Lab Overview

Graphics processing units (GPUs) and other hardware accelerators can dramatically reduce the time taken to train complex machine learning models. In this lab, you will take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform a CPU vs GPU performance analysis for Amazon Machine Learning. The instance is based on the AWS deep learning AMI that comes with many machine learning libraries pre-installed. You will create a Jupyter Notebook to write code and visualize results in a single document. The TensorFlow library is used for the CPU and GPU benchmark code.

Lab Objectives

Upon completion of this Lab you will be able to:

• Run Jupyter Notebook server and create Jupyter Notebooks for machine learning experiments
• Configure an SSH tunnel to forward instance ports through an encrypted channel
• Understand when GPUs can be advantageous in machine learning, and to what extent

Lab Prerequisites

You should be familiar with:

• Working with Linux on the command-line
• Knowledge of the Python programming language is beneficial, but not required

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:

 

Updates

June 28th, 2023 - Resolved the tunnel creation issue

November 28th, 2022 - Updated lab to use EC2 Instance connect

January 10th, 2019 - Added a validation Lab Step to check the work you perform in the Lab