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Distributed Machine Learning with Python

You're reading from   Distributed Machine Learning with Python Accelerating model training and serving with distributed systems

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801815697
Length 284 pages
Edition 1st Edition
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Author (1):
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Guanhua Wang Guanhua Wang
Author Profile Icon Guanhua Wang
Guanhua Wang
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1 – Data Parallelism
2. Chapter 1: Splitting Input Data FREE CHAPTER 3. Chapter 2: Parameter Server and All-Reduce 4. Chapter 3: Building a Data Parallel Training and Serving Pipeline 5. Chapter 4: Bottlenecks and Solutions 6. Section 2 – Model Parallelism
7. Chapter 5: Splitting the Model 8. Chapter 6: Pipeline Input and Layer Split 9. Chapter 7: Implementing Model Parallel Training and Serving Workflows 10. Chapter 8: Achieving Higher Throughput and Lower Latency 11. Section 3 – Advanced Parallelism Paradigms
12. Chapter 9: A Hybrid of Data and Model Parallelism 13. Chapter 10: Federated Learning and Edge Devices 14. Chapter 11: Elastic Model Training and Serving 15. Chapter 12: Advanced Techniques for Further Speed-Ups 16. Other Books You May Enjoy

Section 2 – Model Parallelism

In this section, you will learn about vanilla mode parallelism and pipeline parallelism. You will also implement model-parallel training and an inference pipeline, and learn some further optimization schemes.

This section comprises the following chapters:

  • Chapter 5, Splitting the Model
  • Chapter 6, Pipeline Input and Layer Split
  • Chapter 7, Implementing Model Parallel Training and Serving Workflows
  • Chapter 8, Achieving Higher Throughput and Lower Latency
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