So you have heard a lot about Deep Learning and Convolutional Neural Network, and you want to quickly try it out. But before you dive into the theory you want to get your hands dirty. And you don’t want to write a line of code. You also want to monitor progress of your training process from your smart phone. All I can say is that I respect your laziness! Let’s get started.

Instead of a step by step tutorial on how to install DIGITS on Amazon EC2, if you would rather have an Amazon Machine Image (AMI ) that has DIGITS preinstalled, you can read my follow up article titled “Deep Learning Example using NVIDIA DIGITS 3 on EC2”

In this post we will learn how to set up a Deep Learning framework ( NVIDIA DIGITS + Caffe / Torch ) on an Amazon EC2 instance. This setup will enable you to schedule training tasks, monitor progress, and visualize results using a web interface.

What is NVIDIA DIGITS ?

DIGITS stands for Deep Learning GPU Training System. It is a web / browser based graphical user interface that allows you to prepare data, set training parameters, choose from some popular neural net architectures (or use your own) and train a deep neural net. It is a perfect tool to get started if you know very little about Deep Learning. Under the hood DIGITS uses Caffe — the popular open source deep learning framework. Support for Torch — a deep learning framework backed by Facebook — is in beta, but you can try it out.

GPUs on EC2

One big obstacle in immediately starting with Deep Learning is access to a good GPU. You may not have an NVIDIA card on your laptop and even if you do it may not be very powerful. Sometimes training a deep neural net takes hours and it makes no sense to use your primary computer for the task.

Without a GPU deep learning is painfully slow. In fact, one of contributions of the 2012 paper that firmly established Deep Learning as the undisputed king of image classification algorithms was its clever use of two GPUs.

Fortunately we live in amazing times. We have access to near infinite compute power at our finger tips. All you need to do is to register for Amazon Web Services ( AWS ).

https://aws.amazon.com/

This will give you access to Amazon’s Elastic Compute Cloud (EC2) and its virtually unlimited compute resources ( for a price of course ). The web interface allows you to start a virtual server called an “instance”. We are interested in the two GPU enabled instance types that have the following specifications.

Model GPUs vCPU Mem (GiB) SSD Storage (GB) g2.2xlarge 1 8 15 1 x 60 g2.8xlarge 4 32 60 2 x 120