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:
- 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.
Monitor and debug Ray applications and clusters using the Ray
dashboard.
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.
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 - Ownership: a distributed futures system for fine-grained
tasks - RLlib paper
- Tune paper
Older documents:
Getting Involved
Platform Purpose Estimated Support Level
Response Time
Discourse Forum For discussions about < 1 day Community
development and questions
about usage.
GitHub Issues For reporting bugs and < 2 days Ray OSS Team
filing feature requests.
Slack For collaborating with other < 2 days Community
Ray users.
StackOverflow For asking questions about 3-5 days Community
how to use Ray.
Meetup Group For learning about Ray Monthly Ray DevRel
projects and best practices.