Save Entire Model vs State Dict in PyTorch: Key Differences and Usage

In PyTorch, torch.save(model) saves the entire model including architecture and weights, while torch.save(model.state_dict()) saves only the model's learned parameters. Saving the state dict is more flexible and recommended for most cases, as it requires the model class to be defined when loading.

Quick Comparison

This table summarizes the main differences between saving the entire model and saving only the state dict in PyTorch.

Aspect	Save Entire Model	Save State Dict
What is saved	Full model object (architecture + weights)	Only model parameters (weights and biases)
File size	Larger, includes model code	Smaller, only parameters
Loading requirement	No need to redefine model class	Model class must be defined before loading
Flexibility	Less flexible, tied to exact code	More flexible, can load parameters into compatible model
Recommended use	Quick experiments or exact replication	Production and sharing models
Potential issues	May break if code changes	Safe across code updates if architecture matches

Key Differences

Saving the entire model with torch.save(model) stores the complete model object, including its architecture and learned parameters. This means you can load it back without redefining the model class. However, this method depends on the exact code and environment, so it can break if the model class code changes or if you load it in a different PyTorch version.

On the other hand, saving only the state_dict with torch.save(model.state_dict()) stores just the parameters like weights and biases. To load these parameters, you must first create an instance of the model class and then load the state dict into it. This approach is more flexible and robust, especially when sharing models or updating code, because the architecture is defined explicitly in code and parameters are loaded separately.

In summary, saving the entire model is simpler but less portable, while saving the state dict is the recommended best practice for most real-world applications.

Code Comparison

Here is how to save and load the entire model in PyTorch.

python

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(2, 1)
    def forward(self, x):
        return self.linear(x)

model = SimpleModel()

# Save entire model
torch.save(model, 'entire_model.pth')

# Load entire model
loaded_model = torch.load('entire_model.pth')

# Test prediction
input_tensor = torch.tensor([[1.0, 2.0]])
output = loaded_model(input_tensor)
print(output)

Output

tensor([[value]], grad_fn=<AddmmBackward0>)

State Dict Equivalent

Here is how to save and load only the state dict in PyTorch.

python

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(2, 1)
    def forward(self, x):
        return self.linear(x)

model = SimpleModel()

# Save state dict
torch.save(model.state_dict(), 'state_dict.pth')

# Load state dict
loaded_model = SimpleModel()  # Must create model instance first
loaded_model.load_state_dict(torch.load('state_dict.pth'))
loaded_model.eval()

# Test prediction
input_tensor = torch.tensor([[1.0, 2.0]])
output = loaded_model(input_tensor)
print(output)

Output

tensor([[value]], grad_fn=<AddmmBackward0>)

When to Use Which

Choose saving the entire model when you want a quick save and load cycle without redefining the model class, such as during rapid prototyping or experiments where code stability is guaranteed.

Choose saving the state dict for production, sharing models, or when you expect to update or modify the model code. This method is more robust, flexible, and the recommended best practice in PyTorch.

Key Takeaways

Saving the entire model stores architecture and weights but is less flexible and can break if code changes.

Saving the state dict stores only parameters and requires model class definition when loading.

State dict saving is smaller in size and recommended for production and sharing.

Use entire model saving for quick experiments with stable code.

Use state dict saving for robust, flexible, and maintainable model workflows.