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MLflow 1.3.0

· One min read
MLflow maintainers

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.