Tabular Classification with Lightning

Lightning is a unified framework from Lightning AI that includes PyTorch Lightning, Lightning Fabric, Lightning Apps, and TorchMetrics, designed to complement rather than replace PyTorch by simplifying model training, evaluation, and scaling. PyTorch Lightning provides a structured, high-level approach through LightningModule and Trainer, automating training loops, distributed multi-GPU or multi-node execution, logging, callbacks, checkpoints, and evaluation conventions, while Lightning Fabric offers a lighter-weight alternative that manages hardware and distributed-training setup but leaves engineers in control of custom training loops. Examples based on MLP and ElasticNet-style models illustrate how standard PyTorch nn.Module models can be wrapped in LightningModule or trained with Fabric, reducing boilerplate while preserving flexibility. The Lightning Quant project demonstrates both approaches in an algorithmic-trading context, including custom Trainer and Fabric implementations, datasets and data modules, regularization and metrics, and Typer-based command-line commands for launching training sessions.

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