Analyzing CPU vs GPU Performance for AWS Machine Learning

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