@smurching smurching released this Aug 1, 2018 · 353 commits to master since this release

Assets 2

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: