Introduction to Ray
Ray is a fast and simple framework designed for building and running distributed applications. It integrates libraries that significantly accelerate the development of deep learning and reinforcement learning projects.
Key components include:
- Tune: Facilitates scalable hyperparameter search.
- RLlib: Provides scalable reinforcement learning capabilities.
- Distributed Training: Enables efficient distributed training workflows.
Getting Started
To begin using Ray, installation is straightforward. For the latest stable version, use pip:
pip install rayFor the most recent updates and nightly builds, refer to the official Installation page.
The complete source code for Ray is available on GitHub.
Key Features and Libraries
Ray's ecosystem is built to enhance machine learning development:
- Tune: A scalable hyperparameter search framework, optimized for deep learning and reinforcement learning. The documentation includes a quick start guide for using Tune to train a Convolutional Neural Network (CNN) with PyTorch.
- RLlib: An open-source library for reinforcement learning, offering high scalability and a unified API for diverse applications. A quick start guide demonstrates its use with the SimpleCorridor environment.
