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CHANGELOG.rst

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Changelog

0.4.2 (2018-08-07)

Breaking changes: None

Features:

  • MLflow experiments REST API and mlflow experiments create now support providing --artifact-location (#232, @aarondav)
  • [UI] Runs can now be sorted by columns, and added a Select All button (#227, @ToonKBC)
  • Databricks File System (DBFS) artifactory support added (#226, @andrewmchen)
  • databricks-cli version upgraded to >= 0.8.0 to support new DatabricksConfigProvider interface (#257, @aarondav)

Bug fixes:

  • MLflow client sends REST API calls using snake_case instead of camelCase field names (#232, @aarondav)
  • Minor bug fixes (#243, #242, @aarondav; #251, @javierluraschi; #245, @smurching; #252, @mateiz)

0.4.1 (2018-08-03)

Breaking changes: None

Features:

  • [Projects] MLflow will use the conda installation directory given by the $MLFLOW_CONDA_HOME if specified (e.g. running conda commands by invoking "$MLFLOW_CONDA_HOME/bin/conda"), defaulting to running "conda" otherwise. (#231, @smurching)
  • [UI] Show GitHub links in the UI for projects run from http(s):// GitHub URLs (#235, @smurching)

Bug fixes:

  • Fix GCSArtifactRepository issue when calling list_artifacts on a path containing nested directories (#233, @jakeret)
  • Fix Spark model support when saving/loading models to/from distributed filesystems (#180, @tomasatdatabricks)
  • Add missing mlflow.version import to sagemaker module (#229, @dbczumar)
  • Validate metric, parameter and run IDs in file store and Python client (#224, @mateiz)
  • Validate that the tracking URI is a remote URI for Databricks project runs (#234, @smurching)
  • Fix bug where we'd fetch git projects at SSH URIs into a local directory with the same name as the URI, instead of into a temporary directory (#236, @smurching)

0.4.0 (2018-08-01)

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:

  • Fixed incompatible file structure returned by GCSArtifactRepository (#173, @jakeret)
  • Fixed metric values going out of order on x axis (#204, @mateiz)
  • Fixed occasional hanging behavior when using the projects.run API (#193, @smurching)
  • Miscellaneous bug and documentation fixes from @aarondav, @andrewmchen, @arinto, @jakeret, @mateiz, @smurching, @stbof

0.3.0 (2018-07-18)

Breaking changes:

  • [MLflow Server] Renamed --artifact-root parameter to --default-artifact-root in mlflow server to better reflect its purpose (#165, @aarondav)

Features:

  • Spark MLlib integration: we now support logging SparkML Models directly in the log_model API, model format, and serving APIs (#72, @tomasatdatabricks)
  • Google Cloud Storage is now supported as an artifact storage root (#152, @bnekolny)
  • Support asychronous/parallel execution of MLflow runs (#82, @smurching)
  • [SageMaker] Support for deleting, updating applications deployed via SageMaker (#145, @dbczumar)
  • [SageMaker] Pushing the MLflow SageMaker container now includes the MLflow version that it was published with (#124, @sueann)
  • [SageMaker] Simplify parameters to SageMaker deploy by providing sane defaults (#126, @sueann)
  • [UI] One-element metrics are now displayed as a bar char (#118, @cryptexis)

Bug fixes:

  • Require gitpython>=2.1.0 (#98, @aarondav)
  • Fixed TensorFlow model loading so that columns match the output names of the exported model (#94, @smurching)
  • Fix SparkUDF when number of columns >= 10 (#97, @aarondav)
  • Miscellaneous bug and documentation fixes from @emres, @dmatrix, @stbof, @gsganden, @dennyglee, @anabranch, @mikehuston, @andrewmchen, @juntai-zheng

0.2.1 (2018-06-28)

This is a patch release fixing some smaller issues after the 0.2.0 release.

  • Switch protobuf implementation to C, fixing a bug related to tensorflow/mlflow import ordering (issues #33 and #77, PR #74, @andrewmchen)
  • Enable running mlflow server without git binary installed (#90, @aarondav)
  • Fix Spark UDF support when running on multi-node clusters (#92, @aarondav)

0.2.0 (2018-06-27)

  • Added mlflow server to provide a remote tracking server. This is akin to mlflow ui with new options:
    • --host to allow binding to any ports (#27, @mdagost)
    • --artifact-root to allow storing artifacts at a remote location, S3 only right now (#78, @mateiz)
    • Server now runs behind gunicorn to allow concurrent requests to be made (#61, @mateiz)
  • Tensorflow integration: we now support logging Tensorflow Models directly in the log_model API, model format, and serving APIs (#28, @juntai-zheng)
  • Added experiments.list_experiments as part of experiments API (#37, @mparkhe)
  • Improved support for unicode strings (#79, @smurching)
  • Diabetes progression example dataset and training code (#56, @dennyglee)
  • Miscellaneous bug and documentation fixes from @Jeffwan, @yupbank, @ndjido, @xueyumusic, @manugarri, @tomasatdatabricks, @stbof, @andyk, @andrewmchen, @jakeret, @0wu, @aarondav

0.1.0 (2018-06-05)

  • Initial version of mlflow.