Raydp

Highlights
* Support Spark 3.3.x, 3.4.x, 3.5.x 382 395 397 411
* Dynamic allocation improvement 396
* Fault tolerance improvement 391
* Support setting custom actor owner when convert spark dataframe to ray dataset 376

Thanks pang-wu, minmingzhu, kira-lin, harborn, raviranak, KiranP-d11, gptbert, max-509, Deegue for their contributions to the release!

Highlights
* Support Ray 2.1 – 2.6
* Support Spark 3.1-3.4
* Fix logging: only logs from the driver is printed to the console
* Enable OneCCL as backend for TorchTrainer and TorchEstimator
* Add instructions and Dockerfiles for Ray on K8S
* Add resource affinity scheduling for RayDP executors 366. Thanks to pang-wu

Highlights
- Support Ray 2.1.0 - 2.2.0
- Support Spark 3.1 - 3.3
- XGBoostEstimator API 289
- Support converting Spark Dataframe to Ray dataset in a way that data can be recovered in case of failure. 249

Highlights
- Support Ray 1.9.0 - 2.1.0
- Support Spark 3.1 - 3.3
- Spark master node affinity
- Updated PyTorch and Tensorflow Estimator using new Ray Train API

Thanks KepingYan, kira-lin, pang-wu, carsonwang for their contributions to the release!

Highlights
- Support Ray 1.9.0 - 2.0.0
- Support Spark 3.1/3.2
- Hive support
- Ray placement group support
- Support multiple users running RayDP on the same node
- Support fractional resource scheduling
- Updated Estimator API using Ray Dataset and Ray Train
- Support custom Spark location by picking up $SPARK_HOME
- RayDP executor extra class path support
- Support data ownership transfer for conversion from Spark Dataframe to Ray Dataset
- New Colab tutorials

Thanks Bowen0729, carsonwang, hezhaozhao-git, jjyao, KepingYan, kira-lin, marin-ma, n1CkS4x0, pang-wu, wybryan, Yard1 for their contributions to the release!