Ray
By Anyscale
Ray is an open-source Python framework for scaling AI and Python applications from a laptop to a large distributed cluster, providing low-level distributed primitives plus higher-level libraries for machine learning training, tuning, and…
Definition
Ray is an open-source Python framework for scaling AI and Python applications from a laptop to a large distributed cluster, providing low-level distributed primitives plus higher-level libraries for machine learning training, tuning, and serving.
Overview
Ray was created at UC Berkeley's RISELab to solve a common problem: Python code that works fine on one machine often needs significant rewriting to run across many machines. Ray provides a simple API — mainly decorators like `@ray.remote` — that lets developers turn ordinary Python functions and classes into distributed tasks and actors without manually managing sockets, serialization, or scheduling. On top of this core distributed runtime, Ray ships a set of AI-focused libraries: Ray Train for distributed model training with PyTorch and TensorFlow, Ray Tune for scalable hyperparameter tuning, Ray Serve for model serving and online prediction, and RLlib for reinforcement learning. This makes Ray attractive for teams building end-to-end machine learning pipelines that need to scale from experimentation to production without switching frameworks. Ray is commercially backed by Anyscale, a company founded by Ray's original creators, which offers a managed cloud platform for running Ray clusters (see Anyscale). It is also used as the underlying compute engine inside other AI infrastructure tools and competes conceptually with Kubeflow for orchestrating distributed ML workloads, though Ray operates more at the Python-application layer.
Key Features
- Simple decorator-based API for turning Python functions and classes into distributed tasks and actors
- Ray Train for distributed model training across multi-GPU and multi-node clusters
- Ray Tune for scalable, automated hyperparameter search
- Ray Serve for deploying and scaling model inference endpoints
- RLlib library for distributed reinforcement learning
- Ray Data for distributed data loading and preprocessing pipelines
- Autoscaling cluster launcher for cloud providers and Kubernetes
- Fault-tolerant scheduling that recovers automatically from node failures