DL environment setup locally

Throughout this book, we will be using Ubuntu OS to run all the experiments, because there is great community support for Linux and mostly any DL application can be set up easily on Linux. For any assistance on installation and setup related to Ubuntu, please refer to the tutorials at https://tutorials.ubuntu.com/. On top of that, this book will use the Anaconda package with Python 2.7+ to write our code, train, and test. Anaconda comes with a huge list of pre-installed Python packages, such as numpy, pandas, sklearn, and so on, which are commonly used in all kinds of data science projects.

Why do we need Anaconda? Can't we use Vanilla Python?
Anaconda is a generic bundle that contains iPython Notebook, editor, and lots of Python libraries preinstalled, which saves a lot of time on setting up everything. With Anaconda, we can quickly get started on solving the data science problem, instead of configuring the environment.
But, yes, you can use the default Python—it's totally the reader's choice, and we will learn at the end of this chapter how to configure python env using script.

Downloading and installing Anaconda

Anaconda is a very popular data science platform for people using Python to build machine learning and DL models, and deployable applications. The Anaconda marketing team put it best on their What is Anaconda? page, available at https://www.anaconda.com/what-is-anaconda/. To install Anaconda, perform the following steps:

1. Click Anaconda on the menu, then click Downloads to go to the download page at https://www.anaconda.com/download/#linux
2. Choose the download suitable for your platform (Linux, OS X, or Windows):
   1. Choose Python 3.6 version*
   2. Choose the Graphical Installer
3. Follow the instructions on the wizard, and in 10 to 20 minutes, your Anaconda environment (Python) setup will be ready

Once the installation process is completed, you can use following command to check the Python version on your Terminal:

python -V

You should see the following output:

Python 3.6 :: Anaconda,Inc.

If the command does not work, or returns an error, please check the documentation for help for your platform.

Installing DL libraries

Now, let's install the Python libraries used for DL, specifically, TensorFlow and Keras.

What is TensorFlow?
TensorFlow is a Python library developed and maintained by Google. You can implement many powerful machine learning and DL architectures in custom models and applications using TensorFlow. To find out more, visit https://www.tensorflow.org/.

Install the TensorFlow DL library (for all OS except Windows) by typing the following command:

conda install -c conda-forge tensorflow

Alternatively, you may choose to install using pip and a specific version of TensorFlow for your platform, using the following command:

pip install tensorflow==1.6

You can find the installation instructions for TensorFlow at https://www.tensorflow.org/get_started/os_setup#anaconda_installation.

Now we will install keras using the following command:

pip install keras

To validate the environment and the version of the packages, let's write the following script, which will print the version numbers of each library:

# Import the tensorflow library
import tensorflow
# Import the keras library
import keras

print('tensorflow: %s' % tensorflow.__version__)
print('keras: %s' % keras.__version__)

Save the script as dl_versions.py. Run the script by typing the following command:

python dl_version.py

You should see the following output:

tensorflow: 1.6.0
Using TensorFlow backend.
keras: 2.1.5

Voila! Now our Python development environment is ready for us to write some awesome DL applications in our local.