19  A fastai Learner from Scratch

This final chapter (other than the conclusion and the online chapters) is going to look a bit different. It contains far more code and far less prose than the previous chapters. We will introduce new Python keywords and libraries without discussing them. This chapter is meant to be the start of a significant research project for you. You see, we are going to implement many of the key pieces of the fastai and PyTorch APIs from scratch, building on nothing other than the components that we developed in Chapter 17! The key goal here is to end up with your own Learner class, and some callbacks—enough to be able to train a model on Imagenette, including examples of each of the key techniques we’ve studied. On the way to building Learner, we will create our own version of Module, Parameter, and parallel DataLoader so you have a very good idea of what those PyTorch classes do.

The end-of-chapter questionnaire is particularly important for this chapter. This is where we will be pointing you in the many interesting directions that you could take, using this chapter as your starting point. We suggest that you follow along with this chapter on your computer, and do lots of experiments, web searches, and whatever else you need to understand what’s going on. You’ve built up the skills and expertise to do this in the rest of this book, so we think you are going to do great!

Let’s begin by gathering (manually) some data.

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This is just a preview of this chapter. The rest of this chapter is not available here, but you read the source notebook which has the same content (but with less nice formatting).