pytorch lightning save checkpoint every n epoch

asalmes

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03-12-2024

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Saving model checkpoints at regular intervals during training is crucial for several reasons: it allows you to resume training from where you left off, experiment with different hyperparameters without retraining from scratch, and easily revert to earlier, potentially better-performing model versions. This article demonstrates how to effectively save checkpoints every N epochs using PyTorch Lightning. We'll cover different approaches and best practices.

Understanding PyTorch Lightning's Checkpoint Saving Mechanism

PyTorch Lightning provides a streamlined way to manage checkpoints. By default, it saves the model's state at the end of training. However, for more granular control, especially for saving checkpoints every N epochs, we'll leverage the Trainer's save_checkpoint method within a custom callback or by modifying the on_train_epoch_end method in your LightningModule.

Method 1: Using a Custom Callback

This approach offers clean separation of concerns and improved readability. We create a custom callback that extends PyTorch Lightning's Callback class.

import pytorch_lightning as pl
from pytorch_lightning.callbacks import Callback

class CheckpointEveryNEpochs(Callback):
    def __init__(self, save_step):
        super().__init__()
        self.save_step = save_step

    def on_train_epoch_end(self, trainer, pl_module):
        if (trainer.current_epoch + 1) % self.save_step == 0:
            checkpoint_path = f"epoch={trainer.current_epoch+1}"
            trainer.save_checkpoint(checkpoint_path)

# Example usage:
checkpoint_callback = CheckpointEveryNEpochs(save_step=5) # Save every 5 epochs
trainer = pl.Trainer(callbacks=[checkpoint_callback], ...)

This callback checks if the current epoch is a multiple of save_step. If it is, it saves a checkpoint with the epoch number in the filename. The trainer.save_checkpoint() method handles the saving process efficiently.

Method 2: Modifying the on_train_epoch_end Method in your LightningModule

This method integrates checkpoint saving directly into your LightningModule. While functional, it can sometimes make your LightningModule less focused and harder to maintain for complex projects.

import pytorch_lightning as pl

class MyLightningModule(pl.LightningModule):
    def __init__(self, save_step):
        super().__init__()
        self.save_step = save_step

    # ... your model code ...

    def on_train_epoch_end(self, outputs):
        if (self.trainer.current_epoch + 1) % self.save_step == 0:
            checkpoint_path = f"epoch={self.trainer.current_epoch+1}"
            self.trainer.save_checkpoint(checkpoint_path)

    # ... rest of your LightningModule ...

# Example Usage
model = MyLightningModule(save_step=10) #Save every 10 epochs
trainer = pl.Trainer(...)
trainer.fit(model)

This approach achieves the same outcome as the custom callback but embeds the checkpoint saving logic within the model itself.

Best Practices and Considerations

• Checkpoint Directory: Specify a directory for checkpoints using the default_root_dir argument in the Trainer. This helps organize your saved models.
• Filename Formatting: Use informative filenames that clearly indicate the epoch and potentially other relevant information like hyperparameters.
• Monitor Metrics: Consider saving checkpoints based on a monitored metric (e.g., validation accuracy) instead of a fixed epoch interval. PyTorch Lightning's ModelCheckpoint callback provides this functionality.
• Checkpoint Size: Large checkpoints can consume significant disk space. Periodically clean up older checkpoints if storage becomes an issue.
• Early Stopping: Combine checkpoint saving with early stopping to prevent saving checkpoints after the model starts overfitting.

Choosing the Right Method

The custom callback approach (Method 1) is generally preferred for better code organization and maintainability, especially in larger projects. Method 2 is a viable option for simpler projects where embedding the logic within the LightningModule is acceptable.

Conclusion

Saving checkpoints at regular intervals is a vital part of effective deep learning workflows. PyTorch Lightning provides flexible tools to manage this process. By using either a custom callback or modifying the on_train_epoch_end method, you can easily implement checkpoint saving every N epochs, ensuring efficient training and experimentation. Remember to choose the method that best suits your project's complexity and structure, while adhering to best practices for efficient checkpoint management.