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

Content preview from Learning Ray

Chapter 11. Ray’s Ecosystem and Beyond

Over the course of this book, you’ve seen many examples of Ray’s ecosystem. Now it’s time to take a more systematic approach and show you the full extent of integrations currently available for Ray. We do so by discussing this ecosystem as seen from Ray AIR so that we can discuss it in the context of a representative AIR workflow.

Clearly, we can’t give you concrete code examples for a majority of the libraries in Ray’s ecosystem. Instead, we have to be content with giving you another Ray AIR example showcasing some integrations and discussing what others are available and how to use them. Where appropriate, we’ll point you to more advanced resources to deepen your understanding.

Now that you know much more about Ray and its libraries, this chapter is also the right place to compare what Ray offers to similar systems. As you’ve seen, Ray’s ecosystem is quite complex, can be seen from different angles, and is used for different purposes. That means many aspects of Ray can be compared to other tools in the market.

We’ll also comment on how to integrate Ray into more complex workflows in existing ML platforms. To wrap things up, we’ll give you an idea how to continue your journey of learning Ray after finishing this book.

Note

The notebook for this chapter is available on GitHub.

A Growing Ecosystem

To give you a glimpse of Ray’s ecosystem by means of a concrete example,¹ we’re going to show you how to use Ray AIR with data and models from ...

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