We are happy to announce the availability of MLflow 1.18.0!
In addition to bug and documentation fixes, MLflow 1.18.0 includes the following features and improvements:
- Autologging performance improvements for XGBoost, LightGBM, and scikit-learn (#4416, #4473, @dbczumar)
- Add new PaddlePaddle flavor to MLflow Models (#4406, #4439, @jinminhao)
- Introduce paginated ListExperiments API (#3881, @wamartin-aml)
- Include Runtime version for MLflow Models logged on Databricks (#4421, @stevenchen-db)
- MLflow Models now log dependencies in pip requirements.txt format, in addition to existing conda format (#4409, #4422, @stevenchen-db)
- Add support for limiting the number child runs created by autologging for scikit-learn hyperparameter search models (#4382, @mohamad-arabi)
- Improve artifact upload / download performance on Databricks (#4260, @dbczumar)
- Migrate all model dependencies from conda to “pip” section (#4393, @WeichenXu123)
For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.