.. _converting:
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How to Organize PyTorch Into Lightning
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To enable your code to work with Lightning, perform the following to organize PyTorch into Lightning.
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1. Keep Your Computational Code
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Keep your regular nn.Module architecture
.. testcode::
import lightning.pytorch as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
class LitModel(nn.Module):
def __init__(self):
super().__init__()
self.layer_1 = nn.Linear(28 * 28, 128)
self.layer_2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
return x
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2. Configure Training Logic
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In the training_step of the LightningModule configure how your training routine behaves with a batch of training data:
.. testcode::
class LitModel(pl.LightningModule):
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self.encoder(x)
loss = F.cross_entropy(y_hat, y)
return loss
.. note:: If you need to fully own the training loop for complicated legacy projects, check out :doc:`Own your loop <../model/own_your_loop>`.
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3. Move Optimizer(s) and LR Scheduler(s)
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Move your optimizers to the :meth:`~lightning.pytorch.core.module.LightningModule.configure_optimizers` hook.
.. testcode::
class LitModel(pl.LightningModule):
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.encoder.parameters(), lr=1e-3)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
return [optimizer], [lr_scheduler]
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4. Organize Validation Logic (optional)
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If you need a validation loop, configure how your validation routine behaves with a batch of validation data:
.. testcode::
class LitModel(pl.LightningModule):
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self.encoder(x)
val_loss = F.cross_entropy(y_hat, y)
self.log("val_loss", val_loss)
.. tip:: ``trainer.validate()`` loads the best checkpoint automatically by default if checkpointing was enabled during fitting.
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5. Organize Testing Logic (optional)
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If you need a test loop, configure how your testing routine behaves with a batch of test data:
.. testcode::
class LitModel(pl.LightningModule):
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self.encoder(x)
test_loss = F.cross_entropy(y_hat, y)
self.log("test_loss", test_loss)
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6. Configure Prediction Logic (optional)
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If you need a prediction loop, configure how your prediction routine behaves with a batch of test data:
.. testcode::
class LitModel(LightningModule):
def predict_step(self, batch, batch_idx):
x, y = batch
pred = self.encoder(x)
return pred
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7. Remove any .cuda() or .to(device) Calls
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Your :doc:`LightningModule <../common/lightning_module>` can automatically run on any hardware!
If you have any explicit calls to ``.cuda()`` or ``.to(device)``, you can remove them since Lightning makes sure that the data coming from :class:`~torch.utils.data.DataLoader`
and all the :class:`~torch.nn.Module` instances initialized inside ``LightningModule.__init__`` are moved to the respective devices automatically.
If you still need to access the current device, you can use ``self.device`` anywhere in your ``LightningModule`` except in the ``__init__`` and ``setup`` methods.
.. testcode::
class LitModel(LightningModule):
def training_step(self, batch, batch_idx):
z = torch.randn(4, 5, device=self.device)
...
Hint: If you are initializing a :class:`~torch.Tensor` within the ``LightningModule.__init__`` method and want it to be moved to the device automatically you should call
:meth:`~torch.nn.Module.register_buffer` to register it as a parameter.
.. testcode::
class LitModel(LightningModule):
def __init__(self):
super().__init__()
self.register_buffer("running_mean", torch.zeros(num_features))
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8. Use your own data
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Regular PyTorch DataLoaders work with Lightning. For more modular and scalable datasets, check out :doc:`LightningDataModule <../data/datamodule>`.
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Good to know
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Additionally, you can run only the validation loop using :meth:`~lightning.pytorch.trainer.trainer.Trainer.validate` method.
.. code-block:: python
model = LitModel()
trainer.validate(model)
.. note:: ``model.eval()`` and ``torch.no_grad()`` are called automatically for validation.
The test loop isn't used within :meth:`~lightning.pytorch.trainer.trainer.Trainer.fit`, therefore, you would need to explicitly call :meth:`~lightning.pytorch.trainer.trainer.Trainer.test`.
.. code-block:: python
model = LitModel()
trainer.test(model)
.. note:: ``model.eval()`` and ``torch.no_grad()`` are called automatically for testing.
.. tip:: ``trainer.test()`` loads the best checkpoint automatically by default if checkpointing is enabled.
The predict loop will not be used until you call :meth:`~lightning.pytorch.trainer.trainer.Trainer.predict`.
.. code-block:: python
model = LitModel()
trainer.predict(model)
.. note:: ``model.eval()`` and ``torch.no_grad()`` are called automatically for testing.
.. tip:: ``trainer.predict()`` loads the best checkpoint automatically by default if checkpointing is enabled.