ray-project의 공개 GitHub 발자국

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Numbers every LLM developer should know

A toolkit to run Ray applications on Kubernetes

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

RayLLM - LLMs on Ray (Archived). Read README for more info.

LLMPerf is a library for validating and benchmarking LLMs

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

This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.

RayDP provides simple APIs for running Spark on Ray and integrating Spark with AI libraries.

A portable Multimodal Lakehouse powered by Ray that brings exabyte-level scalability and fast, ACID-compliant, change-data-capture to your ML and analytics workloads.

Examples on how to use LangChain and Ray

Pytorch Lightning Distributed Accelerators using Ray

Keeping track of RL experiments

Distributed XGBoost on Ray

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

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

Tracking Ray Enhancement Proposals

Helm charts for the KubeRay project

A minimal shared memory object store design

LightGBM on Ray

MLFlow Deployment Plugin for Ray Serve

Ray-based Apache Beam runner

Distributed ML Optimizer

Some resources about Ray Forward Meetup

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An experimental distributed execution engine

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Building Real-Time Inference Pipelines with Ray Serve

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A simple demonstration of embedding Ray in a Spark UDF. For Spark + AI Summit 2020.

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Artifacts intended to support the Ray Developer Community: SIGs, RFC overviews, and governance. We're very glad you're here! ✨

Ray repository sandbox

Scalable NLP model fine-tuning and batch inference with Ray and Anyscale

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

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Hackathon 2020! Max Archit Zhe

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Scaling multi-node multi-GPU workloads

Experimental genomics algorithms in Ray

Community Documents

Web-based 3D visualization of SUMO microsimulations using TraCI and three.js.

Queue for building arrow

Distributed Proofs with ZMKL and Ray

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Code samples for Ray Serve Meetup on 04/13/2022

Serializing primitive Python types in Arrow

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Plasma Object Store code for proposed import to Apache Arrow

Experimental scripts for deploying and using Ray

Polymer WebUI for Ray

A Redis HTTP interface with JSON output

HAProxy for bundling with Ray

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A set of runtime environment plugins which can be used by the public.

Website for Ray Summit 2022

Grid-style gang-scheduling and collective communication for Ray

Streaming processing engine based on ray platform.

Dashboard for Tracking Travis Python Test Result.

Code that is shared between Ray projects

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Chinese translation of Ray documentation. This may not be update to date.

Catapult

Libraries for Ray

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raydepsets build and release repo

Pandas object mirror

Modern HTTP benchmarking tool

The repo to study Ray Data CUJ

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Reference implementations of MLPerf™ training benchmarks

Feedback widget for Read the Docs documentation

A repository hosting data for air examples and notebooks

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This repo maintains the packages that are provided for tests.

Rules for building and handling Docker images with Bazel

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Prometheus Client Library for Modern C++

Build unofficial pickle5 wheels

Files for Ray project examples

Arrow build queue

RSS feed reader app build on Vue.js, Vuex, Element UI and PHP as backend.

Airspeed Velocity: A simple Python benchmarking tool with web-based reporting

Replicating redis

Experiments with sharding redis

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Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes

Pre-compiled python libraries with includes

A local scheduler and node manager for Ray