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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

Parameter server architecture

In this section, we will dive into the system architecture of the parameter server paradigm. The domain knowledge requirements for this section are as follows:

  • A Master/Worker architecture in distributed systems
  • Client/Server communication

The parameter server architecture mainly consists of two roles: parameter server and worker. The parameter server can be regarded as the master node in the traditional Master/Worker architecture.

Workers are the computer nodes or GPUs that are responsible for model training. We split the total training data among all the workers. Each worker trains their local model with the training data partition that's been assigned to it.

The duties of parameter server are twofold:

  • Aggregate model updates from all the workers.
  • Update the model parameters held on the parameter server.

The following diagram depicts a simplified parameter server architecture with two workers and one...

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