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@sueann sueann released this Mar 19, 2019 · 57 commits to master since this release

Major features:

  • Support for running MLflow Projects in Docker containers. This allows you to include non-Python dependencies in their project environments and provides stronger isolation when running projects. See the Projects documentation for more information. (#555, @marcusrehm; #819, @mparkhe; #970, @dbczumar)
  • Database stores for the MLflow Tracking Server. Support for a scalable and performant backend store was one of the top community requests. This feature enables you to connect to local or remote SQLAlchemy-compatible databases (currently supported flavors include MySQL, PostgreSQL, SQLite, and MS SQL) and is compatible with file backed store. See the Tracking Store documentation for more information. (#756, @AndersonReyes; #800, #844, #847, #848, #860, #868, #975, @mparkhe; #980, @dbczumar)
  • Simplified custom Python model packaging. You can easily include custom preprocessing and postprocessing logic, as well as data dependencies in models with the python_function flavor using updated mlflow.pyfunc Python APIs. For more information, see the Custom Python Models documentation. (#791, #792, #793, #830, #910, @dbczumar)
  • Plugin systems allowing third party libraries to extend MLflow functionality. The proposal document gives the full detail of the three main changes:
    • You can register additional providers of tracking stores using the mlflow.tracking_store entrypoint. (#881, @zblz)
    • You can register additional providers of artifact repositories using the mlflow.artifact_repository entrypoint. (#882, @mociarain)
    • The logic generating run metadata from the run context (e.g. source_name, source_version) has been refactored into an extendable system of run context providers. Plugins can register additional providers using the mlflow.run_context_provider entrypoint, which add to or overwrite tags set by the base library. (#913, #926, #930, #978, @acroz)
  • Support for HTTP authentication to the Tracking Server in the R client. Now you can connect to secure Tracking Servers using credentials set in environment variables, or provide custom plugins for setting the credentials. As an example, this release contains a Databricks plugin that can detect existing Databricks credentials to allow you to connect to the Databricks Tracking Server. (#938, #959, #992, @tomasatdatabricks)

Breaking changes:

  • [Scoring] The pyfunc scoring server now expects requests with the application/json content type to contain json-serialized pandas dataframes in the split format, rather than the records format. See the documentation on deployment for more detail. (#960, @dbczumar) Also, when reading the pandas dataframes from JSON, the scoring server no longer automatically infers data types as it can result in unintentional conversion of data types (#916, @mparkhe).
  • [API] Remove GetMetric & GetParam from the REST API as they are subsumed by GetRun. (#879, @aarondav)

More features and improvements:

  • [UI] Add a button for downloading artifacts (#967, @mateiz)
  • [CLI] Add CLI commands for runs: now you can list, delete, restore, and describe runs through the CLI (#720, @DorIndivo)
  • [CLI] The run command now can take --experiment-name as an argument, as an alternative to the --experiment-id argument. You can also choose to set the _EXPERIMENT_NAME_ENV_VAR environment variable instead of passing in the value explicitly. (#889, #894, @mparke)
  • [Examples] Add Image classification example with Keras. (#743, @tomasatdatabricks )
  • [Artifacts] Add get_artifact_uri() and _download_artifact_from_uri convenience functions (#779)
  • [Artifacts] Allow writing Spark models directly to the target artifact store when possible (#808, @smurching)
  • [Models] PyTorch model persistence improvements to allow persisting definitions and dependencies outside the immediate scope:
    • Add a code_paths parameter to mlflow.pytorch.save_model and mlflow.pytorch.log_model to allow external module dependencies to be specified as paths to python files. (#842, @dbczumar)
    • Improve mlflow.pytorch.save_model to capture class definitions from notebooks and the main scope (#851, #861, @dbczumar)
  • [Runs][R] Allow client to infer context info when creating new run in fluent API (#958, @tomasatdatabricks)
  • [Runs][UI] Support Git Commit hyperlink for Gitlab and Bitbucket. Previously the clickable hyperlink was generated only for Github pages. (#901)
  • [Search][API] Allow param value to have any content, not just alphanumeric characters, ., and - (#788, @mparkhe)
  • [Search][API] Support "filter" string in the SearchRuns API. Corresponding UI improvements are planned for the future (#905, @mparke)
  • [Logging] Basic support for LogBatch. NOTE: The feature is currently experimental and the behavior is expected to change in the near future. (#950, #951, #955, #1001, @smurching)

Bug fixes and documentation updates:

  • [Artifacts] Fix empty-file upload to DBFS in log_artifact and log_artifacts (#895, #818, @smurching)
  • [Artifacts] S3 artifact store: fix path resolution error when artifact root is bucket root (#928, @dbczumar)
  • [UI] Fix a bug with Databricks notebook URL links (#891, @smurching)
  • [Export] Fix for missing run name in csv export (#864, @jimthompson5802)
  • [Example] Correct missing tensorboardX module error in PyTorch example when running in MLflow Docker container (#809, @jimthompson5802)
  • [Scoring][R] Fix local serving of rfunc models (#874, @kevinykuo)
  • [Docs] Improve flavor-specific documentation in Models documentation (#909, @dbczumar)

Small bug fixes and doc updates (#822, #899, #787, #785, #780, #942, @hanyucui; #862, #904, #954, #806, #857, #845, @stbof; #907, #872, @smurching; #896, #858, #836, #859, #923, #939, #933, #931, #952, @dbczumar; #880, @zblz; #876, @acroz; #827, #812, #816, #829, @jimthompson5802; #837, #790, #897, #974, #900, @mparkhe; #831, #798, @aarondav; #814, @sueann; #824, #912, @mateiz; #922, #947, @tomasatdatabricks; #795, @KevYuen; #676, @mlaradji; #906, @4n4nd; #777, @tmielika; #804, @alkersan)

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