Returns information about an MLflow App.
Parameters:
-
arn
(Optional[MlflowAppArn], default:
None
)
–
The ARN of the MLflow App for which to get information.
Raises:
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def describe_mlflow_app(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = 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.
"""
def _handler(
req: "OperationRequest[capo_sagemaker.types.describe_mlflow_app_request.DescribeMlflowAppRequest]",
) -> OperationResponse[
"capo_sagemaker.types.describe_mlflow_app_response.DescribeMlflowAppResponse"
]:
import capo_sagemaker._operations.sage_maker.describe_mlflow_app
output, http_response = (
capo_sagemaker._operations.sage_maker.describe_mlflow_app.describe_mlflow_app(
req.options, req.input
)
)
return OperationResponse(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 = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|