How to use PyTorch Lightning to save and load models during training and inference?

To save and load models during training and inference using PyTorch Lightning, you can use the save_checkpoint() and load_from_checkpoint() methods. Here are the steps:

1. In your PyTorch Lightning LightningModule, define a method to save the model using the save_checkpoint() method. This method should save any necessary information such as model weights, optimizer state, and any other relevant training state.
2. To load a saved checkpoint during inference, define another method to load the model using the load_from_checkpoint() method. This method should load the saved state and restore it to the model.
3. Call the save_checkpoint() method during training at the end of each epoch or at any other desired intervals.
4. To load the saved checkpoint during inference, call the load_from_checkpoint() method and pass in the path to the saved checkpoint file.

Here’s an example code snippet:

import pytorch_lightning as pl

class MyModel(pl.LightningModule):
    def __init__(self):
        # define your model architecture here

    def forward(self, x):
        # define your forward pass here

    def training_step(self, batch, batch_idx):
        # your training loop here

    def validation_step(self, batch, batch_idx):
        # your validation loop here

    def save_model(self, checkpoint_path):
        checkpoint = {
                'model_state_dict': self.state_dict(),
                'optimizer_state_dict': self.trainer.optimizers[0].state_dict(),
                }
        torch.save(checkpoint, checkpoint_path)

    def load_model(self, checkpoint_path):
        checkpoint = torch.load(checkpoint_path)
        self.load_state_dict(checkpoint['model_state_dict'])
        self.trainer.optimizers[0].load_state_dict(checkpoint['optimizer_state_dict'])

data_module = MyDataModule()
model = MyModel()
trainer = pl.Trainer(max_epochs=10)

for epoch in range(trainer.max_epochs):
    trainer.fit(model, datamodule=data_module)
    model.save_model(f'checkpoint_epoch_{epoch}.pt')

# Load model for inference or resuming training
model.load_model('checkpoint_epoch_9.pt')

This example saves the model at the end of each training epoch and loads it for inference or resuming training. Note that this is just a basic example and you may need to modify it based on your specific needs.