Analyzing CPU vs GPU Performance for AWS Machine Learning
Description
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