PyTorch Lightning is a high-level PyTorch wrapper that organizes PyTorch code to remove boilerplate, enforce best practices, and enable scalable training. Built on top of PyTorch, it abstracts distributed training, mixed precision, callbacks, logging, and more while maintaining full control over the training loop. The framework is designed for researchers who need production-grade code without sacrificing flexibility—you write the research code in a LightningModule, and Lightning handles the engineering complexity. The key insight: Lightning doesn't abstract your PyTorch code; it structures it, making models reproducible, shareable, and scalable from laptop to supercomputer with minimal code changes.