Ray

Ray is a unified Python framework for scaling AI and general compute workloads from a laptop to a cluster, pairing a distributed runtime core with a set of AI libraries.

Visit website ray.io

Its AI libraries cover Data (scalable datasets for ML), Train (distributed training), Tune (hyperparameter tuning), RLlib (reinforcement learning) and Serve (scalable, programmable model and LLM serving), while Ray Core provides the underlying abstractions of stateless tasks, stateful actors and immutable objects shared across a cluster.

The same code runs unchanged on a single laptop or a large cluster, on any cloud provider or on Kubernetes, and ships with a dashboard for monitoring running applications and a separate distributed debugger for troubleshooting them. A growing set of community integrations extends the ecosystem around it.

Python, Apache-2.0 licensed.

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