Building a Gin Config Controlled PyTorch Pipeline with Configurable MLP Variants, Cosine Scheduling, and Runtime Parameter Overrides

This tutorial demonstrates how to build a machine learning training pipeline using Gin Config, a tool that separates experimental settings from source code by storing them in configuration files. The approach keeps the executable training code stable while moving architectural choices, optimizer settings, scheduling parameters, and other training variables into declarative configuration files that can be modified without editing the code itself. The tutorial constructs a practical example using PyTorch that includes a spiral dataset classification task, a configurable neural network with adjustable layers and activation functions, and support for runtime parameter overrides. This separation of configuration from code is significant because it enables researchers and engineers to run multiple experiments with different hyperparameters and model variants without changing the underlying implementation.