stop_auto_ml_job async ¶

async def stop_auto_ml_job(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    auto_ml_job_name: Optional[
        "capo_sagemaker.types.auto_ml_job_name.AutoMLJobName"
    ] = None,
) -> None:
    """<p>A method for forcing a running job to shut down.</p>

    Args:
        auto_ml_job_name: <p>The name of the object you are requesting.</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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker.types.stop_auto_ml_job_request.StopAutoMLJobRequest]",
    ) -> AsyncOperationResponse[None]:
        import capo_sagemaker._operations.sage_maker.stop_auto_ml_job

        (
            output,
            http_response,
        ) = await capo_sagemaker._operations.sage_maker.stop_auto_ml_job.async_stop_auto_ml_job(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StopAutoMLJob")
    )
    input_: capo_sagemaker.types.stop_auto_ml_job_request.StopAutoMLJobRequest = {}
    if auto_ml_job_name is not None:
        input_["auto_ml_job_name"] = auto_ml_job_name

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output