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Mastering Transformers

You're reading from   Mastering Transformers Build state-of-the-art models from scratch with advanced natural language processing techniques

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801077651
Length 374 pages
Edition 1st Edition
Languages
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Authors (2):
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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
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformer FREE CHAPTER 3. Chapter 2: A Hands-On Introduction to the Subject 4. Section 2: Transformer Models – From Autoencoding to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4:Autoregressive and Other 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. Section 3: Advanced Topics
11. Chapter 8: Working with Efficient Transformers 12. Chapter 9:Cross-Lingual and Multilingual Language Modeling 13. Chapter 10: Serving Transformer Models 14. Chapter 11: Attention Visualization and Experiment Tracking 15. Other Books You May Enjoy

Load testing using Locust

There are many applications we can use to load test services. Most of these applications and libraries provide useful information about the response time and delay of the service. They also provide information about the failure rate. Locust is one of the best tools for this purpose. We will use it to load test three methods for serving a Transformer-based model: using fastAPI only, using dockerized fastAPI, and TFX-based serving using fastAPI. Let's get started:

  1. First, we must install Locust:
    $ pip install locust

    This command will install Locust. The next step is to make all the services serving an identical task use the same model. Fixing two of the most important parameters of this test will ensure that all the services have been designed identically to serve a single purpose. Using the same model will help us freeze anything else and focus on the deployment performance of the methods.

  2. Once everything is ready, you can start load testing your...
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