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

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra.

GPU environment and cluster management with LLM support

A parallel framework for population-based multi-agent reinforcement learning.

A basic Ray Tracer that exploits numpy arrays and functions to work reasonably fast.

Framework for Multi-Agent Deep Reinforcement Learning in Poker

TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.

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

syftr is an agent optimizer that helps you find the best agentic workflows for your budget.

Examples on how to use LangChain and Ray

CamTools: Camera Tools for Computer Vision

Distributed Keras Engine, Make Keras faster with only one line of code.

⚡ ⚡ 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐

A practical fork of github/spec-kit with patterns & templates for building scalable multi-agent AI systems. Ships production-ready stacks faster with OpenAI Agents SDK, MCP, A2A, Kubernetes, Dapr, and Ray. It also explicitly treats specifications, architecture history, prompt history, tests, and automated evaluations as first‑class artifacts.

Serving Inside Pytorch