Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Transformers

You're reading from   Mastering Transformers The Journey from BERT to Large Language Models and Stable Diffusion

Arrow left icon
Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781837633784
Length 462 pages
Edition 2nd Edition
Languages
Tools
Concepts
Arrow right icon
Authors (2):
Arrow left icon
Savaş Yıldırım Savaş Yıldırım
Author Profile Icon Savaş Yıldırım
Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Author Profile Icon Meysam Asgari- Chenaghlu
Meysam Asgari- Chenaghlu
Arrow right icon
View More author details
Toc

Table of Contents (25) Chapters Close

Preface 1. Part 1: Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformers FREE CHAPTER 3. Chapter 2: A Hands-On Introduction to the Subject 4. Part 2: Transformer Models: From Autoencoders to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4: From Generative Models to Large Language Models 7. Chapter 5: Fine-Tuning Language Models for Text Classification 8. Chapter 6: Fine-Tuning Language Models for Token Classification 9. Chapter 7: Text Representation 10. Chapter 8: Boosting Model Performance 11. Chapter 9: Parameter Efficient Fine-Tuning 12. Part 3: Advanced Topics
13. Chapter 10: Large Language Models 14. Chapter 11: Explainable AI (XAI) in NLP 15. Chapter 12: Working with Efficient Transformers 16. Chapter 13: Cross-Lingual and Multilingual Language Modeling 17. Chapter 14: Serving Transformer Models 18. Chapter 15: Model Tracking and Monitoring 19. Part 4: Transformers beyond NLP
20. Chapter 16: Vision Transformers 21. Chapter 17: Multimodal Generative Transformers 22. Chapter 18: Revisiting Transformers Architecture for Time Series 23. Index 24. Other Books You May Enjoy

SageMaker inference

Amazon, in collaboration with Hugging Face, created a nice way of deploying models using only a few lines of code. To deploy any model using SageMaker, you can simply visit the model page at Hugging Face and click the Amazon SageMaker button. Please also note that other methods such as Azure are also available:

Figure 14.9 – Amazon SageMaker button

Figure 14.9 – Amazon SageMaker button

This will give the related code to use SageMaker for inference but note that you need to set up the AWS SageMaker environment first. We did not include this part in the book because it is another topic entirely, but you can easily find it in the SageMaker documentation (https://aws.amazon.com/sagemaker/).

Let’s see how we can use SageMaker for inference:

  1. The first step is to install sagemaker and then import it:
    import sagemaker
    import boto3
    from sagemaker.huggingface import HuggingFaceModel
  2. The next step is to run this code to get the role:
    try:
       ...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image