pytorch-lightning

# 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/TPU/CPU switching - Distributed training (DDP, FSDP, DeepSpeed) - Mixed precision (FP16, BF16) - Gradient accumulation - Checkpointing - Logging - Progress bars ## Comm...