Ray: Distributed AI compute framework
Unified framework for scaling AI and Python applications from laptops to clusters with distributed runtime.
Learn more about Ray
Ray is a distributed computing framework designed for scaling Python applications and AI workloads across multiple machines. The framework consists of a core distributed runtime that manages tasks, actors, and objects across a cluster, along with specialized libraries for data processing, training, hyperparameter tuning, reinforcement learning, and model serving. Ray uses a distributed object store and task scheduler to coordinate work across nodes, with support for both stateless functions (tasks) and stateful processes (actors). The framework is commonly used for distributed machine learning training, hyperparameter optimization, reinforcement learning experiments, and serving large language models at scale.
Unified Runtime
Single framework handles diverse workloads from data processing to model serving. Core abstractions of tasks, actors, and objects work consistently across all AI libraries.
Seamless Scaling
Same Python code runs unchanged from laptop to multi-node clusters. Built-in distributed object store and scheduler handle resource management automatically.
Specialized Libraries
Includes purpose-built libraries for data processing, distributed training, hyperparameter tuning, reinforcement learning, and model serving. Each library integrates with the core runtime for optimal performance.
Major Ray Data improvements including Kafka datasource support, enhanced Iceberg capabilities, and new utilization-based autoscaler.
- –Ray plans to drop support for Pydantic V1 starting version 2.56.0
- –Ray Data now has support for bounded reading from Kafka and improved Iceberg support
- –New utilization-based cluster autoscaler for Ray Data workloads
- –Add Kafka as a native datasource for data ingestion
- –Add Dataset.summary() API for quick dataset inspection
This release addresses a security vulnerability by fixing CVE-2025-62593 with improved browser header validation in the dashboard.
- –Fix for CVE-2025-62593: reject Sec-Fetch-* other browser-specific headers in dashboard browser rejection logic
This release improves security by strengthening browser rejection logic in the dashboard to better handle CVE-2025-62593.
- –More robust handling for CVE-2025-62593: test for more browser-specific headers in dashboard browser rejection logic
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