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Accelerate Model Training with PyTorch 2.X

You're reading from  Accelerate Model Training with PyTorch 2.X

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781805120100
Pages 230 pages
Edition 1st Edition
Languages
Author (1):
Maicon Melo Alves Maicon Melo Alves
Profile icon Maicon Melo Alves

Table of Contents (17) Chapters

Preface 1. Part 1: Paving the Way
2. Chapter 1: Deconstructing the Training Process 3. Chapter 2: Training Models Faster 4. Part 2: Going Faster
5. Chapter 3: Compiling the Model 6. Chapter 4: Using Specialized Libraries 7. Chapter 5: Building an Efficient Data Pipeline 8. Chapter 6: Simplifying the Model 9. Chapter 7: Adopting Mixed Precision 10. Part 3: Going Distributed
11. Chapter 8: Distributed Training at a Glance 12. Chapter 9: Training with Multiple CPUs 13. Chapter 10: Training with Multiple GPUs 14. Chapter 11: Training with Multiple Machines 15. Index 16. Other Books You May Enjoy

Quiz time!

Let’s review what we have learned in this chapter by answering a few questions. Initially, try to answer these questions without consulting the material.

Note

The answers to all these questions are available at https://github.com/PacktPublishing/Accelerate-Model-Training-with-PyTorch-2.X/blob/main/quiz/chapter08-answers.md.

Before starting the quiz, remember that this is not a test! This section aims to complement your learning process by revising and consolidating the content covered in this chapter.

Choose the correct option for the following questions.

  1. What are the two main reasons for distributing the training process?
    1. Reliability and performance improvement.
    2. Leak of memory and power consumption.
    3. Power consumption and performance improvement.
    4. Leak of memory and performance improvement.
  2. Which are the two main parallel strategies to distribute the training process?
    1. Model and data parallelism.
    2. Model and hardware parallelism.
    3. Hardware and data parallelism...
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