Learning Ray

Spis treści ebooka

• Foreword
• Preface
  • Who Should Read This Book
  • Goals of This Book
  • Navigating This Book
  • How to Use the Code Examples
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. An Overview of Ray
  • What Is Ray?
    • What Led to Ray?
    • Rays Design Principles
      • Simplicity and abstraction
      • Flexibility and heterogeneity
      • Speed and scalability
    • Three Layers: Core, Libraries, and Ecosystem
  • A Distributed Computing Framework
  • A Suite of Data Science Libraries
    • Ray AIR and the Data Science Workflow
    • Data Processing with Ray Datasets
    • Model Training
      • Reinforcement learning with Ray RLlib
      • Distributed training with Ray Train
    • Hyperparameter Tuning
    • Model Serving
  • A Growing Ecosystem
  • Summary
• 2. Getting Started with Ray Core
  • An Introduction to Ray Core
    • A First Example Using the Ray API
      • Functions and remote Ray tasks
      • Using the object store with put and get
      • Using Rays wait function for nonblocking calls
      • Handling task dependencies
      • From classes to actors
    • An Overview of the Ray Core API
  • Understanding Ray System Components
    • Scheduling and Executing Work on a Node
    • The Head Node
    • Distributed Scheduling and Execution
  • A Simple MapReduce Example with Ray
    • Mapping and Shuffling Document Data
    • Reducing Word Counts
  • Summary
• 3. Building Your First Distributed Application
  • Introducing Reinforcement Learning
  • Setting Up a Simple Maze Problem
  • Building a Simulation
  • Training a Reinforcement Learning Model
  • Building a Distributed Ray App
  • Recapping RL Terminology
  • Summary
• 4. Reinforcement Learning with Ray RLlib
  • An Overview of RLlib
  • Getting Started with RLlib
    • Building a Gym Environment
    • Running the RLlib CLI
    • Using the RLlib Python API
      • Training RLlib algorithms
      • Saving, loading, and evaluating RLlib models
      • Computing actions
      • Accessing policy and model states
  • Configuring RLlib Experiments
    • Resource Configuration
    • Rollout Worker Configuration
    • Environment Configuration
  • Working with RLlib Environments
    • An Overview of RLlib Environments
    • Working with Multiple Agents
    • Working with Policy Servers and Clients
      • Defining a server
      • Defining a client
  • Advanced Concepts
    • Building an Advanced Environment
    • Applying Curriculum Learning
    • Working with Offline Data
    • Other Advanced Topics
  • Summary
• 5. Hyperparameter Optimization with Ray Tune
  • Tuning Hyperparameters
    • Building a Random Search Example with Ray
    • Why Is HPO Hard?
  • An Introduction to Tune
    • How Does Tune Work?
      • Search algorithms
      • Schedulers
    • Configuring and Running Tune
      • Specifying resources
      • Callbacks and metrics
      • Checkpoints, stopping, and resuming
      • Custom and conditional search spaces
  • Machine Learning with Tune
    • Using RLlib with Tune
    • Tuning Keras Models
  • Summary
• 6. Data Processing with Ray
  • Ray Datasets
    • Ray Datasets Basics
      • Creating a Ray Dataset
      • Reading from and writing to storage
      • Built-in transformations
      • Blocks and repartitioning
      • Schemas and data formats
    • Computing Over Ray Datasets
    • Dataset Pipelines
    • Example: Training Copies of a Classifier in Parallel
  • External Library Integrations
  • Building an ML Pipeline
  • Summary
• 7. Distributed Training with Ray Train
  • The Basics of Distributed Model Training
  • Introduction to Ray Train by Example
    • Predicting Big Tips in NYC Taxi Rides
    • Loading, Preprocessing, and Featurization
    • Defining a Deep Learning Model
    • Distributed Training with Ray Train
    • Distributed Batch Inference
  • More on Trainers in Ray Train
    • Migrating to Ray Train with Minimal Code Changes
    • Scaling Out Trainers
    • Preprocessing with Ray Train
    • Integrating Trainers with Ray Tune
    • Using Callbacks to Monitor Training
  • Summary
• 8. Online Inference with Ray Serve
  • Key Characteristics of Online Inference
    • ML Models Are Compute Intensive
    • ML Models Arent Useful in Isolation
  • An Introduction to Ray Serve
    • Architectural Overview
    • Defining a Basic HTTP Endpoint
    • Scaling and Resource Allocation
    • Request Batching
    • Multimodel Inference Graphs
      • Core feature: binding multiple deployments
      • Pattern 1: Pipelining
      • Pattern 2: Broadcasting
      • Pattern 3: Conditional logic
  • End-to-End Example: Building an NLP-Powered API
    • Fetching Content and Preprocessing
    • NLP Models
    • HTTP Handling and Driver Logic
    • Putting It All Together
  • Summary
• 9. Ray Clusters
  • Manually Creating a Ray Cluster
  • Deployment on Kubernetes
    • Setting Up Your First KubeRay Cluster
    • Interacting with the KubeRay Cluster
      • Running Ray programs with kubectl
      • Using the Ray Job Submission server
      • Ray Client
    • Exposing KubeRay
    • Configuring KubeRay
    • Configuring Logging for KubeRay
  • Using the Ray Cluster Launcher
    • Configuring Your Ray Cluster
    • Using the Cluster Launcher CLI
    • Interacting with a Ray Cluster
  • Working with Cloud Clusters
    • AWS
    • Using Other Cloud Providers
  • Autoscaling
  • Summary
• 10. Getting Started with the Ray AI Runtime
  • Why Use AIR?
  • Key AIR Concepts by Example
    • Ray Datasets and Preprocessors
    • Trainers
    • Tuners and Checkpoints
    • Batch Predictors
    • Deployments
  • Workloads That Are Suited for AIR
    • AIR Workload Execution
      • Stateless execution
      • Stateful execution
      • Composite workload execution
      • Online serving execution
    • AIR Memory Management
    • AIR Failure Model
    • Autoscaling AIR Workloads
  • Summary
• 11. Rays Ecosystem and Beyond
  • A Growing Ecosystem
    • Data Loading and Processing
    • Model Training
    • Model Serving
    • Building Custom Integrations
    • An Overview of Rays Integrations
  • Ray and Other Systems
    • Distributed Python Frameworks
    • Ray AIR and the Broader ML Ecosystem
    • How to Integrate AIR into Your ML Platform
  • Where to Go from Here?
  • Summary
• Index

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