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ray

Model Serving & MLOps

A unified framework for scaling AI and Python applications from a laptop to a cluster

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Python Latest ray-2.55.1 · 1mo ago Security brief →

Features

  • Unified runtime that scales Python code across laptops, clusters, clouds, and Kubernetes
  • AI libraries for scalable ML datasets (Data), distributed training (Train), hyperparameter tuning (Tune), reinforcement learning (RLlib), and programmable serving (Serve)
  • Core abstractions: Tasks (stateless functions), Actors (stateful workers), and Objects (immutable shared values)

Security Response History

1 CVE
CVE Severity Disclosed Patched (this tool) vs Ecosystem Median
CVE-2023-4863 KEV high
CVSS 8.8
 2023-09-13 2026-02-18 2y 5mo / median 2y 4mo

Recent releases

View all 4 releases →
ray-2.55.1 Bug fix

Minor fixes and improvements.

Full changelog
  • Fixes SSH connectivity issue in the ray-llm image (#62625 / #62718).
  • Upgrade apt packages in slim base (#62666 / #62717).
View release on GitHub
ray-2.55.0 Breaking risk
Breaking changes
  • local_mode support removed
  • Legacy BlockList, locality_with_output, and callback API removed
  • PyArrow 9.0 compatibility checks removed
Security fixes
  • jackson-databind upgraded from 2.16.1 to 2.18.6 (GHSA-72hv-8253-57qq)
Notable features
  • DataSourceV2 API with scanner/reader framework and file partitioning
  • Kafka datasink with confluent-kafka migration and datetime offset support
  • 2-phase commit checkpointing with trie recovery
View release on GitHub
ray-2.54.1 Bug fix

Hanging issue detector disabled to resolve pipeline performance degradation from blocking API calls.

View release on GitHub
ray-2.54.0 New feature
Notable features
  • New cluster autoscaler enabled by default
  • Ray Data checkpointing support
  • Queue-based autoscaling for Ray Serve TaskConsumer
View release on GitHub

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Install & Platforms

Install via
pip

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