Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
Parameters:
-
tracking_server_name
(Optional[TrackingServerName], default:
None
)
–
The name of the tracking server to connect to your MLflow UI.
-
expires_in_seconds
(Optional[ExpiresInSeconds], default:
None
)
–
The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.
-
session_expiration_duration_in_seconds
(Optional[SessionExpirationDurationInSeconds], default:
None
)
–
The duration in seconds that your MLflow UI session is valid.
Raises:
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def create_presigned_mlflow_tracking_server_url(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = None,
tracking_server_name: Optional[
"capo_sagemaker.types.tracking_server_name.TrackingServerName"
] = None,
expires_in_seconds: Optional[
"capo_sagemaker.types.expires_in_seconds.ExpiresInSeconds"
] = None,
session_expiration_duration_in_seconds: Optional[
"capo_sagemaker.types.session_expiration_duration_in_seconds.SessionExpirationDurationInSeconds"
] = None,
) -> "capo_sagemaker.types.create_presigned_mlflow_tracking_server_url_response.CreatePresignedMlflowTrackingServerUrlResponse":
"""<p>Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-launch-ui.html">Launch the MLflow UI using a presigned URL</a>.</p>
Args:
tracking_server_name: <p>The name of the tracking server to connect to your MLflow UI.</p>
expires_in_seconds: <p>The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.</p>
session_expiration_duration_in_seconds: <p>The duration in seconds that your MLflow UI session is valid.</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.create_presigned_mlflow_tracking_server_url_request.CreatePresignedMlflowTrackingServerUrlRequest]",
) -> OperationResponse[
"capo_sagemaker.types.create_presigned_mlflow_tracking_server_url_response.CreatePresignedMlflowTrackingServerUrlResponse"
]:
import capo_sagemaker._operations.sage_maker.create_presigned_mlflow_tracking_server_url
output, http_response = (
capo_sagemaker._operations.sage_maker.create_presigned_mlflow_tracking_server_url.create_presigned_mlflow_tracking_server_url(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("CreatePresignedMlflowTrackingServerUrl")
)
input_: capo_sagemaker.types.create_presigned_mlflow_tracking_server_url_request.CreatePresignedMlflowTrackingServerUrlRequest = {}
if tracking_server_name is not None:
input_["tracking_server_name"] = tracking_server_name
if expires_in_seconds is not None:
input_["expires_in_seconds"] = expires_in_seconds
if session_expiration_duration_in_seconds is not None:
input_["session_expiration_duration_in_seconds"] = (
session_expiration_duration_in_seconds
)
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|