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

llm-applications

A comprehensive guide to building RAG-based LLM applications for production.

ray-llm

RayLLM - LLMs on Ray

kuberay

A toolkit to run Ray applications on Kubernetes

llmperf

LLMPerf is a library for validating and benchmarking LLMs

tune-sklearn

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

llmperf-leaderboard

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.

ray_lightning

Pytorch Lightning Distributed Accelerators using 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.

mobius

Mobius is an AI infrastructure platform for distributed online learning, including online sample processing, training and serving.

enhancements

Tracking Ray Enhancement Proposals

lightgbm_ray

LightGBM on Ray

kuberay-helm

Helm charts for the KubeRay project

pygloo

Pygloo provides Python bindings for Gloo.

docu-mentor

serve_config_examples

ray-open-ports-checker

Tool to quickly check and see if your Ray cluster is exposed to untrusted clients

rayci

serve_workloads

travis-tracker-v2

issues-to-airtable

llmval-legacy

dind-buildkite-plugin

A Buildkite Plugin to enable Docker-in-Docker

images

Host images

ray-data-user-testing

The repo to study Ray Data CUJ

test_dag