README

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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.
- 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.

 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:

- Ray paper
- Ray HotOS paper
- Ray Architecture v1 whitepaper

 Getting Involved

| 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 DevRel |

Top issues

Latest release

Highlights

 Ray Data shuffle and Parquet reads: In this Ray release, shuffle v2 is the default shuffle strategy for joins, aggregations (\66260). Parquet reads now size read tasks from the row-group metadata in each file's footer instead of estimating from file size, so read tasks are packed to an accurate byte budget (\65596, \65806, \65821).  
 Ray Serve rolling updates: We've added max_surge_percent, which starts replacement replicas before stopping old ones so a rolling update doesn't reduce serving capacity (\66267). A rolling update whose new replicas keep failing now stops at DEPLOY