v0.1.9

What's Changed

• [release] Bump to 0.1.9 for future development by @matthewdeng in #199
• Update README.md by @Yard1 in #200
• Mitigate finishing too quickly by @Yard1 in #203
• Adjust readme for Ray Docs by @Yard1 in #204
• Fail quickly on Windows by @amogkam in #209
• Fix test_num_parallel_tree for cutting edge CI by @Yard1 in #211
• Fix CI failing with xgboost==1.6.0 by @Yard1 in #212
• Adjust CI timeouts by @Yard1 in #213
• Fix sklearn docs throwing an error during build by @Yard1 in #214
• Fix wrap_evaluation_matrices with xgb master by @Yard1 in #215
• Remove code for legacy Ray versions by @krfricke in #217
• Remove support for deprecated ray.util.data.MLDataset by @amogkam in #218

Full Changelog: v0.1.8...v0.1.9

v0.1.8

• Fix "qid" being passed to legacy DMatrix (#193, #194)
• Update README (#197, #198)

xgboost_ray-0.1.7

• Ranking support (#189)
• Fix bad super() call in MLDataset DataSource (#184)
• CI fixes (#185, #187)

xgboost_ray-0.1.6

• Preserve column ordering in DMatrices (#170)
• Dependency management (#171, #173, #174, #175)
• CI improvements (#169)
• Documentation/example fixes (#178, #180)
• Validate kwargs correctly (#172)
• Main: Fix compatibility with XGBoost >= 1.5.0 (#179)

xgboost_ray-0.1.5

• Require up-to-date Ray for compatibility (#164)
• Lazily read env variables (#166)
• Compatibility with XGBoost >= 1.6.0 (#167)

xgboost_ray-0.1.4

• Add API annotations (#158)
• Adjust to Ray API changes (#161)
• Fix Dask data loading issues (#162)
• Import/dependency fixes, removing [default] and adding xgboost as hard dependency again (#156, #159)

xgboost_ray-0.1.3

• Support for more data sources: Ray datasets and general improvements (#139, #140, #141, #145)
• Placement group fixes (#136, #143)
• Usability improvements (#148, #149)
• Various minor fixes/updates (#137, #144, #150, #154)
• xgboost is now an extra dependency (and might have to be installed separately) (#151)

xgboost_ray-0.1.2

• Improvements in the SKlearn API (#125, #126)
• User user warnings instead of logger warnings (#128)
• Testing fixes (#129)
• Fix backwards compatibility with placement groups (#130)
• Improve batching algorithm (#132)

xgboost_ray-0.1.1

• Fix issues with multi class prediction (#110, #123)
• Fix bugs and CI (#113, #114, #115, #118, #120)
• Improve compatibility with external libraries (#108, #120, #121, #122)
• Add Scitkit-learn interface (#111)

Special shout out to @Yard1 for his contributions! Thanks!

xgboost_ray-0.1.0

• Improve fault tolerance (#94)
• Reduce cross-node data transfer (#100)
• Improve Ray Tune integration (#102, #103)
• Improve error reporting for failed actors (#104)
• Add support for Dask dataframes (Dask on Ray) (#99)
• Improve documentation (#105)