pytorch-lightning
Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
npx skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. **Current upstream:** lightning 2.6.4 (PyPI, May 2026). Docs: [lightning.ai/docs/pytorch/stable](https://lightning.ai/docs/pytorch/stable/). Use `import lightning as L` (the `pytorch-lightning` package name still installs the same library). ## Installation ```bash uv pip install lightning ``` Optional extras: ```bash uv pip install lightning[extra] # loggers, strategies, etc. uv pip install wandb mlflow # specific loggers as needed ``` ## 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 ## Cor
- Overview
- Installation
- 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
- 5. Logging - Experiment Tracking
- 6. Distributed Training - Scale to Multiple Devices
- 7. Best Practices
- Quick Workflow
- Resources
- scripts/
uv pip install lightning uv pip install lightning[extra] # loggers, strategies, etc. uv pip install wandb mlflow # specific loggers as needed
What does the pytorch-lightning skill do?
Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.