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MLflow 1.12.0

· One min read
MLflow maintainers

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.