When the development began last fall on Fast.ai 1.0, I decided to try to write my own version in Swift so that I could learn more about how Fast.ai is put together and try to demystify the things that it does as well as learn Pytorch better. Also, it would allow me to continue practicing my Swift skills. Since a couple of the Part 2 lessons are going to be using Swift, I thought I’d share what I’ve created so far for anyone here who is interested. I thought it might be useful for those who want to see how things like callbacks, training loops, closures, etc. can be done in Swift as well as how to run Python and Pytorch code from within Swift. I’ve created a Docker setup for it for ease of installation as well as some examples that can be run. Those are: MNIST, CIFAR-10, Dogs Vs. Cats Redux (Kaggle), Kaggle Planet competition and Pascal VOC 2007. You can also submit the output to Kaggle for the 2 Kaggle competition ones. I’m going to try to add to the readme on how things are architected but for now it just has installation instructions and how to run the examples. Also, unfortunately it only supports CPU for now. Here’s a link to the repo https://github.com/sjaz24/SwiftAI