start_ml_labeling_set_generation_task_run ¶

def start_ml_labeling_set_generation_task_run(
    self,
    transform_id: "capo_glue.types.hash_string.HashString",
    output_s3_path: "capo_glue.types.uri_string.UriString",
    *,
    config_overrides: Optional[GlueClientConfig] = None,
) -> "capo_glue.types.start_ml_labeling_set_generation_task_run_response.StartMLLabelingSetGenerationTaskRunResponse":
    """<p>Starts the active learning workflow for your machine learning transform to improve the transform's quality by generating label sets and adding labels.</p> <p>When the <code>StartMLLabelingSetGenerationTaskRun</code> finishes, Glue will have generated a "labeling set" or a set of questions for humans to answer.</p> <p>In the case of the <code>FindMatches</code> transform, these questions are of the form, “What is the correct way to group these rows together into groups composed entirely of matching records?” </p> <p>After the labeling process is finished, you can upload your labels with a call to <code>StartImportLabelsTaskRun</code>. After <code>StartImportLabelsTaskRun</code> finishes, all future runs of the machine learning transform will use the new and improved labels and perform a higher-quality transformation.</p> <p>Note: The role used to write the generated labeling set to the <code>OutputS3Path</code> is the role associated with the Machine Learning Transform, specified in the <code>CreateMLTransform</code> API.</p>

    Args:
        transform_id: <p>The unique identifier of the machine learning transform.</p>
        output_s3_path: <p>The Amazon Simple Storage Service (Amazon S3) path where you generate the labeling set.</p>

    Raises:
        capo_glue.errors.concurrent_runs_exceeded_exception.ConcurrentRunsExceededException: <p>Too many jobs are being run concurrently.</p>
        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.start_ml_labeling_set_generation_task_run_request.StartMLLabelingSetGenerationTaskRunRequest]",
    ) -> OperationResponse[
        "capo_glue.types.start_ml_labeling_set_generation_task_run_response.StartMLLabelingSetGenerationTaskRunResponse"
    ]:
        import capo_glue._operations.aws_glue.start_ml_labeling_set_generation_task_run

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartMLLabelingSetGenerationTaskRun")
    )
    input_: capo_glue.types.start_ml_labeling_set_generation_task_run_request.StartMLLabelingSetGenerationTaskRunRequest = {
        "transform_id": transform_id,
        "output_s3_path": output_s3_path,
    }

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