start_mlflow_tracking_server `async` ¶

start_mlflow_tracking_server(
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    tracking_server_name: Optional[TrackingServerName] = None,
) -> StartMlflowTrackingServerResponse

Programmatically start an MLflow Tracking Server.

Parameters:

tracking_server_name (Optional[TrackingServerName], default: None ) –

The name of the tracking server to start.

Raises:

ConflictException –

There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
ResourceNotFound –

Resource being access is not found.
UnknownServiceError –

The service returned an error code this client does not model.

Source code in docs_src/capo_sagemaker/_services/async_sage_maker.py

async def start_mlflow_tracking_server(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = 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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker.types.start_mlflow_tracking_server_request.StartMlflowTrackingServerRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker.types.start_mlflow_tracking_server_response.StartMlflowTrackingServerResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.start_mlflow_tracking_server

        (
            output,
            http_response,
        ) = await capo_sagemaker._operations.sage_maker.start_mlflow_tracking_server.async_start_mlflow_tracking_server(
            req.options, req.input
        )
        return AsyncOperationResponse(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 = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output

start_mlflow_tracking_server async ¶

start_mlflow_tracking_server `async` ¶