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.

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