send_pipeline_execution_step_success ¶

def send_pipeline_execution_step_success(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    callback_token: Optional[
        "capo_sagemaker.types.callback_token.CallbackToken"
    ] = None,
    output_parameters: Optional[
        "capo_sagemaker.types.output_parameter_list.OutputParameterList"
    ] = None,
    client_request_token: Optional[
        "capo_sagemaker.types.idempotency_token.IdempotencyToken"
    ] = None,
) -> "capo_sagemaker.types.send_pipeline_execution_step_success_response.SendPipelineExecutionStepSuccessResponse":
    """<p>Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).</p>

    Args:
        callback_token: <p>The pipeline generated token from the Amazon SQS queue.</p>
        output_parameters: <p>A list of the output parameters of the callback step.</p>
        client_request_token: <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.</p>

    Raises:
        capo_sagemaker.errors.conflict_exception.ConflictException: <p>There was a conflict when you attempted to modify a SageMaker entity such as an <code>Experiment</code> or <code>Artifact</code>.</p>
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        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.send_pipeline_execution_step_success_request.SendPipelineExecutionStepSuccessRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.send_pipeline_execution_step_success_response.SendPipelineExecutionStepSuccessResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.send_pipeline_execution_step_success

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("SendPipelineExecutionStepSuccess")
    )
    input_: capo_sagemaker.types.send_pipeline_execution_step_success_request.SendPipelineExecutionStepSuccessRequest = {}
    if callback_token is not None:
        input_["callback_token"] = callback_token
    if output_parameters is not None:
        input_["output_parameters"] = output_parameters
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token

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