Train a model (basic)¶

Audience: Users who need to train a model without coding their own training loops.

Add imports¶

Add the relevant imports at the top of the file

import os
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader, random_split
import pytorch_lightning as pl

Define the PyTorch nn.Modules¶

class Encoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Sequential(nn.Linear(28 * 28, 64), nn.ReLU(), nn.Linear(64, 3))

    def forward(self, x):
        return self.l1(x)


class Decoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Sequential(nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28 * 28))

    def forward(self, x):
        return self.l1(x)

Define a LightningModule¶

The LightningModule is the full recipe that defines how your nn.Modules interact.

The training_step defines how the nn.Modules interact together.
In the configure_optimizers define the optimizer(s) for your models.

class LitAutoEncoder(pl.LightningModule):
    def __init__(self, encoder, decoder):
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder

    def training_step(self, batch, batch_idx):
        # training_step defines the train loop.
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

Define the training dataset¶

Define a PyTorch DataLoader which contains your training dataset.

dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
train_loader = DataLoader(dataset)

Train the model¶

To train the model use the Lightning Trainer which handles all the engineering and abstracts away all the complexity needed for scale.

# model
autoencoder = LitAutoEncoder(Encoder(), Decoder())

# train model
trainer = pl.Trainer()
trainer.fit(model=autoencoder, train_dataloaders=train_loader)

Eliminate the training loop¶

Under the hood, the Lightning Trainer runs the following training loop on your behalf

autoencoder = LitAutoEncoder(encoder, decoder)
optimizer = autoencoder.configure_optimizers()

for batch_idx, batch in enumerate(train_loader):
    loss = autoencoder(batch, batch_idx)

    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

The power of Lightning comes when the training loop gets complicated as you add validation/test splits, schedulers, distributed training and all the latest SOTA techniques.

With Lightning, you can add mix all these techniques together without needing to rewrite a new loop every time.