TensorFlow Machine Learning on the Amazon Deep Learning AMI


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Total available time: 1h:0m

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Lab Overview

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:


Follow these steps to learn by building helpful cloud resources

Logging in to the Amazon Web Services Console

Your first step to start the Lab experience

Forwarding a Virtual Machine Port through an SSH Tunnel

Forward a virtual machine port through an encrypted SSH tunnel

Learning the Basics of TensorFlow

Familiarize yourself with fundamental TensorFlow concepts

Starting a Jupyter Notebook Server

Start a Jupyter notebook server in order to create your own notebook

Creating a Neural Network in TensorFlow

Create a simple neural network in TensorFlow

Visualizing the Learning of the Neural Network with TensorBoard

Use TensorBoard to visualize the network's learning progress during training

Serving a Model with TensorFlow Serving

Serving a trained model using TensorFlow Serving

Consuming the Model Served by TensorFlow Serving

Run a client that consumes the model served by TensorFlow Serving