From PyTorch to PyTorch Lighting: Getting Started Guide

PyTorch Lightning is an open-source wrapper for PyTorch that reduces boilerplate in deep learning projects by organizing model logic, training steps, optimization, logging, and evaluation through standardized components such as LightningModule and Trainer. It aims to make code cleaner, more modular, reproducible, and easier to maintain while allowing researchers to focus on model architecture rather than manual training-loop implementation. Migrating from traditional PyTorch generally involves moving forward-pass, loss, and optimizer logic into a LightningModule, then using a Trainer to run training. The framework also integrates with experiment-tracking tools such as TensorBoard and Weights & Biases, and provides built-in support for multi-GPU, distributed, mixed-precision, and lower-precision training with minimal code changes.

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