Effortlessly scale your most complex workloads
Modern workloads like deep learning and hyperparameter tuning are compute-intensive, and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes.
Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray.
Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms.
Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework.
Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO.
General Python apps
Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core.
Scale data loading, writing, conversions, and transformations in Python with Ray Datasets.
Powered by Ray
Companies of all sizes and stripes are scaling their most challenging problems with Ray. Recommendation engines, ecosystem restoration, large-scale earth science, automated traffic monitoring, flying boats — these are just a few of the many use cases that engineers, scientists and researchers like you are tackling with Ray.
Uber consolidated and optimized their end-to-end deep learning workflows by using Ray as the distributed backend for their machine learning platform. Ray's flexibility, extensibility and low maintenance overhead made Ray a clear winner over alternatives.
O'Reilly Learning Ray Book
Get your free copy of early release chapters of Learning Ray, the first and only comprehensive book on Ray and its ecosystem, authored by members on the Ray engineering team.
Supercharge your Ray journey with Anyscale
Anyscale is a managed cloud offering — from the creators of the Ray project — to create, run and manage your Ray workload. If you or your organization prefers the speed and convenience of a managed service over self-managing clusters and the infra they live on, this might be for you.