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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

Troubleshooting accelerator performance

Before we can analyze our GPU performance, we need to understand generally how to debug and analyze performance on our training platform. SageMaker has some really nice solutions for this. First, all of your logs are sent to Amazon CloudWatch, another AWS service that can help you monitor your job performance. Each node in your cluster will have a full dedicated log stream, and you can read that log stream to view your overall training environment, how SageMaker runs your job, what status your job is in, and all of the logs your script emits. Everything you write to standard out, or print statements, is automatically captured and stored in CloudWatch. The first step to debugging your code is to take a look at the logs and figure out what really went wrong.

Once you know what’s wrong in your script, you’ll probably want to quickly fix it and get it back online, right? That’s why we introduced managed warm pools on SageMaker...

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