MLflow v1.3 Release Notes
Release Date: 2019-09-30 // over 4 years ago-
MLflow 1.3.0 includes several major features and improvements:
๐ Features:
- ๐ฒ The Python client now supports logging & loading models using TensorFlow 2.0 (#1872, @juntai-zheng)
- ๐ 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 upset_experiment
andget_experiment_by_name
(#1775, @smurching) - โ New
mlflow.delete_run
,mlflow.delete_experiment
fluent APIs in the Python client(#1396, @MerelTheisenQB) - ๐ 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) - ๐ป HTML and geojson artifacts are now rendered in the run UI (#1838, @sim-san; #1803, @spadarian)
- ๐ Keras autologging support for
fit_generator
Keras API (#1757, @charnger) - ๐ณ MLflow models packaged as docker containers can be executed via Google Cloud Run (#1778, @ngallot)
- ๐ณ Artifact storage configurations are propagated to containers when executing docker-based MLflow projects locally (#1621, @nlaille)
- ๐ป The Python, Java, R clients and UI now retry HTTP requests on 429 (Too Many Requests) errors (#1846, #1851, #1858, #1859 @tomasatdatabricks; #1847, @smurching)
๐ Bug fixes and documentation updates:
- The R
mlflow_list_artifact
API no longer throws when listing artifacts for an empty run (#1862, @smurching) - ๐ Fixed a bug preventing running the MLflow server against an MS SQL database (#1758, @sifanLV)
- ๐ป 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 viamlflow.set_experiment
(#1820, @mcminnra). - ๐
MlflowClient.log_metric
now logs metric timestamps with millisecond (as opposed to second) resolution (#1804, @ustcscgyer) - ๐ Fixed bugs when listing (#1800, @ahutterTA) and downloading (#1890, @jdlesage) artifacts stored in HDFS.
- ๐ Fixed a bug preventing Kubernetes Projects from pushing to private Docker repositories (#1788, @dbczumar)
- ๐ Fixed a bug preventing deploying Spark models to AzureML (#1769, @Ben-Epstein)
- ๐ Fixed experiment id resolution in projects (#1715, @drewmcdonald)
- โก๏ธ Updated parallel coordinates plot to show all fields available in compared runs (#1753, @mateiz)
- ๐ Streamlined docs for getting started with hosted MLflow (#1834, #1785, #1860 @smurching)
โก๏ธ 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)