PyTorch Lightning tips

PyTorch Lightning is an extension of PyTorch, abstracting complex boilerplate code, enabling more modular and scalable deep learning projects. It automates training loops, validation/testing, multi-GPU distribution, and early stopping, while maintaining PyTorch flexibility. It’s ideal for rapid, organized ML model prototyping and development.

When To Use

Research and Experimentation: Ideal for rapidly testing new ideas without worrying about the underlying engineering complexity.
Large-scale Projects: Facilitates managing and scaling larger models and datasets with less effort.
Reproducibility: Ensures consistent setup across different environments, aiding in reproducibility of experiments.

Benefits

Use for cleaner code: abstracts boilerplate, focusing on model, data, and training logic.
For scalability: supports multi-GPU, TPU, and distributed training with minimal code change.
Rapid prototyping: accelerates development cycle from research to production.
Reproducibility: ensures experiments can be easily reproduced and shared.
Advanced features: enables gradient accumulation, mixed precision, etc., with less complexity.

import pytorch_lightning as pl
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

# Define a model by extending the LightningModule
class SimpleModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = nn.Linear(10, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, batch, _):
        x, y = batch
        y_hat = self(x)
        return nn.functional.cross_entropy(y_hat, y)

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.02)

# Data preparation
x, y = torch.randn(100, 10), torch.randint(0, 2, (100,))
loader = DataLoader(TensorDataset(x, y), batch_size=32)

# PyTorch Lightning Trainer simplifies the training process
trainer = pl.Trainer(max_epochs=5)
trainer.fit(SimpleModel(), loader)