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Installing Python

We will be using virtualenv to create an isolated Python virtual environment. While this isn't strictly necessary, it's an excellent practice. By doing so, we will keep all our Python libraries for this project in a separate isolated environment that won't interfere with the system Python installation. Additionally, virtualenv environments will make it easier to package and deploy our deep neural networks later on.

Let's start by installing Python, pip, and virtualenv, using the aptitude package manager in Ubuntu. The following is the code:

sudo apt-get install python3-pip python3-dev python-virtualenv

Now we can create a virtual environment for our work. We will be keeping all our virtual environment files in a folder called ~/deep-learn. You are free to choose any name you wish for this virtual environment. The following code shows how to create a virtual environment:

virtualenv --no-site-packages -p python3 ~/deep-learn

If you're an experienced Python developer, you might have noticed that I've set up the environment to default to Python 3.x. That's most certainly not required, and TensorFlow / Keras both support Python 2.7. That said, the author feels a moral obligation to the Python community to support modern versions of Python.

Now that the virtual environment has been created, you can activate it as follows:

$source ~/deep-learn/bin/activate
(deep-learn)$ # notice the shell changes to indicate the virtualenv

At this point, every time you log in you will need to activate the virtual environment you want to work in. If you would like to always enter the virtual environment you just created, you can add the source command to ~/.bash_profile.

Now that we've configured our virtual environment, we can add Python packages as required within it. To start, let's make sure we have the latest version of pip, the Python package manager:

easy_install -U pip

Lastly, I recommend installing IPython, which is an interactive Python shell that makes development much easier.

pip install ipython

And that's it. Now we're ready to install TensorFlow and Keras.