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

Short summary

This article describes building a PyTorch training pipeline controlled by Gin config files, where experiment variables are externalized from source code into .gin files. It covers a nonlinear spiral binary classification task with a configurable MLP, exposing optimizer, scheduler, loss, batching, and seeding through @gin.configurable bindings. Two scoped experiments are run with runtime overrides and operative configs exported per run.

• •PyTorch pipeline with Gin config externalizes experiment variables into .gin files
• •Configurable MLP with scoped architectural variants on a spiral classification task
• •Runtime parameter overrides and config export demonstrated across two experiments

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