main.utils.Unassigned object>, auto_parameters=<sagemaker_core.main.utils.Unassigned object>)

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job. The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can’t exceed the maximum number specified.

Parameters:

• integer_parameter_ranges (List[IntegerParameterRange] | None)

• continuous_parameter_ranges (List[ContinuousParameterRange] | None)

• categorical_parameter_ranges (List[CategoricalParameterRange] | None)

• auto_parameters (List[AutoParameter] | None)

integer_parameter_ranges

Type:

The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

continuous_parameter_ranges

Type:

The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

categorical_parameter_ranges

Type:

The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

auto_parameters

Type:

A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Parent(*, trial_name=<sagemaker_core.main.utils.Unassigned object>, experiment_name=<sagemaker_core.main.utils.Unassigned object>)

The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.

Parameters:

• trial_name (str | object | None)

• experiment_name (str | object | None)

trial_name

Type:

The name of the trial.

experiment_name

Type:

The name of the experiment.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ParentHyperParameterTuningJob(*, hyper_parameter_tuning_job_name=<sagemaker_core.main.utils.Unassigned object>)

A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

Parameters:

hyper_parameter_tuning_job_name (str | object | None)

hyper_parameter_tuning_job_name

Type:

The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PartnerAppConfig(*, admin_users=<sagemaker_core.main.utils.Unassigned object>, arguments=<sagemaker_core.main.utils.Unassigned object>)

Configuration settings for the SageMaker Partner AI App.

Parameters:

• admin_users (List[str] | None)

• arguments (Dict[str, str] | None)

admin_users

Type:

The list of users that are given admin access to the SageMaker Partner AI App.

arguments

Type:

This is a map of required inputs for a SageMaker Partner AI App. Based on the application type, the map is populated with a key and value pair that is specific to the user and application.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PartnerAppMaintenanceConfig(*, maintenance_window_start=<sagemaker_core.main.utils.Unassigned object>)

Maintenance configuration settings for the SageMaker Partner AI App.

Parameters:

maintenance_window_start (str | None)

maintenance_window_start

Type:

The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. This value must take the following format: 3-letter-day:24-h-hour:minute. For example: TUE:03:30.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PartnerAppSummary(*, arn=<sagemaker_core.main.utils.Unassigned object>, name=<sagemaker_core.main.utils.Unassigned object>, type=<sagemaker_core.main.utils.Unassigned object>, status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>)

A subset of information related to a SageMaker Partner AI App. This information is used as part of the ListPartnerApps API response.

Parameters:

• arn (str | None)

• name (str | None)

• type (str | None)

• status (str | None)

• creation_time (datetime | None)

arn

Type:

The ARN of the SageMaker Partner AI App.

name

Type:

The name of the SageMaker Partner AI App.

type

Type:

The type of SageMaker Partner AI App to create. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.

status

Type:

The status of the SageMaker Partner AI App.

creation_time

Type:

The creation time of the SageMaker Partner AI App.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PayloadPart(*, bytes=<sagemaker_core.main.utils.Unassigned object>)

A wrapper for pieces of the payload that’s returned in response to a streaming inference request. A streaming inference response consists of one or more payload parts.

Parameters:

bytes (Any | None)

bytes

Type:

A blob that contains part of the response for your streaming inference request.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PendingDeploymentSummary(*, endpoint_config_name, production_variants=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, shadow_production_variants=<sagemaker_core.main.utils.Unassigned object>)

The summary of an in-progress deployment when an endpoint is creating or updating with a new endpoint configuration.

Parameters:

• endpoint_config_name (str | object)

• production_variants (List[PendingProductionVariantSummary] | None)

• start_time (datetime | None)

• shadow_production_variants (List[PendingProductionVariantSummary] | None)

endpoint_config_name

Type:

The name of the endpoint configuration used in the deployment.

production_variants

Type:

An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.

start_time

Type:

The start time of the deployment.

shadow_production_variants

Type:

An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants for the in-progress deployment.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PendingProductionVariantSummary(*, variant_name, deployed_images=<sagemaker_core.main.utils.Unassigned object>, current_weight=<sagemaker_core.main.utils.Unassigned object>, desired_weight=<sagemaker_core.main.utils.Unassigned object>, current_instance_count=<sagemaker_core.main.utils.Unassigned object>, desired_instance_count=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, accelerator_type=<sagemaker_core.main.utils.Unassigned object>, variant_status=<sagemaker_core.main.utils.Unassigned object>, current_serverless_config=<sagemaker_core.main.utils.Unassigned object>, desired_serverless_config=<sagemaker_core.main.utils.Unassigned object>, managed_instance_scaling=<sagemaker_core.main.utils.Unassigned object>, routing_config=<sagemaker_core.main.utils.Unassigned object>)

The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.

Parameters:

• variant_name (str)

• deployed_images (List[DeployedImage] | None)

• current_weight (float | None)

• desired_weight (float | None)

• current_instance_count (int | None)

• desired_instance_count (int | None)

• instance_type (str | None)

• accelerator_type (str | None)

• variant_status (List[ProductionVariantStatus] | None)

• current_serverless_config (ProductionVariantServerlessConfig | None)

• desired_serverless_config (ProductionVariantServerlessConfig | None)

• managed_instance_scaling (ProductionVariantManagedInstanceScaling | None)

• routing_config (ProductionVariantRoutingConfig | None)

variant_name

Type:

The name of the variant.

deployed_images

Type:

An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.

current_weight

Type:

The weight associated with the variant.

desired_weight

Type:

The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

current_instance_count

Type:

The number of instances associated with the variant.

desired_instance_count

Type:

The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.

instance_type

Type:

The type of instances associated with the variant.

accelerator_type

Type:

This parameter is no longer supported. Elastic Inference (EI) is no longer available. This parameter was used to specify the size of the EI instance to use for the production variant.

variant_status

Type:

The endpoint variant status which describes the current deployment stage status or operational status.

current_serverless_config

Type:

The serverless configuration for the endpoint.

desired_serverless_config

Type:

The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.

managed_instance_scaling

Type:

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

routing_config

Type:

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Phase(*, initial_number_of_users=<sagemaker_core.main.utils.Unassigned object>, spawn_rate=<sagemaker_core.main.utils.Unassigned object>, duration_in_seconds=<sagemaker_core.main.utils.Unassigned object>)

Defines the traffic pattern.

Parameters:

• initial_number_of_users (int | None)

• spawn_rate (int | None)

• duration_in_seconds (int | None)

initial_number_of_users

Type:

Specifies how many concurrent users to start with. The value should be between 1 and 3.

spawn_rate

Type:

Specified how many new users to spawn in a minute.

duration_in_seconds

Type:

Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed JobDurationInSeconds.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Pipeline(*, pipeline_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_description=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_status=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_run_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

A SageMaker Model Building Pipeline instance.

Parameters:

• pipeline_arn (str | None)

• pipeline_name (str | object | None)

• pipeline_display_name (str | None)

• pipeline_description (str | None)

• role_arn (str | None)

• pipeline_status (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• last_run_time (datetime | None)

• created_by (UserContext | None)

• last_modified_by (UserContext | None)

• parallelism_configuration (ParallelismConfiguration | None)

• tags (List[Tag] | None)

pipeline_arn

Type:

The Amazon Resource Name (ARN) of the pipeline.

pipeline_name

Type:

The name of the pipeline.

pipeline_display_name

Type:

The display name of the pipeline.

pipeline_description

Type:

The description of the pipeline.

role_arn

Type:

The Amazon Resource Name (ARN) of the role that created the pipeline.

pipeline_status

Type:

The status of the pipeline.

creation_time

Type:

The creation time of the pipeline.

last_modified_time

Type:

The time that the pipeline was last modified.

last_run_time

Type:

The time when the pipeline was last run.

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

parallelism_configuration

Type:

The parallelism configuration applied to the pipeline.

tags

Type:

A list of tags that apply to the pipeline.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineDefinitionS3Location(*, bucket, object_key, version_id=<sagemaker_core.main.utils.Unassigned object>)

The location of the pipeline definition stored in Amazon S3.

Parameters:

• bucket (str)

• object_key (str)

• version_id (str | None)

bucket

Type:

Name of the S3 bucket.

object_key

Type:

The object key (or key name) uniquely identifies the object in an S3 bucket.

version_id

Type:

Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineExecution(*, pipeline_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_status=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_description=<sagemaker_core.main.utils.Unassigned object>, pipeline_experiment_config=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>, parallelism_configuration=<sagemaker_core.main.utils.Unassigned object>, selective_execution_config=<sagemaker_core.main.utils.Unassigned object>, pipeline_parameters=<sagemaker_core.main.utils.Unassigned object>)

An execution of a pipeline.

Parameters:

• pipeline_arn (str | None)

• pipeline_execution_arn (str | None)

• pipeline_execution_display_name (str | None)

• pipeline_execution_status (str | None)

• pipeline_execution_description (str | None)

• pipeline_experiment_config (PipelineExperimentConfig | None)

• failure_reason (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• created_by (UserContext | None)

• last_modified_by (UserContext | None)

• parallelism_configuration (ParallelismConfiguration | None)

• selective_execution_config (SelectiveExecutionConfig | None)

• pipeline_parameters (List[Parameter] | None)

pipeline_arn

Type:

The Amazon Resource Name (ARN) of the pipeline that was executed.

pipeline_execution_arn

Type:

The Amazon Resource Name (ARN) of the pipeline execution.

pipeline_execution_display_name

Type:

The display name of the pipeline execution.

pipeline_execution_status

Type:

The status of the pipeline status.

pipeline_execution_description

Type:

The description of the pipeline execution.

pipeline_experiment_config

Type:

sagemaker_core.main.shapes.PipelineExperimentConfig | None

failure_reason

Type:

If the execution failed, a message describing why.

creation_time

Type:

The creation time of the pipeline execution.

last_modified_time

Type:

The time that the pipeline execution was last modified.

created_by

Type:

sagemaker_core.main.shapes.UserContext | None

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

parallelism_configuration

Type:

The parallelism configuration applied to the pipeline execution.

selective_execution_config

Type:

The selective execution configuration applied to the pipeline run.

pipeline_parameters

Type:

Contains a list of pipeline parameters. This list can be empty.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineExecutionStep(*, step_name=<sagemaker_core.main.utils.Unassigned object>, step_display_name=<sagemaker_core.main.utils.Unassigned object>, step_description=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, end_time=<sagemaker_core.main.utils.Unassigned object>, step_status=<sagemaker_core.main.utils.Unassigned object>, cache_hit_result=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, metadata=<sagemaker_core.main.utils.Unassigned object>, attempt_count=<sagemaker_core.main.utils.Unassigned object>, selective_execution_result=<sagemaker_core.main.utils.Unassigned object>)

An execution of a step in a pipeline.

Parameters:

• step_name (str | None)

• step_display_name (str | None)

• step_description (str | None)

• start_time (datetime | None)

• end_time (datetime | None)

• step_status (str | None)

• cache_hit_result (CacheHitResult | None)

• failure_reason (str | None)

• metadata (PipelineExecutionStepMetadata | None)

• attempt_count (int | None)

• selective_execution_result (SelectiveExecutionResult | None)

step_name

Type:

The name of the step that is executed.

step_display_name

Type:

The display name of the step.

step_description

Type:

The description of the step.

start_time

Type:

The time that the step started executing.

end_time

Type:

The time that the step stopped executing.

step_status

Type:

The status of the step execution.

cache_hit_result

Type:

If this pipeline execution step was cached, details on the cache hit.

failure_reason

Type:

The reason why the step failed execution. This is only returned if the step failed its execution.

metadata

Type:

Metadata to run the pipeline step.

attempt_count

Type:

The current attempt of the execution step. For more information, see Retry Policy for SageMaker Pipelines steps.

selective_execution_result

Type:

The ARN from an execution of the current pipeline from which results are reused for this step.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineExecutionStepMetadata(*, training_job=<sagemaker_core.main.utils.Unassigned object>, processing_job=<sagemaker_core.main.utils.Unassigned object>, transform_job=<sagemaker_core.main.utils.Unassigned object>, tuning_job=<sagemaker_core.main.utils.Unassigned object>, model=<sagemaker_core.main.utils.Unassigned object>, register_model=<sagemaker_core.main.utils.Unassigned object>, condition=<sagemaker_core.main.utils.Unassigned object>, callback=<sagemaker_core.main.utils.Unassigned object>, emr=<sagemaker_core.main.utils.Unassigned object>, quality_check=<sagemaker_core.main.utils.Unassigned object>, clarify_check=<sagemaker_core.main.utils.Unassigned object>, fail=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job=<sagemaker_core.main.utils.Unassigned object>, endpoint=<sagemaker_core.main.utils.Unassigned object>, endpoint_config=<sagemaker_core.main.utils.Unassigned object>)

Metadata for a step execution.

Parameters:

• training_job (TrainingJobStepMetadata | None)

• processing_job (ProcessingJobStepMetadata | None)

• transform_job (TransformJobStepMetadata | None)

• tuning_job (TuningJobStepMetaData | None)

• model (ModelStepMetadata | None)

• register_model (RegisterModelStepMetadata | None)

• condition (ConditionStepMetadata | None)

• callback (CallbackStepMetadata | None)

• emr (EMRStepMetadata | None)

• quality_check (QualityCheckStepMetadata | None)

• clarify_check (ClarifyCheckStepMetadata | None)

• fail (FailStepMetadata | None)

• auto_ml_job (AutoMLJobStepMetadata | None)

• endpoint (EndpointStepMetadata | None)

• endpoint_config (EndpointConfigStepMetadata | None)

training_job

Type:

The Amazon Resource Name (ARN) of the training job that was run by this step execution.

processing_job

Type:

The Amazon Resource Name (ARN) of the processing job that was run by this step execution.

transform_job

Type:

The Amazon Resource Name (ARN) of the transform job that was run by this step execution.

tuning_job

Type:

The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.

model

Type:

The Amazon Resource Name (ARN) of the model that was created by this step execution.

register_model

Type:

The Amazon Resource Name (ARN) of the model package that the model was registered to by this step execution.

condition

Type:

The outcome of the condition evaluation that was run by this step execution.

callback

Type:

The URL of the Amazon SQS queue used by this step execution, the pipeline generated token, and a list of output parameters.

lambda

Type:

The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of output parameters.

emr

Type:

The configurations and outcomes of an Amazon EMR step execution.

quality_check

Type:

The configurations and outcomes of the check step execution. This includes: The type of the check conducted. The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check. The Amazon S3 URIs of newly calculated baseline constraints and statistics. The model package group name provided. The Amazon S3 URI of the violation report if violations detected. The Amazon Resource Name (ARN) of check processing job initiated by the step execution. The Boolean flags indicating if the drift check is skipped. If step property BaselineUsedForDriftCheck is set the same as CalculatedBaseline.

clarify_check

Type:

Container for the metadata for a Clarify check step. The configurations and outcomes of the check step execution. This includes: The type of the check conducted, The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check. The Amazon S3 URIs of newly calculated baseline constraints and statistics. The model package group name provided. The Amazon S3 URI of the violation report if violations detected. The Amazon Resource Name (ARN) of check processing job initiated by the step execution. The boolean flags indicating if the drift check is skipped. If step property BaselineUsedForDriftCheck is set the same as CalculatedBaseline.

fail

Type:

The configurations and outcomes of a Fail step execution.

auto_ml_job

Type:

The Amazon Resource Name (ARN) of the AutoML job that was run by this step.

endpoint

Type:

The endpoint that was invoked during this step execution.

endpoint_config

Type:

The endpoint configuration used to create an endpoint during this step execution.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineExecutionSummary(*, pipeline_execution_arn=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_status=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_description=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_execution_failure_reason=<sagemaker_core.main.utils.Unassigned object>)

A pipeline execution summary.

Parameters:

• pipeline_execution_arn (str | None)

• start_time (datetime | None)

• pipeline_execution_status (str | None)

• pipeline_execution_description (str | None)

• pipeline_execution_display_name (str | None)

• pipeline_execution_failure_reason (str | None)

pipeline_execution_arn

Type:

The Amazon Resource Name (ARN) of the pipeline execution.

start_time

Type:

The start time of the pipeline execution.

pipeline_execution_status

Type:

The status of the pipeline execution.

pipeline_execution_description

Type:

The description of the pipeline execution.

pipeline_execution_display_name

Type:

The display name of the pipeline execution.

pipeline_execution_failure_reason

Type:

A message generated by SageMaker Pipelines describing why the pipeline execution failed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineExperimentConfig(*, experiment_name=<sagemaker_core.main.utils.Unassigned object>, trial_name=<sagemaker_core.main.utils.Unassigned object>)

Specifies the names of the experiment and trial created by a pipeline.

Parameters:

• experiment_name (str | object | None)

• trial_name (str | object | None)

experiment_name

Type:

The name of the experiment.

trial_name

Type:

The name of the trial.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PipelineSummary(*, pipeline_arn=<sagemaker_core.main.utils.Unassigned object>, pipeline_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_display_name=<sagemaker_core.main.utils.Unassigned object>, pipeline_description=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_execution_time=<sagemaker_core.main.utils.Unassigned object>)

A summary of a pipeline.

Parameters:

• pipeline_arn (str | None)

• pipeline_name (str | object | None)

• pipeline_display_name (str | None)

• pipeline_description (str | None)

• role_arn (str | None)

• creation_time (datetime | None)

• last_modified_time (datetime | None)

• last_execution_time (datetime | None)

pipeline_arn

Type:

The Amazon Resource Name (ARN) of the pipeline.

pipeline_name

Type:

The name of the pipeline.

pipeline_display_name

Type:

The display name of the pipeline.

pipeline_description

Type:

The description of the pipeline.

role_arn

Type:

The Amazon Resource Name (ARN) that the pipeline used to execute.

creation_time

Type:

The creation time of the pipeline.

last_modified_time

Type:

The time that the pipeline was last modified.

last_execution_time

Type:

The last time that a pipeline execution began.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PredefinedMetricSpecification(*, predefined_metric_type=<sagemaker_core.main.utils.Unassigned object>)

A specification for a predefined metric.

Parameters:

predefined_metric_type (str | None)

predefined_metric_type

Type:

The metric type. You can only apply SageMaker metric types to SageMaker endpoints.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PriorityClass(*, name, weight)

Priority class configuration. When included in PriorityClasses, these class configurations define how tasks are queued.

Parameters:

• name (str)

• weight (int)

name

Type:

Name of the priority class.

weight

Type:

Weight of the priority class. The value is within a range from 0 to 100, where 0 is the default. A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingClusterConfig(*, instance_count, instance_type, volume_size_in_gb, volume_kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Configuration for the cluster used to run a processing job.

Parameters:

• instance_count (int)

• instance_type (str)

• volume_size_in_gb (int)

• volume_kms_key_id (str | None)

instance_count

Type:

The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.

instance_type

Type:

The ML compute instance type for the processing job.

volume_size_in_gb

Type:

The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario. Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can’t request a VolumeSizeInGB greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.

volume_kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job. Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can’t request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingFeatureStoreOutput(*, feature_group_name)

Configuration for processing job outputs in Amazon SageMaker Feature Store.

Parameters:

feature_group_name (str | object)

feature_group_name

Type:

The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingInput(*, input_name, app_managed=<sagemaker_core.main.utils.Unassigned object>, s3_input=<sagemaker_core.main.utils.Unassigned object>, dataset_definition=<sagemaker_core.main.utils.Unassigned object>)

The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.

Parameters:

• input_name (str)

• app_managed (bool | None)

• s3_input (ProcessingS3Input | None)

• dataset_definition (DatasetDefinition | None)

input_name

Type:

The name for the processing job input.

app_managed

Type:

When True, input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.

s3_input

Type:

Configuration for downloading input data from Amazon S3 into the processing container.

dataset_definition

Type:

Configuration for a Dataset Definition input.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingJob(*, processing_inputs=<sagemaker_core.main.utils.Unassigned object>, processing_output_config=<sagemaker_core.main.utils.Unassigned object>, processing_job_name=<sagemaker_core.main.utils.Unassigned object>, processing_resources=<sagemaker_core.main.utils.Unassigned object>, stopping_condition=<sagemaker_core.main.utils.Unassigned object>, app_specification=<sagemaker_core.main.utils.Unassigned object>, environment=<sagemaker_core.main.utils.Unassigned object>, network_config=<sagemaker_core.main.utils.Unassigned object>, role_arn=<sagemaker_core.main.utils.Unassigned object>, experiment_config=<sagemaker_core.main.utils.Unassigned object>, processing_job_arn=<sagemaker_core.main.utils.Unassigned object>, processing_job_status=<sagemaker_core.main.utils.Unassigned object>, exit_message=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, processing_end_time=<sagemaker_core.main.utils.Unassigned object>, processing_start_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, monitoring_schedule_arn=<sagemaker_core.main.utils.Unassigned object>, auto_ml_job_arn=<sagemaker_core.main.utils.Unassigned object>, training_job_arn=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>)

An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information, see Process Data and Evaluate Models.

Parameters:

• processing_inputs (List[ProcessingInput] | None)

• processing_output_config (ProcessingOutputConfig | None)

• processing_job_name (str | object | None)

• processing_resources (ProcessingResources | None)

• stopping_condition (ProcessingStoppingCondition | None)

• app_specification (AppSpecification | None)

• environment (Dict[str, str] | None)

• network_config (NetworkConfig | None)

• role_arn (str | None)

• experiment_config (ExperimentConfig | None)

• processing_job_arn (str | None)

• processing_job_status (str | None)

• exit_message (str | None)

• failure_reason (str | None)

• processing_end_time (datetime | None)

• processing_start_time (datetime | None)

• last_modified_time (datetime | None)

• creation_time (datetime | None)

• monitoring_schedule_arn (str | None)

• auto_ml_job_arn (str | None)

• training_job_arn (str | None)

• tags (List[Tag] | None)

processing_inputs

Type:

List of input configurations for the processing job.

processing_output_config

Type:

sagemaker_core.main.shapes.ProcessingOutputConfig | None

processing_job_name

Type:

The name of the processing job.

processing_resources

Type:

sagemaker_core.main.shapes.ProcessingResources | None

stopping_condition

Type:

sagemaker_core.main.shapes.ProcessingStoppingCondition | None

app_specification

Type:

sagemaker_core.main.shapes.AppSpecification | None

environment

Type:

Sets the environment variables in the Docker container.

network_config

Type:

sagemaker_core.main.shapes.NetworkConfig | None

role_arn

Type:

The ARN of the role used to create the processing job.

experiment_config

Type:

sagemaker_core.main.shapes.ExperimentConfig | None

processing_job_arn

Type:

The ARN of the processing job.

processing_job_status

Type:

The status of the processing job.

exit_message

Type:

A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

failure_reason

Type:

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

processing_end_time

Type:

The time that the processing job ended.

processing_start_time

Type:

The time that the processing job started.

last_modified_time

Type:

The time the processing job was last modified.

creation_time

Type:

The time the processing job was created.

monitoring_schedule_arn

Type:

The ARN of a monitoring schedule for an endpoint associated with this processing job.

auto_ml_job_arn

Type:

The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.

training_job_arn

Type:

The ARN of the training job associated with this processing job.

tags

Type:

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingJobStepMetadata(*, arn=<sagemaker_core.main.utils.Unassigned object>)

Metadata for a processing job step.

Parameters:

arn (str | None)

arn

Type:

The Amazon Resource Name (ARN) of the processing job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingJobSummary(*, processing_job_name, processing_job_arn, creation_time, processing_job_status, processing_end_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, failure_reason=<sagemaker_core.main.utils.Unassigned object>, exit_message=<sagemaker_core.main.utils.Unassigned object>)

Summary of information about a processing job.

Parameters:

• processing_job_name (str | object)

• processing_job_arn (str)

• creation_time (datetime)

• processing_job_status (str)

• processing_end_time (datetime | None)

• last_modified_time (datetime | None)

• failure_reason (str | None)

• exit_message (str | None)

processing_job_name

Type:

The name of the processing job.

processing_job_arn

Type:

The Amazon Resource Name (ARN) of the processing job..

creation_time

Type:

The time at which the processing job was created.

processing_end_time

Type:

The time at which the processing job completed.

last_modified_time

Type:

A timestamp that indicates the last time the processing job was modified.

processing_job_status

Type:

The status of the processing job.

failure_reason

Type:

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

exit_message

Type:

An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingOutput(*, output_name, s3_output=<sagemaker_core.main.utils.Unassigned object>, feature_store_output=<sagemaker_core.main.utils.Unassigned object>, app_managed=<sagemaker_core.main.utils.Unassigned object>)

Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.

Parameters:

• output_name (str)

• s3_output (ProcessingS3Output | None)

• feature_store_output (ProcessingFeatureStoreOutput | None)

• app_managed (bool | None)

output_name

Type:

The name for the processing job output.

s3_output

Type:

Configuration for processing job outputs in Amazon S3.

feature_store_output

Type:

Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.

app_managed

Type:

When True, output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingOutputConfig(*, outputs, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Configuration for uploading output from the processing container.

Parameters:

• outputs (List[ProcessingOutput])

• kms_key_id (str | None)

outputs

Type:

An array of outputs configuring the data to upload from the processing container.

kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingResources(*, cluster_config)

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

Parameters:

cluster_config (ProcessingClusterConfig)

cluster_config

Type:

The configuration for the resources in a cluster used to run the processing job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingS3Input(*, s3_uri, s3_data_type, local_path=<sagemaker_core.main.utils.Unassigned object>, s3_input_mode=<sagemaker_core.main.utils.Unassigned object>, s3_data_distribution_type=<sagemaker_core.main.utils.Unassigned object>, s3_compression_type=<sagemaker_core.main.utils.Unassigned object>)

Configuration for downloading input data from Amazon S3 into the processing container.

Parameters:

• s3_uri (str)

• s3_data_type (str)

• local_path (str | None)

• s3_input_mode (str | None)

• s3_data_distribution_type (str | None)

• s3_compression_type (str | None)

s3_uri

Type:

The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.

local_path

Type:

The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/. LocalPath is a required parameter when AppManaged is False (default).

s3_data_type

Type:

Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.

s3_input_mode

Type:

Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.

s3_data_distribution_type

Type:

Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.

s3_compression_type

Type:

Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingS3Output(*, s3_uri, s3_upload_mode, local_path=<sagemaker_core.main.utils.Unassigned object>)

Configuration for uploading output data to Amazon S3 from the processing container.

Parameters:

• s3_uri (str)

• s3_upload_mode (str)

• local_path (str | None)

s3_uri

Type:

A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.

local_path

Type:

The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container’s entrypoint is invoked.

s3_upload_mode

Type:

Whether to upload the results of the processing job continuously or after the job completes.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProcessingStoppingCondition(*, max_runtime_in_seconds)

Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.

Parameters:

max_runtime_in_seconds (int)

max_runtime_in_seconds

Type:

Specifies the maximum runtime in seconds.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariant(*, variant_name, model_name=<sagemaker_core.main.utils.Unassigned object>, initial_instance_count=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, initial_variant_weight=<sagemaker_core.main.utils.Unassigned object>, accelerator_type=<sagemaker_core.main.utils.Unassigned object>, core_dump_config=<sagemaker_core.main.utils.Unassigned object>, serverless_config=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, model_data_download_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>, container_startup_health_check_timeout_in_seconds=<sagemaker_core.main.utils.Unassigned object>, enable_ssm_access=<sagemaker_core.main.utils.Unassigned object>, managed_instance_scaling=<sagemaker_core.main.utils.Unassigned object>, routing_config=<sagemaker_core.main.utils.Unassigned object>, inference_ami_version=<sagemaker_core.main.utils.Unassigned object>)

Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants.

Parameters:

• variant_name (str)

• model_name (str | object | None)

• initial_instance_count (int | None)

• instance_type (str | None)

• initial_variant_weight (float | None)

• accelerator_type (str | None)

• core_dump_config (ProductionVariantCoreDumpConfig | None)

• serverless_config (ProductionVariantServerlessConfig | None)

• volume_size_in_gb (int | None)

• model_data_download_timeout_in_seconds (int | None)

• container_startup_health_check_timeout_in_seconds (int | None)

• enable_ssm_access (bool | None)

• managed_instance_scaling (ProductionVariantManagedInstanceScaling | None)

• routing_config (ProductionVariantRoutingConfig | None)

• inference_ami_version (str | None)

variant_name

Type:

The name of the production variant.

model_name

Type:

The name of the model that you want to host. This is the name that you specified when creating the model.

initial_instance_count

Type:

Number of instances to launch initially.

instance_type

Type:

The ML compute instance type.

initial_variant_weight

Type:

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

accelerator_type

Type:

This parameter is no longer supported. Elastic Inference (EI) is no longer available. This parameter was used to specify the size of the EI instance to use for the production variant.

core_dump_config

Type:

Specifies configuration for a core dump from the model container when the process crashes.

serverless_config

Type:

The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

volume_size_in_gb

Type:

The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

model_data_download_timeout_in_seconds

Type:

The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

container_startup_health_check_timeout_in_seconds

Type:

The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

enable_ssm_access

Type:

You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint.

managed_instance_scaling

Type:

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

routing_config

Type:

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

inference_ami_version

Type:

Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads. By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions. The AMI version names, and their configurations, are the following: al2-ami-sagemaker-inference-gpu-2 Accelerator: GPU NVIDIA driver version: 535 CUDA version: 12.2 al2-ami-sagemaker-inference-gpu-2-1 Accelerator: GPU NVIDIA driver version: 535 CUDA version: 12.2 NVIDIA Container Toolkit with disabled CUDA-compat mounting al2-ami-sagemaker-inference-gpu-3-1 Accelerator: GPU NVIDIA driver version: 550 CUDA version: 12.4 NVIDIA Container Toolkit with disabled CUDA-compat mounting

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantCoreDumpConfig(*, destination_s3_uri, kms_key_id=<sagemaker_core.main.utils.Unassigned object>)

Specifies configuration for a core dump from the model container when the process crashes.

Parameters:

• destination_s3_uri (str)

• kms_key_id (str | None)

destination_s3_uri

Type:

The Amazon S3 bucket to send the core dump to.

kms_key_id

Type:

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats: // KMS Key ID “1234abcd-12ab-34cd-56ef-1234567890ab” // Amazon Resource Name (ARN) of a KMS Key “arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab” // KMS Key Alias “alias/ExampleAlias” // Amazon Resource Name (ARN) of a KMS Key Alias “arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias” If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don’t provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role’s account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to “aws:kms”. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantManagedInstanceScaling(*, status=<sagemaker_core.main.utils.Unassigned object>, min_instance_count=<sagemaker_core.main.utils.Unassigned object>, max_instance_count=<sagemaker_core.main.utils.Unassigned object>)

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

Parameters:

• status (str | None)

• min_instance_count (int | None)

• max_instance_count (int | None)

status

Type:

Indicates whether managed instance scaling is enabled.

min_instance_count

Type:

The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

max_instance_count

Type:

The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantRoutingConfig(*, routing_strategy)

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

Parameters:

routing_strategy (str)

routing_strategy

Type:

Sets how the endpoint routes incoming traffic: LEAST_OUTSTANDING_REQUESTS: The endpoint routes requests to the specific instances that have more capacity to process them. RANDOM: The endpoint routes each request to a randomly chosen instance.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantServerlessConfig(*, memory_size_in_mb, max_concurrency, provisioned_concurrency=<sagemaker_core.main.utils.Unassigned object>)

Specifies the serverless configuration for an endpoint variant.

Parameters:

• memory_size_in_mb (int)

• max_concurrency (int)

• provisioned_concurrency (int | None)

memory_size_in_mb

Type:

The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

max_concurrency

Type:

The maximum number of concurrent invocations your serverless endpoint can process.

provisioned_concurrency

Type:

The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency. This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantServerlessUpdateConfig(*, max_concurrency=<sagemaker_core.main.utils.Unassigned object>, provisioned_concurrency=<sagemaker_core.main.utils.Unassigned object>)

Specifies the serverless update concurrency configuration for an endpoint variant.

Parameters:

• max_concurrency (int | None)

• provisioned_concurrency (int | None)

max_concurrency

Type:

The updated maximum number of concurrent invocations your serverless endpoint can process.

provisioned_concurrency

Type:

The updated amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantStatus(*, status, status_message=<sagemaker_core.main.utils.Unassigned object>, start_time=<sagemaker_core.main.utils.Unassigned object>)

Describes the status of the production variant.

Parameters:

• status (str)

• status_message (str | None)

• start_time (datetime | None)

status

Type:

The endpoint variant status which describes the current deployment stage status or operational status. Creating: Creating inference resources for the production variant. Deleting: Terminating inference resources for the production variant. Updating: Updating capacity for the production variant. ActivatingTraffic: Turning on traffic for the production variant. Baking: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.

status_message

Type:

A message that describes the status of the production variant.

start_time

Type:

The start time of the current status change.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProductionVariantSummary(*, variant_name, deployed_images=<sagemaker_core.main.utils.Unassigned object>, current_weight=<sagemaker_core.main.utils.Unassigned object>, desired_weight=<sagemaker_core.main.utils.Unassigned object>, current_instance_count=<sagemaker_core.main.utils.Unassigned object>, desired_instance_count=<sagemaker_core.main.utils.Unassigned object>, variant_status=<sagemaker_core.main.utils.Unassigned object>, current_serverless_config=<sagemaker_core.main.utils.Unassigned object>, desired_serverless_config=<sagemaker_core.main.utils.Unassigned object>, managed_instance_scaling=<sagemaker_core.main.utils.Unassigned object>, routing_config=<sagemaker_core.main.utils.Unassigned object>)

Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.

Parameters:

• variant_name (str)

• deployed_images (List[DeployedImage] | None)

• current_weight (float | None)

• desired_weight (float | None)

• current_instance_count (int | None)

• desired_instance_count (int | None)

• variant_status (List[ProductionVariantStatus] | None)

• current_serverless_config (ProductionVariantServerlessConfig | None)

• desired_serverless_config (ProductionVariantServerlessConfig | None)

• managed_instance_scaling (ProductionVariantManagedInstanceScaling | None)

• routing_config (ProductionVariantRoutingConfig | None)

variant_name

Type:

The name of the variant.

deployed_images

Type:

An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.

current_weight

Type:

The weight associated with the variant.

desired_weight

Type:

The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

current_instance_count

Type:

The number of instances associated with the variant.

desired_instance_count

Type:

The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

variant_status

Type:

The endpoint variant status which describes the current deployment stage status or operational status.

current_serverless_config

Type:

The serverless configuration for the endpoint.

desired_serverless_config

Type:

The serverless configuration requested for the endpoint update.

managed_instance_scaling

Type:

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

routing_config

Type:

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProfilerConfig(*, s3_output_path=<sagemaker_core.main.utils.Unassigned object>, profiling_interval_in_milliseconds=<sagemaker_core.main.utils.Unassigned object>, profiling_parameters=<sagemaker_core.main.utils.Unassigned object>, disable_profiler=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

Parameters:

• s3_output_path (str | None)

• profiling_interval_in_milliseconds (int | None)

• profiling_parameters (Dict[str, str] | None)

• disable_profiler (bool | None)

s3_output_path

Type:

Path to Amazon S3 storage location for system and framework metrics.

profiling_interval_in_milliseconds

Type:

A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.

profiling_parameters

Type:

Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

disable_profiler

Type:

Configuration to turn off Amazon SageMaker Debugger’s system monitoring and profiling functionality. To turn it off, set to True.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProfilerConfigForUpdate(*, s3_output_path=<sagemaker_core.main.utils.Unassigned object>, profiling_interval_in_milliseconds=<sagemaker_core.main.utils.Unassigned object>, profiling_parameters=<sagemaker_core.main.utils.Unassigned object>, disable_profiler=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and storage paths.

Parameters:

• s3_output_path (str | None)

• profiling_interval_in_milliseconds (int | None)

• profiling_parameters (Dict[str, str] | None)

• disable_profiler (bool | None)

s3_output_path

Type:

Path to Amazon S3 storage location for system and framework metrics.

profiling_interval_in_milliseconds

Type:

A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.

profiling_parameters

Type:

Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

disable_profiler

Type:

To turn off Amazon SageMaker Debugger monitoring and profiling while a training job is in progress, set to True.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProfilerRuleConfiguration(*, rule_configuration_name, rule_evaluator_image, local_path=<sagemaker_core.main.utils.Unassigned object>, s3_output_path=<sagemaker_core.main.utils.Unassigned object>, instance_type=<sagemaker_core.main.utils.Unassigned object>, volume_size_in_gb=<sagemaker_core.main.utils.Unassigned object>, rule_parameters=<sagemaker_core.main.utils.Unassigned object>)

Configuration information for profiling rules.

Parameters:

• rule_configuration_name (str)

• rule_evaluator_image (str)

• local_path (str | None)

• s3_output_path (str | None)

• instance_type (str | None)

• volume_size_in_gb (int | None)

• rule_parameters (Dict[str, str] | None)

rule_configuration_name

Type:

The name of the rule configuration. It must be unique relative to other rule configuration names.

local_path

Type:

Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.

s3_output_path

Type:

Path to Amazon S3 storage location for rules.

rule_evaluator_image

Type:

The Amazon Elastic Container Registry Image for the managed rule evaluation.

instance_type

Type:

The instance type to deploy a custom rule for profiling a training job.

volume_size_in_gb

Type:

The size, in GB, of the ML storage volume attached to the processing instance.

rule_parameters

Type:

Runtime configuration for rule container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProfilerRuleEvaluationStatus(*, rule_configuration_name=<sagemaker_core.main.utils.Unassigned object>, rule_evaluation_job_arn=<sagemaker_core.main.utils.Unassigned object>, rule_evaluation_status=<sagemaker_core.main.utils.Unassigned object>, status_details=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>)

Information about the status of the rule evaluation.

Parameters:

• rule_configuration_name (str | None)

• rule_evaluation_job_arn (str | None)

• rule_evaluation_status (str | None)

• status_details (str | None)

• last_modified_time (datetime | None)

rule_configuration_name

Type:

The name of the rule configuration.

rule_evaluation_job_arn

Type:

The Amazon Resource Name (ARN) of the rule evaluation job.

rule_evaluation_status

Type:

Status of the rule evaluation.

status_details

Type:

Details from the rule evaluation.

last_modified_time

Type:

Timestamp when the rule evaluation status was last modified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.Project(*, project_arn=<sagemaker_core.main.utils.Unassigned object>, project_name=<sagemaker_core.main.utils.Unassigned object>, project_id=<sagemaker_core.main.utils.Unassigned object>, project_description=<sagemaker_core.main.utils.Unassigned object>, service_catalog_provisioning_details=<sagemaker_core.main.utils.Unassigned object>, service_catalog_provisioned_product_details=<sagemaker_core.main.utils.Unassigned object>, project_status=<sagemaker_core.main.utils.Unassigned object>, created_by=<sagemaker_core.main.utils.Unassigned object>, creation_time=<sagemaker_core.main.utils.Unassigned object>, tags=<sagemaker_core.main.utils.Unassigned object>, last_modified_time=<sagemaker_core.main.utils.Unassigned object>, last_modified_by=<sagemaker_core.main.utils.Unassigned object>)

The properties of a project as returned by the Search API.

Parameters:

• project_arn (str | None)

• project_name (str | object | None)

• project_id (str | None)

• project_description (str | None)

• service_catalog_provisioning_details (ServiceCatalogProvisioningDetails | None)

• service_catalog_provisioned_product_details (ServiceCatalogProvisionedProductDetails | None)

• project_status (str | None)

• created_by (UserContext | None)

• creation_time (datetime | None)

• tags (List[Tag] | None)

• last_modified_time (datetime | None)

• last_modified_by (UserContext | None)

project_arn

Type:

The Amazon Resource Name (ARN) of the project.

project_name

Type:

The name of the project.

project_id

Type:

The ID of the project.

project_description

Type:

The description of the project.

service_catalog_provisioning_details

Type:

sagemaker_core.main.shapes.ServiceCatalogProvisioningDetails | None

service_catalog_provisioned_product_details

Type:

sagemaker_core.main.shapes.ServiceCatalogProvisionedProductDetails | None

project_status

Type:

The status of the project.

created_by

Type:

Who created the project.

creation_time

Type:

A timestamp specifying when the project was created.

tags

Type:

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

last_modified_time

Type:

A timestamp container for when the project was last modified.

last_modified_by

Type:

sagemaker_core.main.shapes.UserContext | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProjectSummary(*, project_name, project_arn, project_id, creation_time, project_status, project_description=<sagemaker_core.main.utils.Unassigned object>)

Information about a project.

Parameters:

• project_name (str | object)

• project_arn (str)

• project_id (str)

• creation_time (datetime)

• project_status (str)

• project_description (str | None)

project_name

Type:

The name of the project.

project_description

Type:

The description of the project.

project_arn

Type:

The Amazon Resource Name (ARN) of the project.

project_id

Type:

The ID of the project.

creation_time

Type:

The time that the project was created.

project_status

Type:

The status of the project.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PropertyNameQuery(*, property_name_hint)

Part of the SuggestionQuery type. Specifies a hint for retrieving property names that begin with the specified text.

Parameters:

property_name_hint (str)

property_name_hint

Type:

Text that begins a property’s name.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PropertyNameSuggestion(*, property_name=<sagemaker_core.main.utils.Unassigned object>)

A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.

Parameters:

property_name (str | None)

property_name

Type:

A suggested property name based on what you entered in the search textbox in the SageMaker console.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.ProvisioningParameter(*, key=<sagemaker_core.main.utils.Unassigned object>, value=<sagemaker_core.main.utils.Unassigned object>)

A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.

Parameters:

• key (str | None)

• value (str | None)

key

Type:

The key that identifies a provisioning parameter.

value

Type:

The value of the provisioning parameter.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.PublicWorkforceTaskPrice(*, amount_in_usd=<sagemaker_core.main.utils.Unassigned object>)

Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed. Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer. 0.036 0.048 0.060 0.072 0.120 0.240 0.360 0.480 0.600 0.720 0.840 0.960 1.080 1.200 Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars. 0.012 0.024 0.036 0.048 0.060 0.072 0.120 0.240 0.360 0.480 0.600 0.720 0.840 0.960 1.080 1.200 Use one of the following prices for semantic segmentation tasks. Prices are in US dollars. 0.840 0.960 1.080 1.200 Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars. 2.400 2.280 2.160 2.040 1.920 1.800 1.680 1.560 1.440 1.320 1.200 1.080 0.960 0.840 0.720 0.600 0.480 0.360 0.240 0.120 0.072 0.060 0.048 0.036 0.024 0.012 Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars. 1.200 1.080 0.960 0.840 0.720 0.600 0.480 0.360 0.240 0.120 0.072 0.060 0.048 0.036 0.024 0.012 Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars. 1.200 1.080 0.960 0.840 0.720 0.600 0.480 0.360 0.240 0.120 0.072 0.060 0.048 0.036 0.024 0.012

Parameters:

amount_in_usd (USD | None)

amount_in_usd

Type:

Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.QualityCheckStepMetadata(*, check_type=<sagemaker_core.main.utils.Unassigned object>, baseline_used_for_drift_check_statistics=<sagemaker_core.main.utils.Unassigned object>, baseline_used_for_drift_check_constraints=<sagemaker_core.main.utils.Unassigned object>, calculated_baseline_statistics=<sagemaker_core.main.utils.Unassigned object>, calculated_baseline_constraints=<sagemaker_core.main.utils.Unassigned object>, model_package_group_name=<sagemaker_core.main.utils.Unassigned object>, violation_report=<sagemaker_core.main.utils.Unassigned object>, check_job_arn=<sagemaker_core.main.utils.Unassigned object>, skip_check=<sagemaker_core.main.utils.Unassigned object>, register_new_baseline=<sagemaker_core.main.utils.Unassigned object>)

Container for the metadata for a Quality check step. For more information, see the topic on QualityCheck step in the Amazon SageMaker Developer Guide.

Parameters:

• check_type (str | None)

• baseline_used_for_drift_check_statistics (str | None)

• baseline_used_for_drift_check_constraints (str | None)

• calculated_baseline_statistics (str | None)

• calculated_baseline_constraints (str | None)

• model_package_group_name (str | object | None)

• violation_report (str | None)

• check_job_arn (str | None)

• skip_check (bool | None)

• register_new_baseline (bool | None)

check_type

Type:

The type of the Quality check step.

baseline_used_for_drift_check_statistics

Type:

The Amazon S3 URI of the baseline statistics file used for the drift check.

baseline_used_for_drift_check_constraints

Type:

The Amazon S3 URI of the baseline constraints file used for the drift check.

calculated_baseline_statistics

Type:

The Amazon S3 URI of the newly calculated baseline statistics file.

calculated_baseline_constraints

Type:

The Amazon S3 URI of the newly calculated baseline constraints file.

model_package_group_name

Type:

The model package group name.

violation_report

Type:

The Amazon S3 URI of violation report if violations are detected.

check_job_arn

Type:

The Amazon Resource Name (ARN) of the Quality check processing job that was run by this step execution.

skip_check

Type:

This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.

register_new_baseline

Type:

This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics properties.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.QueryFilters(*, types=<sagemaker_core.main.utils.Unassigned object>, lineage_types=<sagemaker_core.main.utils.Unassigned object>, created_before=<sagemaker_core.main.utils.Unassigned object>, created_after=<sagemaker_core.main.utils.Unassigned object>, modified_before=<sagemaker_core.main.utils.Unassigned object>, modified_after=<sagemaker_core.main.utils.Unassigned object>, properties=<sagemaker_core.main.utils.Unassigned object>)

A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the QueryLineage API action.

Parameters:

• types (List[str] | None)

• lineage_types (List[str] | None)

• created_before (datetime | None)

• created_after (datetime | None)

• modified_before (datetime | None)

• modified_after (datetime | None)

• properties (Dict[str, str] | None)

types

Type:

Filter the lineage entities connected to the StartArn by type. For example: DataSet, Model, Endpoint, or ModelDeployment.

lineage_types

Type:

Filter the lineage entities connected to the StartArn(s) by the type of the lineage entity.

created_before

Type:

Filter the lineage entities connected to the StartArn(s) by created date.

created_after

Type:

Filter the lineage entities connected to the StartArn(s) after the create date.

modified_before

Type:

Filter the lineage entities connected to the StartArn(s) before the last modified date.

modified_after

Type:

Filter the lineage entities connected to the StartArn(s) after the last modified date.

properties

Type:

Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs. If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class sagemaker_core.main.shapes.RSessionAppSettings(*, default_resource_spec=<sagemaker_core.main.utils.Unassigned object>, custom_images=<sagemaker_core.main.utils.Unassigned object>)

A collection of settings that apply to an RSessionGateway app.

Parameters:

• default_resource_spec (ResourceSpec | None)

• custom_images (List[CustomImage] | None)

default_resource_spec

Type:

sagemaker_core.main.shapes.ResourceSpec | None

custom_images

Type:

A list of custom SageMaker AI images that are configured to run as a RSession app.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].