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@smurching smurching released this Oct 1, 2019 · 29 commits to master since this release

MLflow 1.3.0 includes several major features and improvements:

Features:

  • The Python client now supports logging & loading models using TensorFlow 2.0 (#1872, @juntai-zheng)
  • Significant performance improvements when fetching runs and experiments in MLflow servers that use SQL database-backed storage (#1767, #1878, #1805 @dbczumar)
  • New GetExperimentByName REST API endpoint, used in the Python client to speed up set_experiment and get_experiment_by_name (#1775, @smurching)
  • New mlflow.delete_run, mlflow.delete_experiment fluent APIs in the Python client(#1396, @MerelTheisenQB)
  • New CLI command (mlflow experiments csv) to export runs of an experiment into a CSV (#1705, @jdlesage)
  • Directories can now be logged as artifacts via mlflow.log_artifact in the Python fluent API (#1697, @apurva-koti)
  • HTML and geojson artifacts are now rendered in the run UI (#1838, @sim-san; #1803, @spadarian)
  • Keras autologging support for fit_generator Keras API (#1757, @charnger)
  • MLflow models packaged as docker containers can be executed via Google Cloud Run (#1778, @ngallot)
  • Artifact storage configurations are propagated to containers when executing docker-based MLflow projects locally (#1621, @nlaille)
  • The Python, Java, R clients and UI now retry HTTP requests on 429 (Too Many Requests) errors (#1846, #1851, #1858, #1859 @tomasatdatabricks; #1847, @smurching)

Bug fixes and documentation updates:

  • The R mlflow_list_artifact API no longer throws when listing artifacts for an empty run (#1862, @smurching)
  • Fixed a bug preventing running the MLflow server against an MS SQL database (#1758, @sifanLV)
  • MLmodel files (artifacts) now correctly display in the run UI (#1819, @ankitmathur-db)
  • The Python mlflow.start_run API now throws when resuming a run whose experiment ID differs from the
    active experiment ID set via mlflow.set_experiment (#1820, @mcminnra).
  • MlflowClient.log_metric now logs metric timestamps with millisecond (as opposed to second) resolution (#1804, @ustcscgyer)
  • Fixed bugs when listing (#1800, @ahutterTA) and downloading (#1890, @jdlesage) artifacts stored in HDFS.
  • Fixed a bug preventing Kubernetes Projects from pushing to private Docker repositories (#1788, @dbczumar)
  • Fixed a bug preventing deploying Spark models to AzureML (#1769, @Ben-Epstein)
  • Fixed experiment id resolution in projects (#1715, @drewmcdonald)
  • Updated parallel coordinates plot to show all fields available in compared runs (#1753, @mateiz)
  • Streamlined docs for getting started with hosted MLflow (#1834, #1785, #1860 @smurching)

Small bug fixes and doc updates (#1848, @pingsutw; #1868, @iver56; #1787, @apurvakoti; #1741, #1737, @apurva-koti; #1876, #1861, #1852, #1801, #1754, #1726, #1780, #1807 @smurching; #1859, #1858, #1851, @tomasatdatabricks; #1841, @ankitmathur-db; #1744, #1746, #1751, @mateiz; #1821, #1730, @dbczumar; #1727, cfmcgrady; #1716, @axsaucedo; #1714, @fhoering; #1405, @ancasarb; #1502, @jimthompson5802; #1720, jke-zq; #1871, @mehdi254; #1782, @stbof)

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