Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Distributed Task Queue (development branch)
A hyperparameter optimization framework
Modin: Scale your Pandas workflows by changing a single line of code
Build, Manage and Deploy AI/ML Systems
Real-time monitor and web admin for Celery distributed task queue
Python Stream Processing
Asynchronous Python framework for event-driven services. A thin client for Kafka, RabbitMQ, NATS, Redis and MQTT with full access to native broker features, plus AsyncAPI docs, in-memory tests and observability out of the box.
A fast and reliable background task processing library for Python 3.
Superduper: End-to-end framework for building custom AI applications and agents.
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
Accelerated deep learning R&D
Distributed task queue with full async support
A distributed task scheduler for Dask
A library for event sourcing in Python.
PySpark + Scikit-learn = Sparkit-learn
InterPlanetary Wayback: A distributed and persistent archive replay system using IPFS
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
A web front-end for password cracking and analytics
Maze Applied Reinforcement Learning Framework
Stream Processing Made Easy
Distributed Automation for XNAT
Conflict-free merge for DataFrames, JSON, ML models & distributed agents — powered by CRDTs. The first merge library where every operation is mathematically guaranteed to converge.