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Fully managed machine learning service to build, train, and deploy ML models at scale
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Scale Python and AI workloads from laptop to cluster effortlessly
Ray provides a unified runtime and libraries to scale data processing, training, hyperparameter tuning, reinforcement learning, and model serving across any infrastructure, from a single machine to large clusters.
When teams consider Ray, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Distributed Hyperparameter Tuning
Find optimal model parameters across hundreds of CPUs in minutes using Ray Tune.
Large‑Scale Data Preprocessing
Process terabytes of data efficiently with Ray Data pipelines on a cluster.
Real‑Time Model Serving
Deploy scalable, low‑latency inference services using Ray Serve.
Reinforcement Learning at Scale
Run parallel RL simulations with RLlib to accelerate research cycles.
Ray’s core APIs are Python‑first; other languages can interact via client libraries or RPC.
Install with `pip install ray`; nightly wheels are available via the official installation page.
Yes, Ray includes native support for deploying clusters on Kubernetes.
Ray ships with the Ray Dashboard and a Distributed Debugger for real‑time observability.
Ray is open source and can be used in commercial projects without licensing fees.
Project at a glance
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