list_inference_recommendations_job_steps ¶

def list_inference_recommendations_job_steps(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    job_name: Optional[
        "capo_sagemaker.types.recommendation_job_name.RecommendationJobName"
    ] = None,
    status: Optional[
        "capo_sagemaker.types.recommendation_job_status.RecommendationJobStatus"
    ] = None,
    step_type: Optional[
        "capo_sagemaker.types.recommendation_step_type.RecommendationStepType"
    ] = None,
    max_results: Optional["capo_sagemaker.types.max_results.MaxResults"] = None,
    next_token: Optional["capo_sagemaker.types.next_token.NextToken"] = None,
) -> "capo_sagemaker.types.list_inference_recommendations_job_steps_response.ListInferenceRecommendationsJobStepsResponse":
    """<p>Returns a list of the subtasks for an Inference Recommender job.</p> <p>The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.</p>

    Args:
        job_name: <p>The name for the Inference Recommender job.</p>
        status: <p>A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.</p>
        step_type: <p>A filter to return details about the specified type of subtask.</p> <p> <code>BENCHMARK</code>: Evaluate the performance of your model on different instance types.</p>
        max_results: <p>The maximum number of results to return.</p>
        next_token: <p>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.</p>

    Raises:
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.list_inference_recommendations_job_steps_request.ListInferenceRecommendationsJobStepsRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.list_inference_recommendations_job_steps_response.ListInferenceRecommendationsJobStepsResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.list_inference_recommendations_job_steps

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListInferenceRecommendationsJobSteps")
    )
    input_: capo_sagemaker.types.list_inference_recommendations_job_steps_request.ListInferenceRecommendationsJobStepsRequest = {}
    if job_name is not None:
        input_["job_name"] = job_name
    if status is not None:
        input_["status"] = status
    if step_type is not None:
        input_["step_type"] = step_type
    if max_results is not None:
        input_["max_results"] = max_results
    if next_token is not None:
        input_["next_token"] = next_token

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