Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
Parameters:
Raises:
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def delete_mlflow_tracking_server(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = None,
tracking_server_name: Optional[
"capo_sagemaker.types.tracking_server_name.TrackingServerName"
] = None,
) -> "capo_sagemaker.types.delete_mlflow_tracking_server_response.DeleteMlflowTrackingServerResponse":
"""<p>Deletes an MLflow Tracking Server. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-cleanup.html.html">Clean up MLflow resources</a>.</p>
Args:
tracking_server_name: <p>The name of the the tracking server to delete.</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.delete_mlflow_tracking_server_request.DeleteMlflowTrackingServerRequest]",
) -> OperationResponse[
"capo_sagemaker.types.delete_mlflow_tracking_server_response.DeleteMlflowTrackingServerResponse"
]:
import capo_sagemaker._operations.sage_maker.delete_mlflow_tracking_server
output, http_response = (
capo_sagemaker._operations.sage_maker.delete_mlflow_tracking_server.delete_mlflow_tracking_server(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("DeleteMlflowTrackingServer")
)
input_: capo_sagemaker.types.delete_mlflow_tracking_server_request.DeleteMlflowTrackingServerRequest = {}
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
|