Managing PyTorch Experiments with Gin Config

Writing code that you have to change for every experiment is messy. We show you how to move your variables into separate files so your core logic stays clean. This makes it much easier to track your settings.

We set up a simple classification task using a flexible neural network. By using Gin bindings, you can control your optimizer, loss functions, and training loops from the outside. You no longer need to touch the source code to test a new idea.

This setup lets you run different experiments and save the exact settings for each one. You can even change parameters while the program is running. It is a great way to keep your research organized and reproducible.