Ray 2.7 features major stability improvements to Ray AI Libraries and KubeRay and introduces RayLLM

The latest release of Ray 2.7 brings significant stability improvements, enhancements to libraries and KubeRay for Kubernetes, and introduces new features such as RayLLM for serving open-source large language models (LLMs) with Ray Serve. The update simplifies APIs in Ray Train for general availability, stabilizes and enhances Ray Serve and KubeRay, and adds support for various accelerator devices including TPUs, Trainium, and Inferentia. Additionally, Ray Data has improved performance with features such as zero-copy fusion for map operators and multithreaded file reading. The release also includes a unified DeploymentHandle API, gRPC ingress support, websocket support with FastAPI, streaming responses, batch requests, model multiplexing, and multi-app support. The update is part of Ray's efforts to simplify the number of concepts that users need to learn about and reduce friction for new machine learning practitioners to quickly adopt Ray Train for distributed training at scale.

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