Using an MXNet Neural Network to Style Images

The hands-on lab is part of these learning paths

Introduction to Machine Learning on AWS
course-steps 4 certification 1 lab-steps 2

Lab Steps

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Logging in to the Amazon Web Services Console
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Connecting to the Virtual Machine using SSH
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Using the Neural Network to Style an Image
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Downloading the Images Styled by the Neural Network
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Publishing GPU metrics to Amazon CloudWatch
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Inspecting the GPU Metrics in Amazon CloudWatch
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DifficultyIntermediate
Duration1h 10m
Students168
Ratings
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Description

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:

 

Updates

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

About the Author

Students30375
Labs93
Courses10
Learning paths6

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 Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Linux Foundation Certified System Administrator (LFCS), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.