ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Ray distributes training, tuning and serving across machines with barely any code change — and underpins a good chunk of modern LLM infrastructure.
From the README
- Data: Scalable Datasets for ML
- Train: Distributed Training
- Tune: Scalable Hyperparameter Tuning
- RLlib: Scalable Reinforcement Learning
- Serve: Scalable and Programmable Serving
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
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Free · no card · unsubscribe anytimeRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
ray has 43.7k stars on GitHub. It has been forked 8k times. ray is written mainly in Python. It has been in active development since 2016. ray is available under the Apache-2.0 license. Its main topics are data-science, deep-learning, deployment, distributed.
Read the full guideWhat is ray?
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Is ray open source?
ray is an open-source project. It is released under the Apache-2.0 license.
Is ray free?
Yes. ray is free and open source — you can use, modify and self-host it.
What license does ray use?
ray is available under the Apache-2.0 license.
What language is ray written in?
ray is written mainly in Python.
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