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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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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

Single-machine multi-GPUs and multi-machine multi-GPUs

So far, we have discussed the main steps in data parallel training. In this section, we will explain two main types of hardware settings in data parallel training:

  • The first type is a single machine with multiple GPUs. In this setting, all the in-parallel training tasks can be launched using either a single process or multiple processes.
  • The second type is multiple machines with multiple GPUs. In this setting, we need to configure the network communication portals among all the machines. We also need to form a process group to synchronize both the cross-machine and cross-GPU training processes.

Single-machine multi-GPU

Compared to multi-machine multi-GPUs, the single-machine multiple-GPU setting is easier to set up. Before we discuss the implementation, let's check if the hardware configuration is good to go. Type the following command in the terminal:

$ nvidia-smi

If the NVIDIA driver and CUDA...

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