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https://github.com/JiahaoYao/awesome-ray

Ray - A curated list of resources: https://github.com/ray-project/ray
https://github.com/JiahaoYao/awesome-ray

List: awesome-ray

Last synced: 16 days ago
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Ray - A curated list of resources: https://github.com/ray-project/ray

Host: GitHub
URL: https://github.com/JiahaoYao/awesome-ray
Owner: JiahaoYao
License: mit
Created: 2022-05-28T21:36:43.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-10-29T16:56:21.000Z (about 1 year ago)
Last Synced: 2024-12-02T12:02:22.136Z (19 days ago)
Size: 86.9 KB
Stars: 43
Watchers: 3
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ultimate-awesome - awesome-ray - Ray - A curated list of resources: (Other Lists / Monkey C Lists)

README

        
# Awesome RAY [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)[](https://github.com/ray-project/ray/)

![ray_logo](https://user-images.githubusercontent.com/20907377/175792492-a085fc63-3b5f-4804-8215-e9c45cb284aa.gif)

[Ray](https://github.com/ray-project/ray/) makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes.

This is a curated list of awesome RAY libraries, projects, and other resources. Contributions are welcome!

## Contents

- [Libraries](#libraries)

- [Models and Projects](#models-and-projects)

- [Videos](#videos)

- [Papers](#papers)

- [Tutorials and Blog Posts](#tutorials-and-blog-posts)

- [Community](#community)



## Libraries



### New Libraries

This section contains libraries that are well-made and useful, but have not necessarily been battle-tested by a large userbase yet.



## Models and Projects

### Ray + LLM

- [FastChat](https://github.com/lm-sys/FastChat) Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality

- [LangChain-Ray](https://github.com/ray-project/langchain-ray) Examples on how to use LangChain and Ray

- [Aviary](https://github.com/ray-project/aviary) Ray Aviary - evaluate multiple LLMs easily

- [LLM-distributed-finetune](https://github.com/AdrianBZG/LLM-distributed-finetune) Finetuning Large Language Models Efficiently on a Distributed Cluster, Uses Ray AIR to orchestrate the training on multiple AWS GPU instances.

### Reinforcmenet Learning

- [muzero-general](https://github.com/werner-duvaud/muzero-general) - A commented and documented implementation of MuZero based on the Google DeepMind paper (Schrittwieser et al., Nov 2019) and the associated pseudocode.

- [rllib-torch-maddpg](https://github.com/Rohan138/rllib-torch-maddpg) - PyTorch implementation of MADDPG (Lowe et al.) in RLLib

- [MARLlib](https://github.com/Replicable-MARL/MARLlib) - a comprehensive Multi-Agent Reinforcement Learning algorithm library

### Ray + JAX / TPU

- [Swarm-jax](https://github.com/kingoflolz/swarm-jax) - Swarm training framework using Haiku + JAX + Ray for layer parallel transformer language models on unreliable, heterogeneous nodes

- [Alpa](https://github.com/alpa-projects/alpa) - Auto parallelization for large-scale neural networks using Jax, XLA, and Ray

### Ray + Database

- [Balsa](https://github.com/balsa-project/balsa#cluster-mode) Balsa is a learned SQL query optimizer. It tailor optimizes your SQL queries to find the best execution plans for your hardware and engine.

- [RaySQL](https://github.com/andygrove/ray-sql) Distributed SQL Query Engine in Python using Ray

- [Quokka](https://github.com/marsupialtail/quokka) Open source SQL engine in Python 

### Ray + X (integration)

- [prefect-ray](https://github.com/PrefectHQ/prefect-ray) Prefect integrations with Ray

- [xgboost_ray](https://github.com/ray-project/xgboost_ray) Distributed XGBoost on Ray

- [Ray-DeepSpeed-Inference](https://github.com/tchordia/ray-serve-deepspeed) Run deepspeed on ray serve

### Ray-Project

- [SkyPilot](https://github.com/skypilot-org/skypilot) a framework for easily running machine learning workloads on any cloud through a unified interface

- [Exoshuffle-CloudSort](https://github.com/exoshuffle/cloudsort) the winning entry of the [2022 CloudSort Benchmark](http://sortbenchmark.org/) in the Indy category.

### distributed computing

- [Fugue](https://github.com/fugue-project/fugue) a unified interface for distributed computing that lets users execute Python, pandas, and SQL code on Ray without rewrites.

- [Daft](https://github.com/Eventual-Inc/Daft) is a fast, Pythonic and scalable open-source dataframe library built for Python and Machine Learning workloads.

- [Flower](https://github.com/adap/flower)(flwr) is a framework for building federated learning systems. Uses Ray for scaling out experiments from desktop, single GPU rack, or multi-node GPU cluster.

- [Modin](https://github.com/modin-project/modin): Scale your pandas workflows by changing one line of code. Uses Ray for transparently scaling out to multiple nodes.

- [Volcano](https://github.com/volcano-sh/volcano) is a batch system built on Kubernetes. It provides a suite of mechanisms that are commonly required by many classes of batch & elastic workloads.

### Ray AIR

- [Ray on Azure ML](https://github.com/microsoft/ray-on-aml) Turning AML compute into Ray cluster

### Misc

- [AutoGluon](https://github.com/awslabs/autogluon) AutoML for Image, Text, and Tabular Data

- [Aws-samples](https://github.com/aws-samples/aws-samples-for-ray) Ray on Amazon SageMaker/EC2/EKS/EMR

- [KubeRay](https://github.com/ray-project/kuberay) A toolkit to run Ray applications on Kubernetes

- [ray-educational-materials](https://github.com/ray-project/ray-educational-materials) This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

- [Metaflow-Ray](https://github.com/outerbounds/metaflow-ray) An extension for Metaflow that enables seamless integration with Ray 



## Videos



### rllib 

- [Deep reinforcement learning at Riot Games by Ben Kasper](https://youtu.be/r6ErUh5sjXQ) - reinforcement learning for game development in production

## Papers

This section contains papers focused on Ray (e.g. RAY-based library whitepapers, research on RAY, etc). Papers implemented in RAY are listed in the [Models/Projects](#projects) section.



## Tutorials and Blog Posts

- [Programming in Ray: Tips for first-time users](https://rise.cs.berkeley.edu/blog/ray-tips-for-first-time-users)

- [Reddit post](https://news.ycombinator.com/item?id=27730807) 

- [Load PyTorch Models 340 Times Faster with Ray](https://medium.com/ibm-data-ai/how-to-load-pytorch-models-340-times-faster-with-ray-8be751a6944c)

## books 

- [Learning Ray](https://github.com/maxpumperla/learning_ray) Learning Ray - Flexible Distributed Python for Machine Learning

## course

- [RL course](https://github.com/anyscale/rl-course) Applied Reinforcement Learning with RLlib

- [MLops course](https://github.com/anyscale/mlops-course) MLops course

## cheatsheet

- [Ray design doc](https://docs.google.com/document/d/167rnnDFIVRhHhK4mznEIemOtj63IOhtIPvSYaPgI4Fg/edit#heading=h.5jeo2fupy3yv)



## Community

- [RAY Discussions](https://discuss.ray.io/)

- [Stack Overflow](https://stackoverflow.com/questions/tagged/ray)

- [Slack](https://forms.gle/9TSdDYUgxYs8SA9e8)

- [GitHub Issues](https://github.com/ray-project/ray/issues)

- [Meetup Group](https://www.meetup.com/Bay-Area-Ray-Meetup/)

- [Twitter](https://twitter.com/raydistributed)

## Contributing

Contributions welcome! Read the [contribution guidelines](contributing.md) first.