Xgboost-ray

- 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!

- 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)

- 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)

- 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)

- 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)

Fix compatibility with python 3.8