pytorch-lightninglisted

# PyTorch Lightning ## Overview 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. ## When to Use This Skill 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 ## Core Capabilities ### 1. LightningModule - Model Definition Organize PyTorch models into six logical sections: 1. **Initialization** - `__init__()` and `setup()` 2. **Training Loop** - `training_step(batch, batch_idx)` 3. **Validation Loop** - `validation_step(batch, batch_idx)` 4. **Test Loop** - `test_step(batch, batch_idx)` 5. **Prediction** - `predict_step(batch, batch_idx)` 6. **Optimizer Configuration** - `configure_optimizers()` **Quick template reference:** See `scripts/template_lightning_module.py` for a complete boilerplate. **Detailed documentation:** Read `references/lightning_module.md` for comprehensive method documentation, hooks, properties, and best practices. ### 2. Trainer - Training Automation The Trai