stop_optimization_job ¶

def stop_optimization_job(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    optimization_job_name: Optional[
        "capo_sagemaker.types.entity_name.EntityName"
    ] = None,
) -> None:
    """<p>Ends a running inference optimization job.</p>

    Args:
        optimization_job_name: <p>The name that you assigned to the optimization job.</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_optimization_job_request.StopOptimizationJobRequest]",
    ) -> OperationResponse[None]:
        import capo_sagemaker._operations.sage_maker.stop_optimization_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StopOptimizationJob")
    )
    input_: capo_sagemaker.types.stop_optimization_job_request.StopOptimizationJobRequest = {}
    if optimization_job_name is not None:
        input_["optimization_job_name"] = optimization_job_name

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