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:
Learn more about Ray AI Libraries:
Or more about Ray Core and its key abstractions:
Learn more about Monitoring and Debugging:
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.
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.
Older documents:
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 Twitter For staying up-to-date on new features. Daily Ray DevRelRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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