Scaling Python with Ray - Helion

ebook

Autor: Holden Karau, Boris Lublinsky
ISBN: 9781098118761
stron: 268, Format: ebook
Data wydania: 2022-11-29
Księgarnia: Helion

Cena książki: 194,65 zł (poprzednio: 226,34 zł)
Oszczędzasz: 14% (-31,69 zł)

Osoby, które kupiły tę książkę, wybierały także »

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.

In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness.

If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to:

Implement stateful applications with Ray actors
Build workflow management in Ray
Use Ray as a unified system for batch and stream processing
Apply advanced data processing with Ray
Build microservices with Ray
Implement reliable Ray applications

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Spis treści

Scaling Python with Ray eBook -- spis treści

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