We are happy to announce the availability of MLflow 1.12.0!

In addition to bug and documentation fixes, MLflow 1.12.0 includes several major features and improvements, in particular a number of improvements to MLflow’s Pytorch integrations and autologging:

PyTorch

  • mlflow.pytorch.log_model, mlflow.pytorch.load_model now support logging/loading TorchScript models (#3557, @shrinath-suresh)
  • mlflow.pytorch.log_model supports passing requirements_file & extra_files arguments to log additional artifacts along with a model (#3436, @shrinath-suresh)

Autologging

  • Add universal mlflow.autolog which enables autologging for all supported integrations (#3561, #3590, @andrewnitu)
  • Add mlflow.pytorch.autolog API for automatic logging of metrics, params, and models from Pytorch Lightning training (#3601, @shrinath-suresh, #3636, @karthik-77). This API is also enabled by mlflow.autolog.
  • Scikit-learn, XGBoost, and LightGBM autologging now support logging model signatures and input examples (#3386, #3403, #3449, @andrewnitu)
  • mlflow.sklearn.autolog now supports logging metrics (e.g. accuracy) and plots (e.g. confusion matrix heat map) (#3423, #3327, @willzhan-db, @harupy)

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