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.
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.
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:
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 for predictions:
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 using BasePredictionWriter
.
import torch
from lightning.pytorch.callbacks import BasePredictionWriter
class CustomWriter(BasePredictionWriter):
def __init__(self, output_dir, write_interval):
super().__init__(write_interval)
self.output_dir = output_dir
def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices):
# this will create N (num processes) files in `output_dir` each containing
# the predictions of it's respective rank
torch.save(predictions, os.path.join(self.output_dir, f"predictions_{trainer.global_rank}.pt"))
# optionally, you can also save `batch_indices` to get the information about the data index
# from your prediction data
torch.save(batch_indices, os.path.join(self.output_dir, f"batch_indices_{trainer.global_rank}.pt"))
# or you can set `writer_interval="batch"` and override `write_on_batch_end` to save
# predictions at batch level
pred_writer = CustomWriter(output_dir="pred_path", write_interval="epoch")
trainer = Trainer(accelerator="gpu", strategy="ddp", devices=8, callbacks=[pred_writer])
model = BoringModel()
trainer.predict(model, return_predictions=False)