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.
Instrucciones de origen · Vista previa de solo lectura
name
pytorch-lightning
description
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.
PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.
Installation:
pip install lightning
Convert PyTorch to Lightning (3 steps):
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)classLitModel(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)
)
deftraining_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 TensorBoardreturn loss
defconfigure_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=, accelerator=, devices=)
model = LitModel()
trainer.fit(model, train_loader)
10
'gpu'
2
That's it! Trainer handles:
GPU/TPU/CPU switching
Distributed training (DDP, FSDP, DeepSpeed)
Mixed precision (FP16, BF16)
Gradient accumulation
Checkpointing
Logging
Progress bars
Common workflows
Workflow 1: From PyTorch to Lightning
Original PyTorch code:
model = MyModel()
optimizer = torch.optim.Adam(model.parameters())
model.to('cuda')
for epoch inrange(max_epochs):
for batch in train_loader:
batch = batch.to('cuda')
optimizer.zero_grad()
loss = model(batch)
loss.backward()
optimizer.step()
Lightning version:
classLitModel(L.LightningModule):
def__init__(self):
super().__init__()
self.model = MyModel()
deftraining_step(self, batch, batch_idx):
loss = self.model(batch) # No .to('cuda') needed!return loss
defconfigure_optimizers(self):
return torch.optim.Adam(self.parameters())
# Train
trainer = L.Trainer(max_epochs=10, accelerator='gpu')
trainer.fit(LitModel(), train_loader)
# Same code as single GPU!
model = LitModel()
# 8 GPUs with DDP (automatic!)
trainer = L.Trainer(
accelerator='gpu',
devices=8,
strategy='ddp'# Or 'fsdp', 'deepspeed'
)
trainer.fit(model, train_loader)
Launch:
# Single command, Lightning handles the rest
python train.py
Organized: Separates research code from engineering
Automatic: DDP, FSDP, DeepSpeed with 1 line
Callbacks: Modular training extensions
Reproducible: Less boilerplate = fewer bugs
Tested: 1M+ downloads/month, battle-tested
Use alternatives instead:
Accelerate: Minimal changes to existing code, more flexibility
Ray Train: Multi-node orchestration, hyperparameter tuning
Raw PyTorch: Maximum control, learning purposes
Keras: TensorFlow ecosystem
Common issues
Issue: Loss not decreasing
Check data and model setup:
# Add to training_stepdeftraining_step(self, batch, batch_idx):
if batch_idx == 0:
print(f"Batch shape: {batch[0].shape}")
print(f"Labels: {batch[1]}")
loss = ...
return loss
