Saving entire model in PyTorch

Introduction

Saving the entire model lets you keep the model's structure and learned knowledge so you can use it later without retraining.

You want to pause training and continue later from the same model.

You finished training and want to share the model with others.

You want to deploy the model to make predictions in a real app.

You want to keep a backup of your model after training.

You want to load the model later to test or improve it.

Syntax

PyTorch

torch.save(model, PATH)

# To load:
model = torch.load(PATH)
model.eval()

PATH is a string with the file name or path where the model is saved.

model.eval() sets the model to evaluation mode, important for layers like dropout or batchnorm.

Examples

Save the entire model to a file named 'model.pth'.

PyTorch

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

Load the saved model from 'model.pth' and set it to evaluation mode.

PyTorch

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

Sample Model

This code trains a simple model for one step, saves the entire model, loads it back, and makes a prediction.

PyTorch

import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple model
class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(2, 1)
    def forward(self, x):
        return self.fc(x)

# Create model instance
model = SimpleNet()

# Dummy input and target
inputs = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
targets = torch.tensor([[1.0], [2.0]])

# Loss and optimizer
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Train for 1 step
model.train()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()

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

# Load model
loaded_model = torch.load(PATH)
loaded_model.eval()

# Predict with loaded model
with torch.no_grad():
    pred = loaded_model(torch.tensor([[5.0, 6.0]]))

print(f"Loss after 1 step: {loss.item():.4f}")
print(f"Prediction for input [5.0, 6.0]: {pred.item():.4f}")

Important Notes

Saving the entire model saves both the architecture and the weights, so you don't need to redefine the model class when loading.

This method can cause issues if you change the model code later, so saving only the state_dict is often safer for long-term projects.

Always call model.eval() before using the loaded model for prediction to get correct results.

Summary

Use torch.save(model, PATH) to save the whole model including structure and weights.

Load the model with torch.load(PATH) and set it to eval mode before prediction.

Saving the entire model is quick and easy but less flexible than saving only weights.

Practice

1. What does torch.save(model, PATH) do in PyTorch?

easy

A. Saves the entire model including its architecture and weights

B. Saves only the model's weights

C. Saves only the model's architecture

D. Saves the training data used for the model

Solution

1. Step 1: Understand torch.save usage

   torch.save(model, PATH) saves the whole model object, which includes both architecture and weights.

2. Step 2: Differentiate from saving weights only

   Saving only weights uses model.state_dict(), but here the entire model is saved.

3. Final Answer:

   Saves the entire model including its architecture and weights -> Option A

4. Quick Check:

   torch.save(model, PATH) saves full model [OK]

Hint: Remember torch.save(model, PATH) saves full model [OK]

Common Mistakes:

• Confusing saving weights only with saving entire model
• Thinking it saves training data
• Assuming it saves only architecture

2. Which of the following is the correct syntax to save an entire PyTorch model to a file named model.pth?

easy

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

B. model.save('model.pth')

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

D. model.save_state('model.pth')

Solution

1. Step 1: Identify correct torch.save usage

   To save the entire model, use torch.save(model, 'model.pth').

2. Step 2: Differentiate from saving weights only

   model.state_dict() saves only weights, so torch.save(model.state_dict(), 'model.pth') is incorrect for entire model.

3. Final Answer:

   torch.save(model, 'model.pth') -> Option C

4. Quick Check:

   torch.save(model, PATH) saves full model [OK]

Hint: Use torch.save(model, PATH) to save entire model [OK]

Common Mistakes:

• Using model.state_dict() when saving entire model
• Calling non-existent model.save() method
• Confusing syntax with other frameworks

3. Consider this code snippet:

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(2, 1)

    def forward(self, x):
        return self.fc(x)

model = SimpleNet()
torch.save(model, 'model.pth')
loaded_model = torch.load('model.pth')
loaded_model.eval()

input_tensor = torch.tensor([[1.0, 2.0]])
output = loaded_model(input_tensor).item()
print(round(output, 2))

What will be printed?

medium

A. A number close to 0.0 (random weights)

B. An error because model.eval() is missing

C. A tensor object instead of a number

D. An error because torch.load cannot load entire model

Solution

1. Step 1: Understand model saving and loading

   The entire model is saved and loaded correctly with torch.save and torch.load. Calling eval() sets model to evaluation mode.

2. Step 2: Predict output value type

   Since weights are random (not trained), output will be a float number close to 0.0. The print rounds it to 2 decimals.

3. Final Answer:

   A number close to 0.0 (random weights) -> Option A

4. Quick Check:

   Loaded model outputs float with random weights [OK]

Hint: Loaded model outputs float with random weights [OK]

Common Mistakes:

• Expecting trained output without training
• Thinking eval() is mandatory to avoid error
• Confusing tensor output with float

4. You saved your entire model using torch.save(model, 'model.pth'). When loading with loaded_model = torch.load('model.pth'), you get an error: AttributeError: Can't get attribute 'SimpleNet'. What is the likely cause?

medium

A. The file 'model.pth' is corrupted

B. The model class SimpleNet is not defined or imported before loading

C. You must use model.load_state_dict() instead of torch.load

D. The model was saved incorrectly with torch.save(model.state_dict())

Solution

1. Step 1: Understand how torch.load works with entire models

   Loading entire models requires the model class definition to be available in the current scope.

2. Step 2: Identify cause of AttributeError

   The error means Python cannot find the class SimpleNet, so it must be defined or imported before loading.

3. Final Answer:

   The model class SimpleNet is not defined or imported before loading -> Option B

4. Quick Check:

   Model class must be defined before torch.load [OK]

Hint: Define model class before loading entire model [OK]

Common Mistakes:

• Assuming torch.load works without class definition
• Confusing state_dict loading with entire model loading
• Thinking file corruption causes this error

5. You want to save a PyTorch model so that it can be loaded later without needing the original model class code. Which approach is best?

hard

A. Save the model architecture as JSON and weights separately

B. Save only the model weights with torch.save(model.state_dict(), PATH) and recreate the model class before loading

C. Save the entire model using torch.save(model, PATH) and load with torch.load(PATH)

D. Export the model to ONNX format for framework-independent loading

Solution

1. Step 1: Understand limitations of saving entire model

   Saving entire model requires the original class code to load, so it is not independent.

2. Step 2: Identify framework-independent saving method

   Exporting to ONNX format allows loading the model in other frameworks without original class code.

3. Final Answer:

   Export the model to ONNX format for framework-independent loading -> Option D

4. Quick Check:

   ONNX export enables class-free model loading [OK]

Hint: Use ONNX export for class-free model loading [OK]

Common Mistakes:

• Thinking torch.save saves model independent of class code
• Assuming JSON saves PyTorch model architecture
• Confusing state_dict saving with full model saving