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

We build a Gin Config controlled PyTorch pipeline where the training code stays fixed and the experiment variables move into . gin files. We construct a nonlinear spiral binary classification task and define a configurable MLP with scoped architectural variants.

We expose the optimizer, scheduler, loss, batching, seeding, and training loop through @gin. configurable bindings. We then run two scoped experiments, apply runtime overrides without editing source, and export the operative config for each run.