pytorch-lightning

Topics in the source

• PyTorch Lightning
• Overview
• When to Use This Skill
• Core Capabilities
• 1. LightningModule - Model Definition
• 2. Trainer - Training Automation
• 3. LightningDataModule - Data Pipeline Organization
• 4. Callbacks - Extensible Training Logic

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs/TPUs. This skill should be used when: Building, training, or deploying neural networks using PyTorch Lightning Organizing PyTorch code into LightningModules Configuring Trainers for multi-GPU/TPU training Implementing data pipelines with LightningDataModules Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed) Structuring deep learning projects professionally Organize PyTorch models into six logical sections: 1. Initialization - init() and setup() 2. Training Loop - trainingstep(batch, batchidx) 3. Validation Loop - validationstep(batch, batchidx) 4. Test Loop - teststep(batch, batchidx) 5. Prediction - predictstep(batch, batchidx) 6. Optimizer Configuration - configureoptimizers() Quick template reference: See scripts/templatelightningmodule.py for a complete boilerplate. Detailed documentation: Read references/lightningmodule.md for comprehensive method documentat

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