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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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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

Distributed training on Amazon SageMaker

In the last chapter, we learned about SageMaker generally. Now, I’d like to dive into distributed training capabilities. We can break these up into four different categories: containers, orchestration, usability, and performance at scale.

As we learned in an earlier chapter, AWS offers deep learning (DL) containers that you can easily point to for your own scripts and code. These are strongly recommended as the first starting point for your project because all of the frameworks, versions, and libraries have been tested and integrated for you. This means that you can simply pick a container based on whichever DL framework you are using—for example, PyTorch or TensorFlow—and this container has already been tested on AWS and SageMaker. You can also select the GPU version of this container, and it will already have all of the NVIDIA libraries compiled and installed to run nicely on your GPUs. If you have your own container...

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