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

Summary

<p>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.</p> <p>The post <a href="https://www.marktechpost.com/2026/07/15/building-a-gin-config-controlled-pytorch-pipeline-with-configurable-mlp-variants-cosine-scheduling-and-runtime-parameter-overrides/">Building a Gin Config Controlled PyTorch Pipeline with Configurable MLP Variants, Cosine Scheduling, and Runtime Parameter Overrides</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>