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Pretrain Vision and Large Language Models in Python

You're reading from   Pretrain Vision and Large Language Models in Python End-to-end techniques for building and deploying foundation models on AWS

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618257
Length 258 pages
Edition 1st Edition
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Concepts
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Author (1):
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Emily Webber Emily Webber
Author Profile Icon Emily Webber
Emily Webber
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Before Pretraining
2. Chapter 1: An Introduction to Pretraining Foundation Models FREE CHAPTER 3. Chapter 2: Dataset Preparation: Part One 4. Chapter 3: Model Preparation 5. Part 2: Configure Your Environment
6. Chapter 4: Containers and Accelerators on the Cloud 7. Chapter 5: Distribution Fundamentals 8. Chapter 6: Dataset Preparation: Part Two, the Data Loader 9. Part 3: Train Your Model
10. Chapter 7: Finding the Right Hyperparameters 11. Chapter 8: Large-Scale Training on SageMaker 12. Chapter 9: Advanced Training Concepts 13. Part 4: Evaluate Your Model
14. Chapter 10: Fine-Tuning and Evaluating 15. Chapter 11: Detecting, Mitigating, and Monitoring Bias 16. Chapter 12: How to Deploy Your Model 17. Part 5: Deploy Your Model
18. Chapter 13: Prompt Engineering 19. Chapter 14: MLOps for Vision and Language 20. Chapter 15: Future Trends in Pretraining Foundation Models 21. Index 22. Other Books You May Enjoy

Advanced Training Concepts

In this chapter, we will cover advanced training concepts at scale, such as evaluating throughput, calculating model teraFLOPS (TFLOPS) per device, compiling, and using the scaling laws to determine the right length of training time. In the last chapter, you learned about how to do large-scale training on SageMaker, in general terms. In this chapter, you’ll learn about particularly complex and sophisticated techniques you can use to drive down the overall cost of your job. This lower cost directly translates to higher model performance because you can train for longer on the same budget.

We will cover the following topics in this chapter:

  • Evaluating and improving throughput with model TFLOPS
  • Using FlashAttention to speed up your training runs
  • Speeding up your jobs with compilation
  • Amazon SageMaker Training Compiler and Neo
  • Running compiled models on Amazon’s Trainium and Inferentia custom hardware
  • Solving for an...
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