Known issues and limitations
CML has the following known issues and limitations with experiments and MLflow.
- CML currently supports only Python for experiment tracking.
- Experiment runs cannot be created from MLFlow on sessions using Legacy Engine. Instead, create a session using an ML Runtime.
- The version column in the runs table is empty for every run. In a future release, this will show a git commit sha for projects using git.
- There is currently no mechanism for registering a model to a Model Registry. In a future release, you will be able to register models to a Model Registry and then deploy Model REST APIs with those models.
- Browsing an empty experiment will display a spinner that doesn’t go away.
- Running an experiment from the workbench (from the dropdown menu) refers to legacy experiments and should not be used going forward.
- Tag/Metrics/Parameter columns that were previously hidden on the runs table will be remembered, but CML won’t remember hiding any of the other columns (date, version, user, etc.)
- Admins can not browse all experiments. They can only see their experiments on the global Experiment page.
- Performance issues may arise when browsing the run details of a run with a lot of metric results, or when comparing a lot of runs.
- Runs can not be deleted or archived.