hi all,
I have been working on the concept on of centralized workspace where I have been trying to create a centralized workspace repo.
I am running my model on Workspace A, while I am logging my model on to a remote Databricks workspace B,
I have connected the two repo's by using set_tracking_uri(),
destination_workspace_url = "xxxxxxxxxxxxxxxxxxxxxx"
destination_access_token = "xxxxxxxxxxxxxxxxxxxxxxxx"
import mlflow
import os
from mlflow.tracking import MlflowClient
client = MlflowClient()
os.environ['MLFLOW_TRACKING_TOKEN'] = destination_access_token
mlflow.set_tracking_uri(destination_workspace_url)
tracking_uri = mlflow.set_tracking_uri(destination_workspace_url)
experiment = mlflow.set_experiment("/Users/xxxxxxxxxxxxx/mlFlow_centralized_rep")
print(experiment)
Now the connection is successful, also the hyper parameters and the output score is getting logged but when I am trying to log the model using the command ,
mlflow.pyfunc.log_model("random_forest_model_v10", python_model=wrappedModel, conda_env=conda_env, signature=signature)
this doesn't seem to work I am encountered with the following error,
REST EXCEPTION: Resource id "xxxxxxxxx" does not exist.
I have tried configuring the compute cluster with the databricks configure --token
But this does not seem to work, it's not logging the artifacts that's the main issue here. Any suggestions on this please ?
