You're reading from Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801817950

Length 554 pages

Edition 2nd Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

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Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Amazon SageMaker

2. Chapter 1: Introducing Amazon SageMaker FREE CHAPTER

3. Chapter 2: Handling Data Preparation Techniques

4. Section 2: Building and Training Models

5. Chapter 3: AutoML with Amazon SageMaker Autopilot

6. Chapter 4: Training Machine Learning Models

7. Chapter 5: Training CV Models

8. Chapter 6: Training Natural Language Processing Models

9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks

10. Chapter 8: Using Your Algorithms and Code

11. Section 3: Diving Deeper into Training

12. Chapter 9: Scaling Your Training Jobs

13. Chapter 10: Advanced Training Techniques

14. Section 4: Managing Models in Production

15. Chapter 11: Deploying Machine Learning Models

16. Chapter 12: Automating Machine Learning Workflows

17. Chapter 13: Optimizing Prediction Cost and Performance

18. Other Books You May Enjoy

Customizing an existing framework container

Of course, we could simply write a Dockerfile referencing one of the Deep Learning Containers images (https://github.com/aws/deep-learning-containers/blob/master/available_images.md) and add our own commands. See the following example:

FROM 763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:2.4.1-cpu-py37-ubuntu18.04
. . .

Instead, let's customize and rebuild the PyTorch training and inference containers on our local machine. The process is similar to other frameworks.

Build environment

Docker needs to be installed and running. To avoid throttling when pulling base images, I recommend that you create a Docker Hub account (https://hub.docker.com) and log in with docker login or Docker Desktop.

To avoid bizarre dependency issues (I'm looking at you, macOS), I also recommend that you build images on an Amazon EC2 instance powered by Amazon Linux 2. You don't need a large one (m5.large should suffice...

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You're reading from Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

Table of Contents (19) Chapters

Customizing an existing framework container

Authors (1)

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You're reading from Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

Table of Contents (19) Chapters

Customizing an existing framework container

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