Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for simplifying ML compute:
Learn more about Ray AIR and its libraries:
Or more about Ray Core and its key abstractions:
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
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.
|Platform||Purpose||Estimated Response Time||Support Level|
|Discourse Forum||For discussions about development and questions about usage.||< 1 day||Community|
|GitHub Issues||For reporting bugs and filing feature requests.||< 2 days||Ray OSS Team|
|Slack||For collaborating with other Ray users.||< 2 days||Community|
|StackOverflow||For asking questions about how to use Ray.||3-5 days||Community|
|Meetup Group||For learning about Ray projects and best practices.||Monthly||Ray DevRel|
|For staying up-to-date on new features.||Daily||Ray DevRel|