Scaling Python with Ray

Spis treści ebooka

• Foreword
• Preface
  • What You Will Learn
  • A Note on Responsibility
  • Conventions Used in This Book
  • License
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • From Holden
    • From Boris
• 1. What Is Ray, and Where Does It Fit?
  • Why Do You Need Ray?
  • Where Can You Run Ray?
  • Running Your Code with Ray
  • Where Does It Fit in the Ecosystem?
    • Big Data / Scalable DataFrames
    • Machine Learning
    • Workflow Scheduling
    • Streaming
    • Interactive
  • What Ray Is Not
  • Conclusion
• 2. Getting Started with Ray (Locally)
  • Installation
    • Installing for x86 and M1 ARM
    • Installing (from Source) for ARM
  • Hello Worlds
    • Ray Remote (Task/Futures) Hello World
      • Sleepy task
      • Nested and chained tasks
    • Data Hello World
    • Actor Hello World
  • Conclusion
• 3. Remote Functions
  • Essentials of Ray Remote Functions
  • Composition of Remote Ray Functions
  • Ray Remote Best Practices
  • Bringing It Together with an Example
  • Conclusion
• 4. Remote Actors
  • Understanding the Actor Model
  • Creating a Basic Ray Remote Actor
  • Implementing the Actors Persistence
  • Scaling Ray Remote Actors
  • Ray Remote Actors Best Practices
  • Conclusion
• 5. Ray Design Details
  • Fault Tolerance
  • Ray Objects
  • Serialization/Pickling
    • cloudpickle
    • Apache Arrow
  • Resources / Vertical Scaling
  • Autoscaler
  • Placement Groups: Organizing Your Tasks and Actors
  • Namespaces
  • Managing Dependencies with Runtime Environments
  • Deploying Ray Applications with the Ray Job API
  • Conclusion
• 6. Implementing Streaming Applications
  • Apache Kafka
    • Basic Kafka Concepts
    • Kafka APIs
  • Using Kafka with Ray
  • Scaling Our Implementation
  • Building Stream-Processing Applications with Ray
    • Key-Based Approach
    • Key-Independent Approach
  • Going Beyond Kafka
  • Conclusion
• 7. Implementing Microservices
  • Understanding Microservice Architecture in Ray
    • Deployment
    • Additional Deployment Capabilities
      • Adding parameters to annotations
      • Improving scalability with resource replicas
      • Resource requirements for deployments
      • Implementing request routing with FastAPI
    • Deployment Composition
  • Using Ray Serve for Model Serving
    • Simple Model Service Example
    • Considerations for Model-Serving Implementations
    • Speculative Model Serving Using the Ray Microservice Framework
  • Conclusion
• 8. Ray Workflows
  • What Is Ray Workflows?
  • How Is It Different from Other Solutions?
  • Ray Workflows Features
    • What Are the Main Features?
    • Workflow Primitives
  • Working with Basic Workflow Concepts
    • Workflows, Steps, and Objects
    • Dynamic Workflows
    • Virtual Actors
  • Workflows in Real Life
    • Building Workflows
    • Managing Workflows
    • Building a Dynamic Workflow
    • Building Workflows with Conditional Steps
    • Handling Exceptions
    • Handling Durability Guarantees
    • Extending Dynamic Workflows with Virtual Actors
    • Integrating Workflows with Other Ray Primitives
    • Triggering Workflows (Connecting to Events)
    • Working with Workflow Metadata
  • Conclusion
• 9. Advanced Data with Ray
  • Creating and Saving Ray Datasets
  • Using Ray Datasets with Different Tools
  • Using Tools on Ray Datasets
    • pandas-like DataFrames with Dask
    • Indexing
    • Shuffles
      • Rolling windows and map_overlap
      • Aggregations
      • Full shuffles and partitioning
      • Partitioning
    • Embarrassingly Parallel Operations
    • Working with Multiple DataFrames
      • Multi-DataFrame internals
      • Missing functionality
    • What Does Not Work
    • Whats Slower
    • Handling Recursive Algorithms
    • What Other Functions Are Different
    • pandas-like DataFrames with Modin
    • Big Data with Spark
    • Working with Local Tools
  • Using Built-in Ray Dataset Operations
  • Implementing Ray Datasets
  • Conclusion
• 10. How Ray Powers Machine Learning
  • Using scikit-learn with Ray
  • Using Boosting Algorithms with Ray
    • Using XGBoost
    • Using LightGBM
  • Using PyTorch with Ray
  • Reinforcement Learning with Ray
  • Hyperparameter Tuning with Ray
  • Conclusion
• 11. Using GPUs and Accelerators with Ray
  • What Are GPUs Good At?
  • The Building Blocks
  • Higher-Level Libraries
  • Acquiring and Releasing GPU and Accelerator Resources
  • Rays ML Libraries
  • Autoscaler with GPUs and Accelerators
  • CPU Fallback as a Design Pattern
  • Other (Non-GPU) Accelerators
  • Conclusion
• 12. Ray in the Enterprise
  • Ray Dependency Security Issues
  • Interacting with the Existing Tools
  • Using Ray with CI/CD Tools
  • Authentication with Ray
  • Multitenancy on Ray
  • Credentials for Data Sources
  • Permanent Versus Ephemeral Clusters
    • Ephemeral Clusters
    • Permanent Clusters
  • Monitoring
  • Instrumenting Your Code with Ray Metrics
  • Wrapping Custom Programs with Ray
  • Conclusion
• A. Space Beaver Case Study: Actors, Kubernetes, and More
  • High-Level Design
  • Implementation
    • Outbound Mail Client
    • Shared Actor Patterns and Utilities
    • Mail Server Actor
    • Satellite Actor
    • User Actor
    • SMS Actor and Serve Implementation
  • Testing
  • Deployment
  • Conclusion
• B. Installing and Deploying Ray
  • Installing Ray Locally
  • Using Ray Docker Images
  • Using Ray Clusters
    • Installing Ray on AWS
    • Installing Ray on IBM Cloud
    • Installing Ray on Kubernetes
    • Installing Ray on a kind Cluster
    • Using ray up
    • Using the Ray Kubernetes Operator
    • Installing Ray on OpenShift
  • Conclusion
• C. Debugging with Ray
  • General Debugging Tips with Ray
  • Serialization Errors
  • Local Debugging with Ray Local
  • Remote Debugging
    • Rays Integrated Debugger (via Pdb)
    • Other Tools
      • PyCharm
      • Python profilers
  • Ray and Container Exit Codes
  • Ray Logs
  • Container Errors
  • Native Errors
  • Conclusion
• Index

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