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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

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801815697

Length 284 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Guanhua Wang

View More author details

Table of Contents (17) Chapters

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

Case study of Mesh-TensorFlow

We discussed Megatron-LM in detail due to its popularity. Now, we will briefly discuss Mesh-TensorFlow in this section.

This approach is quite easy to understand. Basically, Mesh-TensorFlow combines data and model parallelism by allowing users to configure two dimensions—that is, batch and model dimensions—as shown in the following diagram:

Figure 9.13 – Mesh-TensorFlow's two-dimensional (2D) parallelism

As shown in the preceding diagram, mesh-tensorflow allows users to set parallelism levels in two dimensions, as follows:

Batch dimension: How many concurrent batches to train (data parallelism)
Model dimension: How many splits over the model (model parallelism)

As shown in Figure 9.13, let's assume the user sets both batch dimension as 2 and model dimension as 2. This means that we use two GPUs to do model-parallel training, and we have two groups of this two-GPU model parallelism...

The rest of the chapter is locked

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Authors (1)

Wang

Wang

Wallace Wang has written dozens of computer books over the years, including Microsoft Office for Dummies and Beginning Programming for Dummies, along with Macintosh and the iPhone books such as macOS Programming for Absolute Beginners, Beginning iPhone Development with Swift 5, Pro iPhone Development with Swift 5, and Beginning ARKit for iPhone and iPad. When he's not helping people discover the fascinating world of programming, he performs stand-up comedy and appears on two radio shows on KNSJ in San Diego called Notes From the Underground and Laugh-In Your Face Radio. In his free time, Wallace also writes a screenwriting blog called The 15 Minute Movie Method and a blog about the latest cat news on the Internet called Cat Daily News.

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