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Machine Learning with Amazon SageMaker Cookbook

You're reading from   Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800567030
Length 762 pages
Edition 1st Edition
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Author (1):
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Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
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Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker 2. Chapter 2: Building and Using Your Own Algorithm Container Image FREE CHAPTER 3. Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker 4. Chapter 4: Preparing, Processing, and Analyzing the Data 5. Chapter 5: Effectively Managing Machine Learning Experiments 6. Chapter 6: Automated Machine Learning in Amazon SageMaker 7. Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor 8. Chapter 8: Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms 9. Chapter 9: Managing Machine Learning Workflows and Deployments 10. Other Books You May Enjoy

Preparing the entrypoint PyTorch inference script

In the previous recipe, we prepared the entrypoint training script to train a PyTorch model. In this recipe, we will prepare the entrypoint inference script to deploy a PyTorch model with SageMaker as we will see in the next recipe.

If you are planning to migrate your custom PyTorch neural network code and perform training and deployment with the SageMaker platform, then this recipe, the previous one, and the next one are for you!

Getting ready

This recipe continues from Preparing the entrypoint PyTorch training script.

How to do it

The instructions in this recipe focus on preparing the inference entrypoint script. Let's start by creating an empty file named pytorch_inference.py inside the Jupyter notebook instance and proceed with the next set of steps:

  1. Navigate to the /ml-experients/chapter03/PyTorch directory.

    Figure 3.42 – Navigating to the my-experiments/chapter03/SKLearn directory

    We can see in...

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