Building a GAN (Generative Adversarial Network)

A walkthrough demonstrates how to build an interactive Generative Adversarial Network application with Lightning, PyTorch Lightning, and Gradio. The project trains a GAN on MNIST digits, using a generator to create images from random noise and a discriminator to distinguish generated images from real examples, then adapts a Lightning quick-start template to manage training and serving. It covers creating a Python environment, installing dependencies, updating the PyTorch Lightning training script to use LightningCLI, configuring checkpoints so the trained model is available to the serving component, and replacing the default Gradio interface with controls for random seed and number of generated digits. Running the app provides a TensorBoard-based training view and an interactive demo that becomes available after training, allowing users to generate digit grids and experiment with inputs, hyperparameters, and model design; the same application can also be deployed to the cloud with an added command-line option.

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