Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Modin: Speed up your Pandas workflows by changing a single line of code
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Multi & Single Agent Reinforcement Learning for Traffic Signal Control Problem
Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.
An introductory tutorial about leveraging Ray core features for distributed patterns.
A toolkit to run Ray applications on Kubernetes
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.
A framework for easy prototyping of distributed reinforcement learning algorithms
Buggregator is a beautiful, lightweight debug server build on Laravel that helps you catch your smpt, sentry, var-dump, monolog, ray outputs. It runs without installation on multiple platforms.
A parallel framework for population-based multi-agent reinforcement learning.
Plane, 2D and 3D Ray objects for openFrameworks.It checks for the intersection of a ray with a segment, a sphere, a triangle, a plane, an ofPrimitive, an ofPolyline an with an ofMesh.
Debug your NodeJS & web code with Ray to fix problems faster
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial