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">

In this tutorial, we implement a Gin Config –controlled PyTorch experiment pipeline in which the executable training code remains stable. At the same time, the experimental degrees of freedom are moved into declarative configuration files. We construct a nonlinear spiral binary classification task, define a configurable MLP with scoped architectural variants, and expose parameters for the optimizer, scheduler, loss, batching, seeding, and training loop via @gin.configurable bindings. We use Gin’s scoped references to instantiate separate model configurations, runtime bindings to override selected parameters without editing source code, and operative config export to capture the exact resolved configuration that produces each training run.

We start by installing Gin Config and importing the core Python libraries, PyTorch, NumPy, and the plotting libraries required for the experiment. We create a clean project directory structure and reset Gin’s global configuration state so the notebook runs reproducibly. We then define the seed function, generate a nonlinear spiral dataset, and build a configurable DataLoader that Gin can control through external bindings.

We define the neural network building blocks that form the configurable model and the training utilities. We create an MLP class whose architecture, activation function, dropout, and layer normalization behavior are controlled through Gin rather than hardcoded values. We also implement configurable optimizer, scheduler, loss, and evaluation functions so the training pipeline remains modular and experiment-ready.

We implement the main training loop, in which the model performs forward passes, computes binary cross-entropy loss, backpropagates gradients, applies gradient clipping, and updates parameters. We evaluate the model after each epoch on both the training and validation sets, while storing loss, accuracy, and learning rate history. We then define the top-level experiment runner that connects the dataset, model, optimizer, scheduler, and training loop through Gin-managed dependencies.

We create the actual Gin configuration files that control the experiment without modifying the Python source code. We define a shared base configuration and then compose two scoped experiments: a compact GELU-based AdamW model and a wider ReLU-based SGD model. We also demonstrate runtime overrides, parameter queries, config locking, result serialization, and operative config export for reproducible experiment tracking.

We visualize the validation loss and validation accuracy curves for both Gin-controlled experiments. We summarize the final parameter counts, validation losses, and validation accuracies to clearly compare the two configurations. We also print the operative configuration and the generated files, which provide a complete record of the exact settings used during execution.