Ray

This release adds major new features such as a utilization‑based autoscaler, Kafka datasource, and deployment topology visibility, while dropping Pydantic V1 support and removing deprecated APIs.

This release addresses CVE-2025-62593 by rejecting Sec-Fetch-* and other browser-specific headers in the dashboard's browser rejection logic.

Security update adds more robust handling for CVE-2025-62593 by checking extra browser-specific headers in dashboard rejection logic.

Ray 2.x introduces token authentication, extensive Iceberg enhancements, new expression capabilities, and numerous performance improvements, while dropping Python 3.9 support and adjusting several defaults that may require migration steps.

Added reuse of previous metadata for identical tensor list transfers using `nixl`.

This release introduces Ray Train v2 as the default, adds application‑level autoscaling to Ray Serve, and brings numerous new features and fixes to Ray Data, Train, Tune, and Serve.

Fixed a deadlock that occurred when cancelling stale requests on in-order actors.

This release adds major enhancements across Ray Data, Core, Train, Serve, and RLLib, including a new hash‑based shuffle, expression API, multi‑node LLM support, Direct Transport for GPU data, async inference in Serve, and extensive performance and memory optimizations.

There are no functional changes between version 2.49.2 and 2.49.1; this release only increments the patch version for out-of-band reasons.

This release addresses two bugs: GPU metrics missing in the Ray Dashboard and a regression in Ray Data handling large schemas.

This release adds extensive performance enhancements, new features such as JaxTrainer, async inference, and custom autoscaling, while fixing numerous bugs; it also introduces a few breaking changes that may require migration steps.

This release adds Delta Lake support, new collective operations, custom request routing, and offline policy evaluation, while also delivering numerous performance improvements and bug fixes across Ray Data, Train, Serve, Tune, and RLlib.

Ray 2.47.1 resolves a startup failure on macOS.

This release adds major LLM serving capabilities, custom routing, and numerous performance and observability improvements across Ray Data, Train, Serve, RLlib, and the Dashboard, while also introducing several breaking changes such as non‑blocking pipelines, stricter Serve name validation, and default uv run integration.

Ray 2.46 adds hash‑shuffle support for Data, vLLM v1 compatibility for Serve LLM, a new Train Grafana dashboard, and numerous performance and bug‑fix improvements.

Ray 2.45 adds configurable object store fallback, new cgraph transport options, a ClickHouse sink and several dataset API enhancements, along with numerous stability fixes and an upgraded LightGBM version.

There are no functional changes between version 2.44.1 and 2.44.0; this release only increments the patch version for out-of-band reasons.

Ray 2.45 introduces the beta Ray Compiled Graph for ultra‑low‑overhead task graphs, adds Iceberg write support and LLM enhancements to Ray Data, a state export API to Ray Train, and numerous performance and usability improvements across Serve, RLlib, and other libraries, while deprecating Ray Workflows and removing the ray.storage dependency.

Ray 2.43 adds alpha LLM support in Ray Data and Serve, introduces experimental Ray Train V2, and brings uv run integration along with numerous enhancements and bug fixes.

This release fixes an incorrect assertion in Ray Data.

This release adds audio/video reading to Ray Data, introduces new Train and Tune callbacks, stabilizes the DeploymentHandle API, and brings numerous bug fixes and performance enhancements across Ray libraries including Serve, RLlib, and Core.

This release adds extensive new features across Ray Data, Train, Tune, Serve, RLlib, and core components, upgrades key dependencies like Arrow, Dask, and Hudi, and includes numerous bug fixes and deprecations to prepare for future API changes.