start_mlflow_tracking_server ¶

def start_mlflow_tracking_server(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    tracking_server_name: Optional[
        "capo_sagemaker.types.tracking_server_name.TrackingServerName"
    ] = None,
) -> "capo_sagemaker.types.start_mlflow_tracking_server_response.StartMlflowTrackingServerResponse":
    """<p>Programmatically start an MLflow Tracking Server.</p>

    Args:
        tracking_server_name: <p>The name of the tracking server to start.</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_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.start_mlflow_tracking_server_request.StartMlflowTrackingServerRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.start_mlflow_tracking_server_response.StartMlflowTrackingServerResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.start_mlflow_tracking_server

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartMlflowTrackingServer")
    )
    input_: capo_sagemaker.types.start_mlflow_tracking_server_request.StartMlflowTrackingServerRequest = {}
    if tracking_server_name is not None:
        input_["tracking_server_name"] = tracking_server_name

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