describe_mlflow_app `async` ¶

describe_mlflow_app(
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    arn: Optional[MlflowAppArn] = None,
) -> DescribeMlflowAppResponse

Returns information about an MLflow App.

Parameters:

arn (Optional[MlflowAppArn], default: None ) –

The ARN of the MLflow App for which to get information.

Raises:

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 describe_mlflow_app(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    arn: Optional["capo_sagemaker.types.mlflow_app_arn.MlflowAppArn"] = None,
) -> "capo_sagemaker.types.describe_mlflow_app_response.DescribeMlflowAppResponse":
    """<p>Returns information about an MLflow App.</p>

    Args:
        arn: <p>The ARN of the MLflow App for which to get information.</p>

    Raises:
        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.describe_mlflow_app_request.DescribeMlflowAppRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker.types.describe_mlflow_app_response.DescribeMlflowAppResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_mlflow_app

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeMlflowApp")
    )
    input_: capo_sagemaker.types.describe_mlflow_app_request.DescribeMlflowAppRequest = {}
    if arn is not None:
        input_["arn"] = arn

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

describe_mlflow_app async ¶

describe_mlflow_app `async` ¶