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

> Source: <https://www.marktechpost.com/2026/07/15/building-a-gin-config-controlled-pytorch-pipeline-with-configurable-mlp-variants-cosine-scheduling-and-runtime-parameter-overrides/>
> Published: 2026-07-15 18:03:38+00:00

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.

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