We are happy to announce the availability of MLflow 1.13.1!

MLflow 1.13.1 is a patch release containing bug fixes and small changes:

  • Fix bug causing Spark autologging to ignore configuration options specified by mlflow.autolog() (#3917, @dbczumar)
  • Fix bugs causing metrics to be dropped during TensorFlow autologging (#3913, #3914, @dbczumar)
  • Fix incorrect value of optimizer name parameter in autologging PyTorch Lightning (#3901, @harupy)
  • Fix model registry database allow_null_for_run_id migration failure affecting MySQL databases (#3836, @t-henri)
  • Fix failure in transition_model_version_stage when uncanonical stage name is passed (#3929, @harupy)
  • Fix an undefined variable error causing AzureML model deployment to fail (#3922, @eedeleon)
  • Reclassify scikit-learn as a pip dependency in MLflow Model conda environments (#3896, @harupy)
  • Fix experiment view crash and artifact view inconsistency caused by artifact URIs with redundant slashes (#3928, @dbczumar)