Ray version 1.9 has been released

Ray version 1.9 has been released! The beta version of Ray Train includes usability improvements for distributed PyTorch training and checkpoint management, support for Ray Client, and an integration with Ray Datasets for distributed data ingest. Ray Datasets now supports groupby and aggregations, including multi-column/multi-lambda aggregations. Docker images for multiple CUDA versions are also available, allowing users to specify a `-cuXXX` suffix to pick a specific version. A new Ray Job Submission server + CLI & SDK clients is being launched, enabling users to package, deploy, and manage their Ray application as Jobs, which can be submitted by a Job manager of their choice.