get_ml_task_run ¶

def get_ml_task_run(
    self,
    transform_id: "capo_glue.types.hash_string.HashString",
    task_run_id: "capo_glue.types.hash_string.HashString",
    *,
    config_overrides: Optional[GlueClientConfig] = None,
) -> "capo_glue.types.get_ml_task_run_response.GetMLTaskRunResponse":
    """<p>Gets details for a specific task run on a machine learning transform. Machine learning task runs are asynchronous tasks that Glue runs on your behalf as part of various machine learning workflows. You can check the stats of any task run by calling <code>GetMLTaskRun</code> with the <code>TaskRunID</code> and its parent transform's <code>TransformID</code>.</p>

    Args:
        transform_id: <p>The unique identifier of the machine learning transform.</p>
        task_run_id: <p>The unique identifier of the task run.</p>

    Raises:
        capo_glue.errors.entity_not_found_exception.EntityNotFoundException: <p>A specified entity does not exist</p>
        capo_glue.errors.internal_service_exception.InternalServiceException: <p>An internal service error occurred.</p>
        capo_glue.errors.invalid_input_exception.InvalidInputException: <p>The input provided was not valid.</p>
        capo_glue.errors.operation_timeout_exception.OperationTimeoutException: <p>The operation timed out.</p>
        capo_glue.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_glue.types.get_ml_task_run_request.GetMLTaskRunRequest]",
    ) -> OperationResponse[
        "capo_glue.types.get_ml_task_run_response.GetMLTaskRunResponse"
    ]:
        import capo_glue._operations.aws_glue.get_ml_task_run

        output, http_response = (
            capo_glue._operations.aws_glue.get_ml_task_run.get_ml_task_run(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetMLTaskRun")
    )
    input_: capo_glue.types.get_ml_task_run_request.GetMLTaskRunRequest = {
        "transform_id": transform_id,
        "task_run_id": task_run_id,
    }

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