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MLflow 0.4.0

· 2 min read
MLflow maintainers

We are happy to announce the availability of MLflow 0.4.0!

MLflow Release 0.4.0 is ready, released 2018-08-01. The release is available on PyPI and docs are updated. Here are the release notes (also available on GitHub):

Breaking changes:

  • [Projects] Removed the use_temp_cwd argument to mlflow.projects.run() (--new-dir flag in the mlflow run CLI). Runs of local projects now use the local project directory as their working directory. Git projects are still fetched into temporary directories (#215, @smurching)
  • [Tracking] GCS artifact storage is now a pluggable dependency (no longer installed by default). To enable GCS support, install google-cloud-storage on both the client and tracking server via pip (#202, @smurching).
  • [Tracking] Clients running MLflow 0.4.0 and above require a server running MLflow 0.4.0 or above, due to a fix that ensures clients no longer double-serialize JSON into strings when sending data to the server (#200, @aarondav). However, the MLflow 0.4.0 server remains backwards-compatible with older clients (#216, @aarondav)

Features:

  • [Examples] Add a more advanced tracking example: using MLflow with PyTorch and TensorBoard (#203)
  • [Models] H2O model support (#170, @ToonKBC)
  • [Projects] Support for running projects in subdirectories of Git repos (#153, @juntai-zheng)
  • [SageMaker] Support for specifying a compute specification when deploying to SageMaker (#185, @dbczumar)
  • [Server] Added --static-prefix option to serve UI from a specified prefix to MLflow UI and server (#116, @andrewmchen)
  • [Tracking] Azure blob storage support for artifacts (#206, @mateiz)
  • [Tracking] Add support for Databricks-backed RestStore (#200, @aarondav)
  • [UI] Enable productionizing frontend by adding CSRF support (#199, @aarondav)
  • [UI] Update metric and parameter filters to let users control column order (#186, @mateiz)

Bug fixes:

Visit the change log to read about the new features.