stop_ai_benchmark_job ¶

def stop_ai_benchmark_job(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    ai_benchmark_job_name: Optional[
        "capo_sagemaker.types.ai_entity_name.AIEntityName"
    ] = None,
) -> (
    "capo_sagemaker.types.stop_ai_benchmark_job_response.StopAIBenchmarkJobResponse"
):
    """<p>Stops a running AI benchmark job.</p>

    Args:
        ai_benchmark_job_name: <p>The name of the AI benchmark job to stop.</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.stop_ai_benchmark_job_request.StopAIBenchmarkJobRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.stop_ai_benchmark_job_response.StopAIBenchmarkJobResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.stop_ai_benchmark_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StopAIBenchmarkJob")
    )
    input_: capo_sagemaker.types.stop_ai_benchmark_job_request.StopAIBenchmarkJobRequest = {}
    if ai_benchmark_job_name is not None:
        input_["ai_benchmark_job_name"] = ai_benchmark_job_name

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