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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781804618257

Length 258 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

GPT/LLMs

Author (1):

Emily Webber

View More author details

Table of Contents (23) Chapters

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

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Containers and Accelerators on the Cloud

In this chapter, you’ll learn how to containerize your scripts and optimize them for accelerators on the cloud. We’ll learn about a range of accelerators for foundation models, including trade-offs around cost and performance across the entire machine learning lifecycle. You’ll learn about key aspects of Amazon SageMaker and AWS to train models on accelerators, optimize performance, and troubleshoot common issues. if you’re already familiar with containers and accelerators on AWS, feel free to skip this chapter.

In this chapter, we’re going to cover the following main topics: