ray

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Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:

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Learn more about Ray AI Libraries_:

• Data_: Scalable Datasets for ML
• Train_: Distributed Training
• Tune_: Scalable Hyperparameter Tuning
• RLlib_: Scalable Reinforcement Learning
• Serve_: Scalable and Programmable Serving

Or more about Ray Core_ and its key abstractions:

• Tasks_: Stateless functions executed in the cluster.
• Actors_: Stateful worker processes created in the cluster.
• Objects_: Immutable values accessible across the cluster.

Learn more about Monitoring and Debugging:

• Monitor Ray apps and clusters with the Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>__.
• Debug Ray apps with the Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html>__.

Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations_.

Install Ray with: pip install ray. For nightly wheels, see the Installation page <https://docs.ray.io/en/latest/ray-overview/installation.html>__.

.. _Serve: https://docs.ray.io/en/latest/serve/index.html .. _Data: https://docs.ray.io/en/latest/data/data.html .. _Workflow: https://docs.ray.io/en/latest/workflows/ .. _Train: https://docs.ray.io/en/latest/train/train.html .. _Tune: https://docs.ray.io/en/latest/tune/index.html .. _RLlib: https://docs.ray.io/en/latest/rllib/index.html .. _ecosystem of community integrations: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html

Why Ray?

Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.

Ray is a unified way to scale Python and AI applications from a laptop to a cluster.

With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.

More Information

• Documentation_
• Ray Architecture whitepaper_
• Exoshuffle: large-scale data shuffle in Ray