81 lines
2.4 KiB
ReStructuredText
81 lines
2.4 KiB
ReStructuredText
#####################################
|
|
Deploy models into production (basic)
|
|
#####################################
|
|
**Audience**: All users.
|
|
|
|
----
|
|
|
|
*****************************
|
|
Load a checkpoint and predict
|
|
*****************************
|
|
The easiest way to use a model for predictions is to load the weights using **load_from_checkpoint** found in the LightningModule.
|
|
|
|
.. code-block:: python
|
|
|
|
model = LitModel.load_from_checkpoint("best_model.ckpt")
|
|
model.eval()
|
|
x = torch.randn(1, 64)
|
|
|
|
with torch.no_grad():
|
|
y_hat = model(x)
|
|
|
|
----
|
|
|
|
**************************************
|
|
Predict step with your LightningModule
|
|
**************************************
|
|
Loading a checkpoint and predicting still leaves you with a lot of boilerplate around the predict epoch. The **predict step** in the LightningModule removes this boilerplate.
|
|
|
|
.. code-block:: python
|
|
|
|
class MyModel(LightningModule):
|
|
def predict_step(self, batch, batch_idx, dataloader_idx=0):
|
|
return self(batch)
|
|
|
|
And pass in any dataloader to the Lightning Trainer:
|
|
|
|
.. code-block:: python
|
|
|
|
data_loader = DataLoader(...)
|
|
model = MyModel()
|
|
trainer = Trainer()
|
|
predictions = trainer.predict(model, data_loader)
|
|
|
|
----
|
|
|
|
********************************
|
|
Enable complicated predict logic
|
|
********************************
|
|
When you need to add complicated pre-processing or post-processing logic to your data use the predict step. For example here we do `Monte Carlo Dropout <https://arxiv.org/pdf/1506.02142.pdf>`_ for predictions:
|
|
|
|
.. code-block:: python
|
|
|
|
class LitMCdropoutModel(pl.LightningModule):
|
|
def __init__(self, model, mc_iteration):
|
|
super().__init__()
|
|
self.model = model
|
|
self.dropout = nn.Dropout()
|
|
self.mc_iteration = mc_iteration
|
|
|
|
def predict_step(self, batch, batch_idx):
|
|
# enable Monte Carlo Dropout
|
|
self.dropout.train()
|
|
|
|
# take average of `self.mc_iteration` iterations
|
|
pred = [self.dropout(self.model(x)).unsqueeze(0) for _ in range(self.mc_iteration)]
|
|
pred = torch.vstack(pred).mean(dim=0)
|
|
return pred
|
|
|
|
----
|
|
|
|
****************************
|
|
Enable distributed inference
|
|
****************************
|
|
By using the predict step in Lightning you get free distributed inference
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
trainer = Trainer(devices=8, accelerator="gpu")
|
|
predictions = trainer.predict(model, data_loader)
|