We are happy to announce the availability of MLflow 1.3.0!

In addition to several bug and documentation fixes, MLflow 1.3.0 includes the following major features and improvements:

  • The Python client now supports logging & loading models using TensorFlow 2.0
  • Significant performance improvements when fetching runs and experiments in MLflow servers that use SQL database-backed storage
  • New GetExperimentByName REST API endpoint, used in the Python client to speed up set_experiment and get_experiment_by_name
  • New mlflow.delete_run, mlflow.delete_experiment fluent APIs in the Python client
  • New CLI command (mlflow experiments csv) to export runs of an experiment into a CSV
  • Directories can now be logged as artifacts via mlflow.log_artifact in the Python fluent API
  • HTML and geojson artifacts are now rendered in the run UI
  • Keras autologging support for fit_generator Keras API
  • MLflow models packaged as docker containers can be executed via Google Cloud Run
  • Artifact storage configurations are propagated to containers when executing docker-based MLflow projects locally
  • The Python, Java, R clients and UI now retry HTTP requests on 429 (Too Many Requests) errors

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