Saving and Loading Models in PyTorch: Best Practices

When working with deep learning models in PyTorch, it’s essential to know how to save and load your models efficiently. There are two main ways to do this, but not all methods are recommended. In this quick blog, we’ll explore the different options, explain their differences, and discuss why saving the entire model isn’t ideal.

1. Saving the Entire Model (Not Recommended)

You can save the entire model using PyTorch’s torch.save() method, which serializes the model, including its architecture, weights, and optimizer state.

torch.save(model, 'model.pth')

This method uses Python’s pickleutility under the hood, saving everything in one go. While it seems convenient, it has a downside: loading the model requires the exact same architecture definition and class to be present in your codebase. This makes the saved model less portable and harder to use across different projects or environments.

2. Saving Only the Model Weights (Recommended)

The recommended way to save models in PyTorch is by saving only the model’s learned parameters (weights and biases) using model.state_dict(). This method saves the model's weights as a dictionary, making it easier to load them into any compatible architecture.

torch.save(model.state_dict(), 'model_weights.pth')

With this approach, you can easily load the saved weights into a model with the same architecture, even if the original model class isn’t present in the environment.

Loading the Model

Loading a model depends on how you saved it. Here’s how to load models using both approaches.

Loading the Entire Model

If you saved the entire model, you can load it directly using torch.load():

model = torch.load('model.pth')
print(model)

However, this method is less flexible because you need the original model definition in your code.

Loading Only the Model Weights

To load only the model weights, you need to reconstruct the model architecture first and then load the saved weights:

model = SimpleNet(input_dim=65)  # Recreate the model
model.load_state_dict(torch.load('model_weights.pth'))
print(model)

This method allows for more flexibility since the model architecture can be defined separately.

Why Not Save the Entire Model?

While saving the entire model might seem easier, it comes with some limitations:

• Compatibility issues: You must have the exact model class definition when loading.
• Less flexibility: If you update the model’s architecture later, the saved file becomes harder to reuse.
• Portability: Saved files become less portable across different projects or environments.

Conclusion

For best practices in PyTorch, save the model’s parameters using model.state_dict(). This method ensures more flexibility, better compatibility across environments, and reduces dependency on the original model class when loading.