Effortlessly scale your most complex workloads

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving.

Trusted by leading AI and machine learning teams

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HEAR FROM LEADERS DEVELOPING THE NEXT GENERATION OF AI APPLICATIONS USING RAY

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Ya Xu

LinkedIn

LinkedIn’s AI Innovations: From Recommendations to Generative Innovations

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Albert Greenberg

Uber

AI/ML at Uber from Predictive to Generative Models

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Brian McClendon

Niantic

How Ray Transformed Niantic's Big Data Dilemma

Learn about Ray’s rich set of libraries and integrations

Deep learning

Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray.

Hyperparameter tuning

Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms.

Model serving

Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework.

Reinforcement learning

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.

Data processing

Scale data loading, writing, conversions, and transformations in Python with Ray Datasets.

Powered by Ray

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"We use Ray to run a number of AI workloads at Samsara. Since implementing the platform, we’ve been able to scale the training of our deep learning models to hundreds of millions of inputs, and accelerate deployment while cutting inference costs by 50% - we even use Ray to drive model evaluation on our IoT devices! Ray's performance, resource efficiency, and flexibility made it a great choice for supporting our evolving AI requirements."
Evan Welbourne
Head of AI and Data
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“We chose Ray as the unified compute backend for our machine learning and deep learning platform because it has allowed us to significantly improve performance and fault tolerance, while also reducing the complexity of our technology stack. Ray has brought significant value to our business, and has enabled us to rapidly pretrain, fine-tune and evaluate our LLMs.”
Min Cai
Distinguished Engineer
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"At OpenAI, we are tackling some of the world’s most complex and demanding computational problems. Ray powers our solutions to the thorniest of these problems and allows us to iterate at scale much faster than we could before. As an example, we use Ray to train our largest models, including ChatGPT."
Greg Brockman
Co-founder and President

O'Reilly Learning Ray Book

Get your free copy 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.