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MLflow 2.2.0

· 4 min read
MLflow maintainers

We are happy to announce the availability of MLflow 2.2.0!

MLflow 2.2.0 includes several major features and improvements

Features:

Bug fixes:

  • [Recipes] Fix dataset format validation in the ingest step for custom dataset sources (#7638, @sunishsheth2009)
  • [Recipes] Fix bug in identification of worst performing examples during training (#7658, @sunishsheth2009)
  • [Recipes] Ensure consistent rendering of the recipe graph when inspect() is called (#7852, @sunishsheth2009)
  • [Recipes] Correctly respect positive_class configuration in the transform step (#7626, @sunishsheth2009)
  • [Recipes] Make logged metric names consistent with mlflow.evaluate() (#7613, @sunishsheth2009)
  • [Recipes] Add run_id and artifact_path keys to logged MLmodel files (#7651, @sunishsheth2009)
  • [UI] Fix bugs in UI validation of experiment names, model names, and tag keys (#7818, @subramaniam02)
  • [Tracking] Resolve artifact locations to absolute paths when creating experiments (#7670, @bali0019)
  • [Tracking] Exclude Delta checkpoints from Spark datasource autologging (#7902, @harupy)
  • [Tracking] Consistently return an empty list from GetMetricHistory when a metric does not exist (#7589, @bali0019; #7659, @harupy)
  • [Artifacts] Fix support for artifact operations on Windows paths in UNC format (#7750, @bali0019)
  • [Artifacts] Fix bug in HDFS artifact listing (#7581, @pwnywiz)
  • [Model Registry] Disallow creation of model versions with local filesystem sources in mlflow server (#7908, @harupy)
  • [Model Registry] Fix handling of deleted model versions in FileStore (#7716, @harupy)
  • [Model Registry] Correctly initialize Model Registry SQL tables independently of MLflow Tracking (#7704, @harupy)
  • [Models] Correctly move PyTorch model outputs from GPUs to CPUs during inference with pyfunc (#7885, @ankit-db)
  • [Build] Fix compatiblility issues with Python installations compiled using PYTHONOPTIMIZE=2 (#7791, @dbczumar)
  • [Build] Fix compatibility issues with the upcoming pandas 2.0 release (#7899, @harupy; #7910, @dbczumar)

Documentation updates:

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.