Prototyping a GPGPU Neural Network for Deep-Learning Big Data Analysis

This work presents a step-by-step guide on how to prototype a Deep-Learning application that executes both on GPU and CPU clusters. Python and Redis are the core supporting tools of this guide. This tutorial will allow the reader to understand the basics of building a distributed high performance GPU application in a few hours. Since we do not depend on any deep-learning application or framework-we use low-level building blocks-this tutorial can be adjusted for any other parallel algorithm the reader might want to prototype on Big Data. Finally, we will discuss how to move from a prototype to a fully blown production application.