{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Module 2. Training on Local Environment \n", "---\n", "\n", "This hands-on lab fine-tunes a pre-trained Image Classification model stored in model zoo, and train purely without using SageMaker training instance.\n", "\n", "***If you already have experience with Deep Learning training using PyTorch, you can skip this notebook and go straight to SageMaker training notebook. The main purpose of this notebook is to show that SageMaker is a Docker container based and you can easily move your training code to SageMaker with just a few lines of code.***\n", "\n", "This hands-on can be completed in about **20 minutes**. \n", "\n", "
It is recommended to use a GPU instance including g4dn.xlarge, and p3.2xlarge for this notebook. It also works on a CPU instance, but it can take 10-15 minutes to train just one epoch.