Errors when accessing MLflow artifacts without using the MLflow client
MLflow experiment permissions are now enforced on artifacts in MLflow Tracking, enabling you to easily control access to your datasets, models, and other files.
Invalid mount exception
Problem
When trying to access an MLflow run artifact using Databricks File System (DBFS) commands, such as dbutils.fs
, you get the following error:
com.databricks.backend.daemon.data.common.InvalidMountException: Error while using path /databricks/mlflow-tracking/<experiment-id>/<run-id>/artifacts for resolving path '/<experiment-id>/<run-id>/artifacts' within mount at '/databricks/mlflow-tracking'.
FileNotFoundError
Problem
When trying to access an MLflow run artifact using %sh
/os.listdir()
, you get the following error:
FileNotFoundError: [Errno 2] No such file or directory: '/databricks/mlflow-tracking/'