Ray v2.0.0.dev0

What is Ray?

Overview of Ray

A Gentle Introduction to Ray
Community Integrations
Installing Ray

Ray Core

Ray Core Walkthrough
Using Ray
Configuring Ray
Ray Dashboard
Tutorial and Examples
Design patterns and anti-patterns
API and Package Reference

Multi-node Ray

Ray Cluster Overview
Ray Cluster Quick Start
Ray Deployment Guide
Config YAML and CLI Reference
Launching Cloud Clusters
- AWS Configurations
Ray Client
Ray with Cluster Managers

Ray Serve

Serve: Scalable and Programmable Serving
End-to-End Tutorial
Core API: Deployments
Calling Deployments via HTTP and Python
Deploying Ray Serve
Serving ML Models
Performance Tuning
Serve Architecture
Advanced Tutorials
Ray Serve FAQ
Serve API Reference

Ray Data

Datasets: Flexible Distributed Data Loading
Dataset Pipelines
Example: Large-scale ML Ingest
Dataset Tensor Support
Dataset API Reference
Dask on Ray
Mars on Ray
Modin (Pandas on Ray)
RayDP (Spark on Ray)

Ray Workflows

Workflows: Fast, Durable Application Flows
Workflow Basics
Workflow Management
Virtual Actors
API Comparisons
Workflows API Reference

Ray Tune

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

Ray RLlib

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

Ray SGD

RaySGD: Deep Learning on Ray
RaySGD User Guide
RaySGD Examples
RaySGD Architecture
RaySGD API
Migrating from Ray SGD v1
RaySGD v1: Distributed Training Wrappers

More Libraries

Distributed multiprocessing.Pool
Distributed Scikit-learn / Joblib
Distributed XGBoost on Ray
Distributed LightGBM on Ray
Distributed PyTorch Lightning Training on Ray
Ray Collective Communication Lib

Observability

Exporting Metrics
Ray Debugger
Logging
Tracing

Contributor Guide

Getting Involved / Contributing
Building Ray from Source
Testing Autoscaling Locally
Ray Whitepapers
Debugging (internal)
Profiling (internal)

Theme by the Executable Book Project

Contents

TuneGridSearchCV
TuneSearchCV

Scikit-Learn API (tune.sklearn)¶

TuneGridSearchCV¶

TuneSearchCV¶

Trial Schedulers (tune.schedulers) Stopping mechanisms (tune.stopper)

By The Ray Team
© Copyright 2021, The Ray Team.