create_studio_lifecycle_config ¶

def create_studio_lifecycle_config(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    studio_lifecycle_config_name: Optional[
        "capo_sagemaker.types.studio_lifecycle_config_name.StudioLifecycleConfigName"
    ] = None,
    studio_lifecycle_config_content: Optional[
        "capo_sagemaker.types.studio_lifecycle_config_content.StudioLifecycleConfigContent"
    ] = None,
    studio_lifecycle_config_app_type: Optional[
        "capo_sagemaker.types.studio_lifecycle_config_app_type.StudioLifecycleConfigAppType"
    ] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
) -> "capo_sagemaker.types.create_studio_lifecycle_config_response.CreateStudioLifecycleConfigResponse":
    """<p>Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.</p>

    Args:
        studio_lifecycle_config_name: <p>The name of the Amazon SageMaker AI Studio Lifecycle Configuration to create.</p>
        studio_lifecycle_config_content: <p>The content of your Amazon SageMaker AI Studio Lifecycle Configuration script. This content must be base64 encoded.</p>
        studio_lifecycle_config_app_type: <p>The App type that the Lifecycle Configuration is attached to.</p>
        tags: <p>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. </p>

    Raises:
        capo_sagemaker.errors.resource_in_use.ResourceInUse: <p>Resource being accessed is in use.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.create_studio_lifecycle_config_request.CreateStudioLifecycleConfigRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_studio_lifecycle_config_response.CreateStudioLifecycleConfigResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.create_studio_lifecycle_config

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateStudioLifecycleConfig")
    )
    input_: capo_sagemaker.types.create_studio_lifecycle_config_request.CreateStudioLifecycleConfigRequest = {}
    if studio_lifecycle_config_name is not None:
        input_["studio_lifecycle_config_name"] = studio_lifecycle_config_name
    if studio_lifecycle_config_content is not None:
        input_["studio_lifecycle_config_content"] = studio_lifecycle_config_content
    if studio_lifecycle_config_app_type is not None:
        input_["studio_lifecycle_config_app_type"] = (
            studio_lifecycle_config_app_type
        )
    if tags is not None:
        input_["tags"] = tags

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