Ray

Ray is an open source framework for managing, executing, and optimizing distributed machine learning and AI compute workloads.

Ray is a Python-native framework designed to scale and distribute applications for machine learning, data processing, and AI model serving. It provides core primitives like tasks, actors, and objects to build distributed systems. Developers use it to manage heterogeneous compute resources, including CPUs and GPUs, across any scale. The framework integrates with common ML tools and frameworks to support end-to-end workflows from data processing to model training and inference. It specifically offers libraries for reinforcement learning and distributed model serving to handle complex AI tasks.

Ray at a glance

Best for: Software engineer, Researcher
Runs on: open source
Pricing: Free
Released: October 2016
Category: AI coding tool
Website: ray.io

Key features

Distributed Python task and actor execution
Multi-modal data processing for images, video, and audio
Distributed model training for foundation and time series models
Independent model scaling and serving with Ray Serve
Heterogeneous compute support for offline batch inference
Production-level reinforcement learning via Ray RLlib

Use it for

Scaling distributed Python applications
Training large-scale AI and machine learning models
Serving LLMs and other foundation models
Processing structured and unstructured data pipelines
Running reinforcement learning workflows

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