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Ray v0.9.0.dev0

  • What is Ray?

Overview of Ray

  • A Gentle Introduction to Ray
  • Installing Ray

Ray Core

  • Ray Core Walkthrough
  • Using Ray
  • Configuring Ray
  • Tutorial and Examples
    • Tips for first-time users
    • Parameter Server
    • Asynchronous Advantage Actor Critic (A3C)
    • Simple Parallel Model Selection
    • Learning to Play Pong
    • Batch L-BFGS
    • News Reader
    • Streaming MapReduce
    • Fault-Tolerant Fairseq Training
    • Tips for testing Ray programs
  • API and Package Reference

Ray Cluster

  • Distributed Ray Overview
  • Using the Ray Cluster Launcher
  • Cluster Autoscaling
  • Launching Cloud Clusters
  • Ray with Cluster Managers

Ray Serve

  • Ray Serve: Scalable and Programmable Serving
  • Key Concepts
  • Tutorials
  • Deploying Ray Serve
  • Advanced Topics, Configurations, & FAQ
  • Serve Architecture
  • Serve API Reference

Ray Tune

  • Tune: Scalable Hyperparameter Tuning
  • Key Concepts
  • User Guide & Configuring Tune
  • Tutorials & FAQ
  • Examples
  • Tune API Reference
  • Contributing to Tune

RLlib

  • RLlib: Scalable Reinforcement Learning
  • RLlib Table of Contents
  • RLlib Training APIs
  • RLlib Environments
  • RLlib Models, Preprocessors, and Action Distributions
  • RLlib Algorithms
  • RLlib Offline Datasets
  • RLlib Concepts and Custom Algorithms
  • RLlib Examples
  • RLlib Package Reference
  • Contributing to RLlib

Ray SGD

  • RaySGD: Distributed Training Wrappers
  • Distributed PyTorch
  • Distributed TensorFlow
  • Distributed Dataset
  • RaySGD API Documentation

Community Libraries

  • Distributed multiprocessing.Pool
  • Distributed Scikit-learn / Joblib
  • Parallel Iterators
  • Pandas on Ray
  • Dask on Ray
  • Mars on Ray

Ray Observability

  • Ray Monitoring with Prometheus

Contributing

  • Getting Involved / Contributing

Development and Ray Internals

  • Building Ray from Source
  • Ray Whitepaper
  • Debugging
  • Profiling for Ray Developers
  • Fault Tolerance
Theme by the Executable Book Project
Contents

Ray Tutorials and Examples¶

Get started with Ray, Tune, and RLlib with these notebooks that you can run online in Colab or Binder: Ray Tutorial Notebooks

../_images/pipeline.png

Tips for first-time users¶

../_images/param_actor.png

Parameter Server¶

../_images/a3c.png

Asynchronous Advantage Actor Critic (A3C)¶

../_images/hyperparameter.png

Simple Parallel Model Selection¶

../_images/pong.png

Learning to Play Pong¶

../_images/default.png

Batch L-BFGS¶

../_images/default.png

News Reader¶

../_images/default.png

Streaming MapReduce¶

../_images/default.png

Fault-Tolerant Fairseq Training¶

../_images/default.png

Tips for testing Ray programs¶

Configuring Ray Tips for first-time users

By The Ray Team
© Copyright 2019, The Ray Team.