start_pipeline_execution async ¶

async def start_pipeline_execution(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    pipeline_name: Optional[
        "capo_sagemaker.types.pipeline_name_or_arn.PipelineNameOrArn"
    ] = None,
    pipeline_execution_display_name: Optional[
        "capo_sagemaker.types.pipeline_execution_name.PipelineExecutionName"
    ] = None,
    pipeline_parameters: Optional[
        "capo_sagemaker.types.parameter_list.ParameterList"
    ] = None,
    pipeline_execution_description: Optional[
        "capo_sagemaker.types.pipeline_execution_description.PipelineExecutionDescription"
    ] = None,
    client_request_token: Optional[
        "capo_sagemaker.types.idempotency_token.IdempotencyToken"
    ] = None,
    parallelism_configuration: Optional[
        "capo_sagemaker.types.parallelism_configuration.ParallelismConfiguration"
    ] = None,
    selective_execution_config: Optional[
        "capo_sagemaker.types.selective_execution_config.SelectiveExecutionConfig"
    ] = None,
    pipeline_version_id: Optional[
        "capo_sagemaker.types.pipeline_version_id.PipelineVersionId"
    ] = None,
    mlflow_experiment_name: Optional[
        "capo_sagemaker.types.mlflow_experiment_entity_name.MlflowExperimentEntityName"
    ] = None,
) -> "capo_sagemaker.types.start_pipeline_execution_response.StartPipelineExecutionResponse":
    """<p>Starts a pipeline execution.</p>

    Args:
        pipeline_name: <p>The name or Amazon Resource Name (ARN) of the pipeline.</p>
        pipeline_execution_display_name: <p>The display name of the pipeline execution.</p>
        pipeline_parameters: <p>Contains a list of pipeline parameters. This list can be empty. </p>
        pipeline_execution_description: <p>The description of the pipeline execution.</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 once.</p>
        parallelism_configuration: <p>This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.</p>
        selective_execution_config: <p>The selective execution configuration applied to the pipeline run.</p>
        pipeline_version_id: <p>The ID of the pipeline version to start execution from.</p>
        mlflow_experiment_name: <p> The MLflow experiment name of the pipeline execution. </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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker.types.start_pipeline_execution_request.StartPipelineExecutionRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker.types.start_pipeline_execution_response.StartPipelineExecutionResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.start_pipeline_execution

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartPipelineExecution")
    )
    input_: capo_sagemaker.types.start_pipeline_execution_request.StartPipelineExecutionRequest = {}
    if pipeline_name is not None:
        input_["pipeline_name"] = pipeline_name
    if pipeline_execution_display_name is not None:
        input_["pipeline_execution_display_name"] = pipeline_execution_display_name
    if pipeline_parameters is not None:
        input_["pipeline_parameters"] = pipeline_parameters
    if pipeline_execution_description is not None:
        input_["pipeline_execution_description"] = pipeline_execution_description
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token
    if parallelism_configuration is not None:
        input_["parallelism_configuration"] = parallelism_configuration
    if selective_execution_config is not None:
        input_["selective_execution_config"] = selective_execution_config
    if pipeline_version_id is not None:
        input_["pipeline_version_id"] = pipeline_version_id
    if mlflow_experiment_name is not None:
        input_["mlflow_experiment_name"] = mlflow_experiment_name

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