Organize Your Code

Any raw PyTorch can be converted to Fabric with zero refactoring required, giving maximum flexibility in how you want to organize your projects.

However, when developing a project in a team or sharing the code publicly, it can be beneficial to conform to a standard format of how core pieces of the code are organized. This is what the LightningModule was made for!

Here is how you can neatly separate the research code (model, loss, optimization, etc.) from the “trainer” code (training loop, checkpointing, logging, etc.).

Step 1: Move your code into LightningModule hooks

Take these main ingredients and put them in a LightningModule:

  • The PyTorch model(s) as an attribute (e.g. self.model)

  • The forward, including loss computation, goes into training_step()

  • Setup of optimizer(s) goes into configure_optimizers()

  • Setup of the training data loader goes into train_dataloader()

import lightning as L

class LitModel(L.LightningModule):
    def __init__(self):
        self.model = ...

    def training_step(self, batch, batch_idx):
        # Main forward, loss computation, and metrics goes here
        x, y = batch
        y_hat = self.model(x)
        loss = self.loss_fn(y, y_hat)
        acc = self.accuracy(y, y_hat)
        return loss

    def configure_optimizers(self):
        # Return one or several optimizers
        return torch.optim.Adam(self.parameters(), ...)

    def train_dataloader(self):
        # Return your dataloader for training
        return DataLoader(...)

    def on_train_start(self):
        # Do something at the beginning of training

    def any_hook_you_like(self, *args, **kwargs):

This is a minimal LightningModule, but there are many other useful hooks you can use.

Step 2: Call hooks from your Fabric code

In your Fabric training loop, you can now call the hooks of the LightningModule interface. It is up to you to call everything at the right place.

import lightning as L

fabric = L.Fabric(...)

# Instantiate the LightningModule
model = LitModel()

# Get the optimizer(s) from the LightningModule
optimizer = model.configure_optimizers()

# Get the training data loader from the LightningModule
train_dataloader = model.train_dataloader()

# Set up objects
model, optimizer = fabric.setup(model, optimizer)
train_dataloader = fabric.setup_dataloaders(train_dataloader)

# Call the hooks at the right time

for epoch in range(num_epochs):
    for i, batch in enumerate(dataloader):
        loss = model.training_step(batch, i)

        # Control when hooks are called
        if condition:

Your code is now modular. You can switch out the entire LightningModule implementation for another one, and you don’t need to touch the training loop:

  # Instantiate the LightningModule
- model = LitModel()
+ model = DopeModel()


Access Fabric inside LightningModule

You can access the Fabric instance in any of the LightningModule hooks via self.fabric, provided that you called fabric.setup() on the module.

import lightning as L

class LitModel(L.LightningModule):
    def on_train_start(self):
        # Access Fabric and its attributes

fabric = L.Fabric()
model = fabric.setup(LitModel())

To maximize compatibility with LightningModules written for the Lightning Trainer, self.trainer is also available and will reroute to self.fabric.