xgboost_ray-0.0.5

• Added distributed callbacks called before/after train/data loading (#71)
• Improved fault tolerance testing and benchmarking (#72)
• Placement group fixes (#74)
• Improved warnings/errors when using incompatible APIs (#76, #82, #84)
• Enhanced compatibility with XGBoost 0.90 (legacy) and XGBoost 1.4 (#85, #90)
• Better testing (#72, #87)
• Minor bug/API fixes (#78, #83, #89)

xgboost_ray-0.0.4

• Add GCS support (Petastorm) (#63)
• Enforce labels are set for train/evaluation data (#64)
• Re-factor data loading structure, making it easier to add or change data loading backends (#66)
• Distributed and locality-aware data loading for Modin dataframes (#67)
• Documentation cleanup (#68)
• Fix RayDeviceQuantileDMatrix usage (#69)

xgboost_ray-0.0.3

• Added Petastorm integration (#46)
• Improved Tune integration (#54, #55)
• Fixed and improved tests (#40, #42, #47, #50, #52, #56, #60)
• Compatibility with Ray client (#57)
• Improved fault tolerance handling (#59)

xgboost_ray-0.0.2

Fix compatibility with python 3.8

xgboost_ray-0.0.1

Initial version of xgboost on ray, featuring:

• Distributed training and predict support, tested on clusters of up to 600 nodes
• Fault tolerance: Restarting the whole run from latest checkpoint if a node fails
• Fault tolerance: Automatic scaling down/up when nodes die/become available again
• Data loading from various sources (CSV, Parquet, Modin dataframes, Ray MLDataset, pandas, numpy)
• Seamless integration with Ray Tune
• Initial Ray placement group support