TensorFlow Machine Learning on the Amazon Deep Learning AMI

Lab Steps

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Logging in to the Amazon Web Services Console
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Forwarding a Virtual Machine Port through an SSH Tunnel
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Learning the Basics of TensorFlow
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Starting a Jupyter Notebook Server
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Creating a Neural Network in TensorFlow
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Visualizing the Learning of the Neural Network with TensorBoard
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Serving a Model with TensorFlow Serving
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Consuming the Model Served by TensorFlow Serving
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This lab is currently under maintenance. You can start the lab, but some instructions and screenshots may be inaccurate. We're actively working to resolve this issue and we apologize for any inconvenience.

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DifficultyIntermediate
Time Limit1h 40m
Students828
Ratings
3.9/5
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Description

TensorFlow is a popular framework used for machine learning. The Amazon Deep Learning AMI comes bundled with everything you need to start using TensorFlow from development through to production. In this Lab, you will develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI.

Lab Objectives

Upon completion of this lab you will be able to:

  • Create machine learning models in TensorFlow
  • Visualize TensorFlow graphs and the learning process in TensorBoard
  • Serve trained TensorFlow models with TensorFlow Serving
  • Create clients that consume served TensorFlow models, all with the Amazon Deep Learning AMI

Lab Prerequisites

You should be familiar with:

  • Working at the Linux command line
  • The Python programming language
  • Some linear algebra knowledge is beneficial (basic vector and matrix operations)
  • Basic understanding of neural networks 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

August 1st, 2021 - Fixed typo in lab instructions

December 7th. 2020 - Updated instructions to address an issue that prevented a lab step from being able to be completed successfully

April 15th, 2020 - Updated instructions to provide for a more streamlined lab experience

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

About the Author
Students112024
Labs180
Courses9
Learning paths30

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.