pytorch-lightning
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
npx skills add Orchestra-Research/AI-Research-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 - High-Level Training Framework ## Quick start PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility. **Installation**: ```bash pip install lightning ``` **Convert PyTorch to Lightning** (3 steps): ```python import lightning as L import torch from torch import nn from torch.utils.data import DataLoader, Dataset # Step 1: Define LightningModule (organize your PyTorch code) class LitModel(L.LightningModule): def __init__(self, hidden_size=128): super().__init__() self.model = nn.Sequential( nn.Linear(28 * 28, hidden_size), nn.ReLU(), nn.Linear(hidden_size, 10) ) def training_step(self, batch, batch_idx): x, y = batch y_hat = self.model(x) loss = nn.functional.cross_entropy(y_hat, y) self.log('train_loss', loss) # Auto-logged to TensorBoard return loss def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=1e-3) # Step 2: Create data train_loader = DataLoader(train_dataset, batch_size=32) # Step 3: Train with Trainer (handles everything else!) trainer = L.Trainer(max_epochs=10, accelerator='gpu', devices=2) model = LitModel() trainer.fit(model, train_loader) ``` **That's it!** Trainer handles: - GPU/T
- Quick start
- Common workflows
- Workflow 1: From PyTorch to Lightning
- Workflow 2: Validation and testing
- Workflow 3: Distributed training (DDP)
- Workflow 4: Callbacks for monitoring
- Workflow 5: Learning rate scheduling
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
pip install lightning Single command, Lightning handles the rest python train.py
What does the pytorch-lightning skill do?
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-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 Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 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.