hands-on lab

Using an MXNet Neural Network to Style Images

Up to 1h 10m
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.


Lab Overview

Neural networks have been used for many applications throughout the deep learning revolution. In this Lab, you will use the AWS Deep Learning AMI using a GPU instance (p2.xlarge). You will perform neural style transfers - an algorithm for combining the content of one image with the style of another image. This process involves using convolutional neural networks (CNN). The code you will run is implemented in Python using the MXNet deep learning framework. Additionally, you will setup a custom Python script to aggregate GPU performance data and publish it into Amazon CloudWatch. You will then be able to examine the performance and cost associated with the CNN as it runs.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Perform neural style transfers using the AWS Deep Learning AMI
  • Publish GPU metrics to Amazon CloudWatch using a Python script
  • Examine GPU performance in Amazon CloudWatch 

Lab Prerequisites

You should be familiar with:

  • Working with Linux on the command-line
  • Graphics processing unit (GPU) concepts
  • 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:



July 17th, 2024 - Updated the instructions to reflect the latest UI

October 30th, 2023 - Resolved an issue that caused the lab to fail to provision on rare occasions

December 5th, 2022 - Added checks to track lab progress

September 8th, 2022 - Recovered the GPU monitoring script functionality

August 31st, 2020 - Updated screenshots for the new EC2 user interface

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

About the author

Logan Rakai, opens in a new tab
Lead Content Developer - Labs
Learning paths

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Security Specialist (CKS), Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.

LinkedIn, Twitter, GitHub

Covered topics

Lab steps

Logging In to the Amazon Web Services Console
Connecting to the Virtual Machine using SSH
Using the Neural Network to Style an Image
Downloading the Images Styled by the Neural Network
Publishing GPU metrics to Amazon CloudWatch
Inspecting the GPU Metrics in Amazon CloudWatch