Programming PyTorch for Deep Learning

Chapter 1. Getting Started with PyTorch

In this chapter we set up all we need for working with PyTorch. Once we’ve done that, every chapter following will build on this initial foundation, so it’s important that we get it right. This leads to our first fundamental question: should you build a custom deep learning computer or just use one of the many cloud-based resources available?

Building a Custom Deep Learning Machine

There is an urge when diving into deep learning to build yourself a monster for all your compute needs. You can spend days looking over different types of graphics cards, learning the memory lanes possible CPU selections will offer you, the best sort of memory to buy, and just how big an SSD drive you can purchase to make your disk access as fast as possible. I am not claiming any immunity from this; I spent a month a couple of years ago making a list of parts and building a new computer on my dining room table.

My advice, especially if you’re new to deep learning, is this: don’t do it. You can easily spend several thousands of dollars on a machine that you may not use all that much. Instead, I recommend that you work through this book by using cloud resources (in either Amazon Web Services, Google Cloud, or Microsoft Azure) and only then start thinking about building your own machine if you feel that you require a single machine for 24/7 operation. You do not need to make a massive investment in hardware to run any of the code in this book.

You might not ever ...