Overview
Ray is an open-source AI compute engine that originated at UC Berkeley’s RISELab and is now developed by Anyscale. It provides a unified task- and actor-based runtime that can scale from a laptop to thousands of GPUs or heterogeneous CPU/GPU clusters. Developers can build distributed applications in pure Python while Ray handles scheduling, failure recovery and resource management under the hood.
Key Capabilities
- Ray Core – task & actor primitives for parallel and distributed Python.
- Ray Data – distributed data preprocessing pipeline for structured & unstructured data.
- Ray Train – simple APIs to run distributed training for deep-learning frameworks.
- Ray Tune – scalable hyper-parameter tuning with many built-in search algorithms.
- Ray Serve – production-grade model-serving layer with autoscaling and fractional GPU sharing.
- RLlib – high-performance reinforcement-learning library.
Together, these components unify data ingest, model training, hyper-parameter search, inference, and reinforcement learning on a single, elastic runtime, making Ray a full-stack solution for modern AI workloads.
