The hands-on lab is part of these learning paths
Ready for the real environment experience?
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.
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
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
Before completing the Lab instructions, the environment will look as follows:
After completing the Lab instructions, the environment should look similar to:
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
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.