create_project ¶

def create_project(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    project_name: Optional[
        "capo_sagemaker.types.project_entity_name.ProjectEntityName"
    ] = None,
    project_description: Optional[
        "capo_sagemaker.types.entity_description.EntityDescription"
    ] = None,
    service_catalog_provisioning_details: Optional[
        "capo_sagemaker.types.service_catalog_provisioning_details.ServiceCatalogProvisioningDetails"
    ] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
    template_providers: Optional[
        "capo_sagemaker.types.create_template_provider_list.CreateTemplateProviderList"
    ] = None,
) -> "capo_sagemaker.types.create_project_output.CreateProjectOutput":
    """<p>Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.</p>

    Args:
        project_name: <p>The name of the project.</p>
        project_description: <p>A description for the project.</p>
        service_catalog_provisioning_details: <p>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 <a href="https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html">What is Amazon Web Services Service Catalog</a>.</p>
        tags: <p>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 <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>
        template_providers: <p> An array of template provider configurations for creating infrastructure resources for the project. </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_project_input.CreateProjectInput]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_project_output.CreateProjectOutput"
    ]:
        import capo_sagemaker._operations.sage_maker.create_project

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateProject")
    )
    input_: capo_sagemaker.types.create_project_input.CreateProjectInput = {}
    if project_name is not None:
        input_["project_name"] = project_name
    if project_description is not None:
        input_["project_description"] = project_description
    if service_catalog_provisioning_details is not None:
        input_["service_catalog_provisioning_details"] = (
            service_catalog_provisioning_details
        )
    if tags is not None:
        input_["tags"] = tags
    if template_providers is not None:
        input_["template_providers"] = template_providers

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