book

Learning Ray

by Max Pumperla, Edward Oakes, Richard Liaw

February 2023

Beginner

271 pages

7h 15m

English

O'Reilly Media, Inc.

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Foreword
Preface
Who Should Read This BookGoals of This BookNavigating This BookHow to Use the Code ExamplesConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. An Overview of Ray
What Is Ray?What Led to Ray?Ray’s Design PrinciplesThree Layers: Core, Libraries, and EcosystemA Distributed Computing FrameworkA Suite of Data Science LibrariesRay AIR and the Data Science WorkflowData Processing with Ray DatasetsModel TrainingHyperparameter TuningModel ServingA Growing EcosystemSummary
2. Getting Started with Ray Core
An Introduction to Ray CoreA First Example Using the Ray APIAn Overview of the Ray Core APIUnderstanding Ray System ComponentsScheduling and Executing Work on a NodeThe Head NodeDistributed Scheduling and ExecutionA Simple MapReduce Example with RayMapping and Shuffling Document DataReducing Word CountsSummary
3. Building Your First Distributed Application
Introducing Reinforcement LearningSetting Up a Simple Maze ProblemBuilding a SimulationTraining a Reinforcement Learning ModelBuilding a Distributed Ray AppRecapping RL TerminologySummary
4. Reinforcement Learning with Ray RLlib
An Overview of RLlibGetting Started with RLlibBuilding a Gym EnvironmentRunning the RLlib CLIUsing the RLlib Python APIConfiguring RLlib ExperimentsResource ConfigurationRollout Worker ConfigurationEnvironment ConfigurationWorking with RLlib EnvironmentsAn Overview of RLlib EnvironmentsWorking with Multiple AgentsWorking with Policy Servers and ClientsAdvanced ConceptsBuilding an Advanced EnvironmentApplying Curriculum LearningWorking with Offline DataOther Advanced TopicsSummary
5. Hyperparameter Optimization with Ray Tune
Tuning HyperparametersBuilding a Random Search Example with RayWhy Is HPO Hard?An Introduction to TuneHow Does Tune Work?Configuring and Running TuneMachine Learning with TuneUsing RLlib with TuneTuning Keras ModelsSummary
6. Data Processing with Ray
Ray DatasetsRay Datasets BasicsComputing Over Ray DatasetsDataset PipelinesExample: Training Copies of a Classifier in ParallelExternal Library IntegrationsBuilding an ML PipelineSummary
7. Distributed Training with Ray Train
The Basics of Distributed Model TrainingIntroduction to Ray Train by ExamplePredicting Big Tips in NYC Taxi RidesLoading, Preprocessing, and FeaturizationDefining a Deep Learning ModelDistributed Training with Ray TrainDistributed Batch InferenceMore on Trainers in Ray TrainMigrating to Ray Train with Minimal Code ChangesScaling Out TrainersPreprocessing with Ray TrainIntegrating Trainers with Ray TuneUsing Callbacks to Monitor TrainingSummary
8. Online Inference with Ray Serve
Key Characteristics of Online InferenceML Models Are Compute IntensiveML Models Aren’t Useful in IsolationAn Introduction to Ray ServeArchitectural OverviewDefining a Basic HTTP EndpointScaling and Resource AllocationRequest BatchingMultimodel Inference GraphsEnd-to-End Example: Building an NLP-Powered APIFetching Content and PreprocessingNLP ModelsHTTP Handling and Driver LogicPutting It All TogetherSummary

9. Ray Clusters
Manually Creating a Ray ClusterDeployment on KubernetesSetting Up Your First KubeRay ClusterInteracting with the KubeRay ClusterExposing KubeRayConfiguring KubeRayConfiguring Logging for KubeRayUsing the Ray Cluster LauncherConfiguring Your Ray ClusterUsing the Cluster Launcher CLIInteracting with a Ray ClusterWorking with Cloud ClustersAWSUsing Other Cloud ProvidersAutoscalingSummary
10. Getting Started with the Ray AI Runtime
Why Use AIR?Key AIR Concepts by ExampleRay Datasets and PreprocessorsTrainersTuners and CheckpointsBatch PredictorsDeploymentsWorkloads That Are Suited for AIRAIR Workload ExecutionAIR Memory ManagementAIR Failure ModelAutoscaling AIR WorkloadsSummary
11. Ray’s Ecosystem and Beyond
A Growing EcosystemData Loading and ProcessingModel TrainingModel ServingBuilding Custom IntegrationsAn Overview of Ray’s IntegrationsRay and Other SystemsDistributed Python FrameworksRay AIR and the Broader ML EcosystemHow to Integrate AIR into Your ML PlatformWhere to Go from Here?Summary
Index
About the Authors

Overview

Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.

Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.

Learn how to build your first distributed applications with Ray Core
Conduct hyperparameter optimization with Ray Tune
Use the Ray RLlib library for reinforcement learning
Manage distributed training with the Ray Train library
Use Ray to perform data processing with Ray Datasets
Learn how work with Ray Clusters and serve models with Ray Serve
Build end-to-end machine learning applications with Ray AIR

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