| name | pytorch-lightning |
| description | Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training. |
| license | Apache-2.0 license |
| metadata | {"skill-author":"K-Dense Inc."} |
| verified | false |
| lastVerifiedAt | "2026-02-19T05:29:09.098Z" |
| source | builtin |
| trust_score | 100 |
| provenance_sha | 39d536af1bf3935e |
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:
- Initialization -
__init__() and setup()
- Training Loop -
training_step(batch, batch_idx)
- Validation Loop -
validation_step(batch, batch_idx)
- Test Loop -
test_step(batch, batch_idx)
- Prediction -
predict_step(batch, batch_idx)
- 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 Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:
- Multi-GPU/TPU support with strategy selection (DDP, FSDP, DeepSpeed)
- Automatic mixed precision training
- Gradient accumulation and clipping
- Checkpointing and early stopping
- Progress bars and logging
Quick setup reference: See scripts/quick_trainer_setup.py for common Trainer configurations.
Detailed documentation: Read references/trainer.md for all parameters, methods, and configuration options.
