Book Image

Hands-On Deep Learning for Images with TensorFlow

By : Will Ballard
Book Image

Hands-On Deep Learning for Images with TensorFlow

By: Will Ballard

Overview of this book

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
Table of Contents (7 chapters)

Installing Docker

We'll need to download Docker to get it installed, and in this section, you'll see how we install Docker on Windows and use a script that's suitable for installation on Linux.

Let's install Docker from The quickest way to get this done is to head up to the menu. Here, we'll choose to download the version for Windows. Give it a click, which will take you right over to the Docker store, where you can download the specific installer for your platform, as shown in the following screenshot:

Docker installer window

All the platforms are available here. We'll just download the MSI for Windows. It downloads relatively quickly, and once it's on your PC, you can just click the MSI installer and it will quickly continue.

Installing on Ubuntu is best done with a script. So, I've provided a sample installation script ( that will update your local package manager pointing to the official Docker distribution repositories, and then simply use apps to get the installation completed.

Getting Docker installed on Linux is pretty straightforward: you just run the install-docker shell script I've provided. The packages will update, download, and then install. When you get to the end of it, you just have to type docker --help to make sure that everything is installed:

Output—docker --help command

Now, for GPU support, which will make your Keras and TensorFlow models run faster, there is a special version called nvidia-docker, which exposes devices on Ubuntu to your Docker containers to allow GPU acceleration. There's an install script for this as well ( Now, assuming that you do have an actual NVIDIA graphics card, you can use NVIDIA Docker in place of Docker.

Here, we're running a test command that uses the NVIDIA SMI, which is really the status program that shows you the GPU status on your machine:

GPU status

And you can see, our TITAN X is fully exposed to Docker. Getting Docker installed is a relatively easy operation.

In the next section, we're going to take a look at authoring a Docker file to set up a complete machine learning environment.