ray-project

ray

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 accelerating ML workloads.

llm-numbers

Numbers every LLM developer should know

kuberay

A toolkit to run Ray applications on Kubernetes

aviary

Ray Aviary - evaluate multiple LLMs easily

tune-sklearn

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

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.

langchain-ray

Examples on how to use LangChain and Ray

xgboost_ray

Distributed XGBoost on Ray

deltacat

A portable Pythonic Data Catalog API powered by Ray that brings exabyte-level scalability and fast, ACID-compliant, change-data-capture to your big data workloads.

rayfed

A multiple parties joint, distributed execution engine based on Ray, to help build your own federated learning frameworks in minutes.

lightgbm_ray

LightGBM on Ray

enhancements

Tracking Ray Enhancement Proposals

ray_beam_runner

Ray-based Apache Beam runner

llms-in-prod-workshop-2023

Deploy and Scale LLM-based applications

kuberay-helm

Helm charts for the KubeRay project

anyscale-berkeley-ai-hackathon

Ray and Anyscale for UC Berkeley AI Hackathon!

llm-application

serve_config_examples

Ray-Forward

Some resources about Ray Forward Meetup

ray-demos

Collection of demos build with Ray

rayci

rllib-contrib

scipy-ray-scalable-ml-tutorial-2023

serve_workloads

travis-tracker-v2

train-serve-primer

pyenv

Packaged semi-hermetic python environments for use in bazel.

pytest-sphinx

sphinx doctest plugin for pytest

ray-train-user-testing

test_dag