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.
Learn more about Monitoring and Debugging:
- Monitor Ray apps and clusters with the Ray Dashboard.
- Debug Ray apps with the Ray Distributed Debugger.
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.
- 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:
| 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 |
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.
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.