ray-resources

Ray Documentation

This directory contains consolidated Ray documentation organized by topic.

Ray Core

• Getting Started - Introduction to Ray Core concepts
• Tasks - Using remote functions (tasks) with examples and patterns
• Actors - Using remote classes (actors) with concurrency and advanced features
• Objects - Working with Ray objects and serialization
• Scheduling - Resource management, placement groups, and scheduling strategies
• Fault Tolerance - Handling failures in tasks, actors, and objects
• API Reference - Complete API documentation

Ray Libraries

Ray Data

• Examples - Batch inference and data processing examples

Ray Train

• Examples - Distributed training examples for various frameworks

Ray Tune

• Guide - Hyperparameter tuning with Ray Tune
• Examples - Tuning examples for various ML frameworks

Ray Serve

• Guide - Scalable model serving
• Examples - Serving examples

Ray RLlib

• Guide - Reinforcement learning library

Cluster & Deployment

• Kubernetes - Deploying Ray on Kubernetes
• Job Submission - Submitting jobs to Ray clusters

Quick Links

• Main Index - Overview of all Ray features