The application of deep learning models to real world problems has been growing exponentially over the past several years. The widespread availability of NVIDIA GPUs, packages such as Tensorflow and Keras, and large online data sets has democratized this technology and ignited interest among developers, analysts, data scientists, and others looking to leverage the power that deep learning offers.
However, setting up a functional and effective deep learning environment can be challenging for a number of reasons. First, there are several moving parts and versions of the various software components must be compatible with one another. As new editions are released, configuration instructions that worked several months ago may no longer be viable. Also, searching the Web for help will return instructions that take differing (and sometimes incorrect) approaches – adding further confusion.
The objective of this post is to provide a rock-solid Ubuntu configuration that is anchored to the most current GPU-based version of Tensorflow – to get your deep learning environment up and running quickly and painlessly.
Note: This installation has been tested on both an AWS p2.xlarge instance as well as one of my personal Intel-based PCs.