What is PyTorch Lightning?
PyTorch Lightning is a lightweight wrapper around the PyTorch library that helps researchers and engineers to organize their PyTorch code and streamline the training process. PyTorch Lightning provides a structured framework for organizing PyTorch code, automating repetitive tasks, and enabling advanced features such as multi-GPU training, mixed precision, and distributed training.
Why use PyTorch Lightning?
Some benefits of using PyTorch Lightning include:
- Organized code: PyTorch Lightning encourages a more structured and modular approach to organizing PyTorch code.
- Automation: PyTorch Lightning automates repetitive tasks, such as logging, checkpointing, and TensorBoard integration.
- Scalability: PyTorch Lightning supports multi-GPU, mixed precision, and distributed training out of the box.
- Flexibility: PyTorch Lightning does not impose any constraints on the underlying PyTorch code, allowing users to leverage the full power of the PyTorch library.
PyTorch Lightning example
Here’s an example of creating a simple PyTorch Lightning model:
import pytorch_lightning as pl
import torch
from torch import nn
from torch.optim import Adam
class MyModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer = nn.Linear(28 * 28, 10)
def forward(self, x):
return torch.relu(self.layer(x))
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = nn.functional.cross_entropy(y_hat, y)
self.log("train_loss", loss)
return loss
def configure_optimizers(self):
return Adam(self.parameters(), lr=0.001)
# Train the model
model = MyModel()
trainer = pl.Trainer(max_epochs=10)
trainer.fit(model, train_dataloader, val_dataloader)
In this example, we create a simple linear model using PyTorch Lightning and train it using the provided dataloaders.
