# SageMaker Migration Exercise (PyTorch)

In this exercise, you'll migrate an [example notebook](Local%20Notebook.ipynb) (fitting a PyTorch CNN model on the MNIST Digits sample dataset) into the SageMaker data science workflow.

**To get started, clone this repository into a SageMaker Notebook instance (any instance type will do) and fire up the [Instructions.ipynb](Instructions.ipynb) notebook!**


## Prerequisites

This practice exercise is intended to be delivered with in-person support, and assumes you:

- Have had a high-level introduction to the SageMaker workflow, and:
- Are familiar with using the AWS Console to access Amazon SageMaker and Amazon S3
- Are familiar with configuring SageMaker Notebook Instance Execution Roles with appropriate Amazon S3 access

If that doesn't sound like you, you might prefer to check out:

- The official [Introductory Amazon SageMaker Tutorial](https://aws.amazon.com/getting-started/tutorials/build-train-deploy-machine-learning-model-sagemaker/)
- The ["Get Started with the Amazon SageMaker Console"](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-console.html) page in the [Amazon SageMaker Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html)