Announcing Ray 2.0

Ray 2.0 is a major release that aims to make distributed computing scalable, unified, and open. The new Ray AI Runtime (AIR) simplifies the process of running machine learning workloads by aligning existing native ML libraries and integrating with popular ML frameworks in the community. Additionally, Ray now supports natively shuffling large amounts of data with the Ray Datasets library. Production support for Kubernetes is provided through KubeRay, which makes it easier to deploy Ray-based jobs and services on K8s. High-Availability for large-scale Ray Serve deployments is also introduced in this release. New observability tooling provides developers with visibility into the health and performance of their Ray workloads. Deployment Graph API simplifies building, testing, and deploying an inference graph of deployments. RLlib refactors its algorithms to follow simpler patterns and introduces new algorithms for offline reinforcement learning. Overall, Ray 2.0 aims to make distributed computing more accessible and efficient for ML practitioners and infrastructure groups.

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