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create_model_package_group

create_model_package_group(
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    model_package_group_name: Optional[EntityName] = None,
    model_package_group_description: Optional[EntityDescription] = None,
    tags: Optional[TagList] = None,
    managed_configuration: Optional[ManagedConfiguration] = None,
) -> CreateModelPackageGroupOutput

Creates a model group. A model group contains a group of model versions.

Parameters:

Raises:

  • ResourceLimitExceeded –

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

  • UnknownServiceError –

    The service returned an error code this client does not model.

Source code in docs_src/capo_sagemaker/_services/sage_maker.py
def create_model_package_group(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    model_package_group_name: Optional[
        "capo_sagemaker.types.entity_name.EntityName"
    ] = None,
    model_package_group_description: Optional[
        "capo_sagemaker.types.entity_description.EntityDescription"
    ] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
    managed_configuration: Optional[
        "capo_sagemaker.types.managed_configuration.ManagedConfiguration"
    ] = None,
) -> "capo_sagemaker.types.create_model_package_group_output.CreateModelPackageGroupOutput":
    """<p>Creates a model group. A model group contains a group of model versions.</p>

    Args:
        model_package_group_name: <p>The name of the model group.</p>
        model_package_group_description: <p>A description for the model group.</p>
        tags: <p>A list of key value pairs associated with the model group. For more information, see <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services resources</a> in the <i>Amazon Web Services General Reference Guide</i>.</p>
        managed_configuration: <p>The managed configuration of the model package group.</p>

    Raises:
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.create_model_package_group_input.CreateModelPackageGroupInput]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_model_package_group_output.CreateModelPackageGroupOutput"
    ]:
        import capo_sagemaker._operations.sage_maker.create_model_package_group

        output, http_response = (
            capo_sagemaker._operations.sage_maker.create_model_package_group.create_model_package_group(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateModelPackageGroup")
    )
    input_: capo_sagemaker.types.create_model_package_group_input.CreateModelPackageGroupInput = {}
    if model_package_group_name is not None:
        input_["model_package_group_name"] = model_package_group_name
    if model_package_group_description is not None:
        input_["model_package_group_description"] = model_package_group_description
    if tags is not None:
        input_["tags"] = tags
    if managed_configuration is not None:
        input_["managed_configuration"] = managed_configuration

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output