@dbczumar dbczumar released this Dec 21, 2018 · 3 commits to branch-0.8 since this release

Assets 2

MLflow 0.8.1 introduces several significant improvements:

  • Improved UI responsiveness and load time, especially when displaying experiments containing hundreds to thousands of runs.
  • Improved visualizations, including interactive scatter plots for MLflow run comparisons
  • Expanded support for scoring Python models as Spark UDFs. For more information, see the updated documentation for this feature.
  • By default, saved models will now include a Conda environment specifying all of the dependencies necessary for loading them in a new environment.

Features:

  • [API/CLI] Support for running MLflow projects from ZIP files (#759, @jmorefieldexpe)
  • [Python API] Support for passing model conda environments as dictionaries to save_model and log_model functions (#748, @dbczumar)
  • [Models] Default Anaconda environments have been added to many Python model flavors. By default, models produced by save_model and log_model functions will include an environment that specifies all of the versioned dependencies necessary to load and serve the models. Previously, users had to specify these environments manually. (#705, #707, #708, #749, @dbczumar)
  • [Scoring] Support for synchronous deployment of models to SageMaker (#717, @dbczumar)
  • [Tracking] Include the Git repository URL as a tag when tracking an MLflow run within a Git repository (#741, @whiletruelearn, @mateiz)
  • [UI] Improved runs UI performance by using a react-virtualized table to optimize row rendering (#765, #762, #745, @smurching)
  • [UI] Significant performance improvements for rendering run metrics, tags, and parameter information (#764, #747, @smurching)
  • [UI] Scatter plots, including run comparsion plots, are now interactive (#737, @mateiz)
  • [UI] Extended CSRF support by allowing the MLflow UI server to specify a set of expected headers that clients should set when making AJAX requests (#733, @aarondav)

Bug fixes and documentation updates:

  • [Python/Scoring] MLflow Python models that produce Pandas DataFrames can now be evaluated as Spark UDFs correctly. Spark UDF outputs containing multiple columns of primitive types are now supported (#719, @tomasatdatabricks)
  • [Scoring] Fixed a serialization error that prevented models served with Azure ML from returning Pandas DataFrames (#754, @dbczumar)
  • [Docs] New example demonstrating how the MLflow REST API can be used to create experiments and log run information (#750, kjahan)
  • [Docs] R documentation has been updated for clarity and style consistency (#683, @stbof)
  • [Docs] Added clarification about user setup requirements for executing remote MLflow runs on Databricks (#736, @andyk)

Small bug fixes and doc updates (#768, #715, @smurching; #728, dodysw; #730, mshr-h; #725, @kryptec; #769, #721, @dbczumar; #714, @stbof)