Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.
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.
Review the instructions the agent will receive before you install.
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)
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:
Original PyTorch code:
model = MyModel()
optimizer = torch.optim.Adam(model.parameters())
model.to('cuda')
for epoch in range(max_epochs):
for batch in train_loader:
batch = batch.to('cuda')
optimizer.zero_grad()
loss = model(batch)
loss.backward()
optimizer.step()
Lightning version:
class LitModel(L.LightningModule):
def __init__(self):
super().__init__()
self.model = MyModel()
def training_step(self, batch, batch_idx):
loss = self.model(batch) # No .to('cuda') needed!
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters())
# Train
trainer = L.Trainer(max_epochs=10, accelerator='gpu')
trainer.fit(LitModel(), train_loader)
Benefits: 40+ lines → 15 lines, no device management, automatic distributed
class LitModel(L.LightningModule):
def __init__(self):
super().__init__()
self.model = MyModel()
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)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self.model(x)
val_loss = nn.functional.cross_entropy(y_hat, y)
acc = (y_hat.argmax(dim=1) == y).float().mean()
self.log('val_loss', val_loss)
self.log('val_acc', acc)
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self.model(x)
test_loss = nn.functional.cross_entropy(y_hat, y)
self.log('test_loss', test_loss)
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-3)
# Train with validation
trainer = L.Trainer(max_epochs=10)
trainer.fit(model, train_loader, val_loader)
# Test
trainer.test(model, test_loader)
Automatic features:
# 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
No changes needed:
num_nodes=2)from lightning.pytorch.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor
# Create callbacks
checkpoint = ModelCheckpoint(
monitor='val_loss',
mode='min',
save_top_k=3,
filename='model-{epoch:02d}-{val_loss:.2f}'
)
early_stop = EarlyStopping(
monitor='val_loss',
patience=5,
mode='min'
)
lr_monitor = LearningRateMonitor(logging_interval='epoch')
# Add to Trainer
trainer = L.Trainer(
max_epochs=100,
callbacks=[checkpoint, early_stop, lr_monitor]
)
trainer.fit(model, train_loader, val_loader)
Result:
class LitModel(L.LightningModule):
# ... (training_step, etc.)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
# Cosine annealing
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=100,
eta_min=1e-5
)
return {
'optimizer': optimizer,
'lr_scheduler': {
'scheduler': scheduler,
'interval': 'epoch', # Update per epoch
'frequency': 1
}
}
# Learning rate auto-logged!
trainer = L.Trainer(max_epochs=100)
trainer.fit(model, train_loader)
Use PyTorch Lightning when:
Key advantages:
Use alternatives instead:
Issue: Loss not decreasing
Check data and model setup:
# Add to training_step
def training_step(self, batch, batch_idx):
if batch_idx == 0:
print(f"Batch shape: {batch[0].shape}")
print(f"Labels: {batch[1]}")
loss = ...
return loss
Issue: Out of memory
Reduce batch size or use gradient accumulation:
trainer = L.Trainer(
accumulate_grad_batches=4, # Effective batch = batch_size × 4
precision='bf16' # Or 'fp16', reduces memory 50%
)
Issue: Validation not running
Ensure you pass val_loader:
# WRONG
trainer.fit(model, train_loader)
# CORRECT
trainer.fit(model, train_loader, val_loader)
Issue: DDP spawns multiple processes unexpectedly
Lightning auto-detects GPUs. Explicitly set devices:
# Test on CPU first
trainer = L.Trainer(accelerator='cpu', devices=1)
# Then GPU
trainer = L.Trainer(accelerator='gpu', devices=1)
Callbacks: See references/callbacks.md for EarlyStopping, ModelCheckpoint, custom callbacks, and callback hooks.
Distributed strategies: See references/distributed.md for DDP, FSDP, DeepSpeed ZeRO integration, multi-node setup.
Hyperparameter tuning: See references/hyperparameter-tuning.md for integration with Optuna, Ray Tune, and WandB sweeps.
Precision options:
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