Saving the Brains of Your Machine Learning Model: A Guide to PyTorch Model Persistence

This is the common approach. While it doesn't capture the entire architecture as code, it stores the essential details. Here's how it works:

This approach is efficient and allows you to load the architecture back along with the weights when needed.

Saving the Entire Model:

PyTorch's torch.save() function can also save the complete model, including both the architecture and the learned weights. This is a single-step solution but can be less flexible compared to the state dictionary method.

Here's a thing to keep in mind: If you plan to modify the model architecture later, saving the state dictionary is better. You can then use it to create a new model with the same architecture but different weights.

import torch

# Define your model architecture (replace this with your actual model class)
class MyModel(torch.nn.Module):
  def __init__(self):
    super(MyModel, self).__init__()
    # ... your model layers here ...

# Create an instance of your model
model = MyModel()

# Train your model (replace this with your training loop)
# ... train the model ...

# Save the model state dictionary
torch.save(model.state_dict(), "saved_model.pth")

import torch

# Define your model architecture (replace this with your actual model class)
class MyModel(torch.nn.Module):
  def __init__(self):
    super(MyModel, self).__init__()
    # ... your model layers here ...

# Create an instance of your model
model = MyModel()

# Train your model (replace this with your training loop)
# ... train the model ...

# Save the entire model (architecture + weights)
torch.save(model, "entire_model.pt")

Remember to replace the MyModel class with your actual model definition.

TorchScript allows you to save your PyTorch model in a format that can be run without the original Python code. This is useful for deploying models in production environments. While TorchScript doesn't directly save the architecture as text, it captures the computational graph defining the model's behavior.

Here's a basic example:

import torch
import torch.jit

# Define your model (same as previous example)
# ...

# Create a model instance
model = MyModel()

# Put the model in evaluation mode (optional for TorchScript)
model.eval()

# Trace the model with some sample input
dummy_input = torch.randn(1, 3, 32, 32)  # Replace with your input shape
traced_model = torch.jit.trace(model, dummy_input)

# Save the traced model
traced_model.save("traced_model.pt")

ONNX (Open Neural Network Exchange):

ONNX is a format for representing deep learning models that can be run by various frameworks. It allows you to export your PyTorch model to a format usable by other tools and platforms. There might be limitations on what kind of model architectures ONNX can represent, so check compatibility before using it.

Here's an example using the torch.onnx package:

import torch
import torch.onnx

# Define your model (same as previous example)
# ...

# Create a model instance
model = MyModel()

# Define dummy input for exporting
dummy_input = torch.randn(1, 3, 32, 32)  # Replace with your input shape

# Export the model to ONNX
torch.onnx.export(model, dummy_input, "model.onnx")