PYTHON

pytorch save model

Saving:
	torch.save(model, PATH)


Loading: 
	model = torch.load(PATH)
	model.eval()
    
A common PyTorch convention is to save models using either a .pt or .pth file extension.

save and load model pytorch

torch.save(model.state_dict(), filepath)

#Later to restore:
model.load_state_dict(torch.load(filepath))
model.eval()

saving model in pytorch

torch.save(model.state_dict(), PATH)

save model pytorch

Recommended approach for saving a model
There are two main approaches for serializing and restoring a model.

The first (recommended) saves and loads only the model parameters:

torch.save(the_model.state_dict(), PATH)
Then later:

the_model = TheModelClass(*args, **kwargs)
the_model.load_state_dict(torch.load(PATH))
The second saves and loads the entire model:

torch.save(the_model, PATH)
Then later:

the_model = torch.load(PATH)
However in this case, the serialized data is bound to the specific classes and the exact directory structure used, so it can break in various ways when used in other projects, or after some serious refactors.

save model pytorch

Recommended approach for saving a model
There are two main approaches for serializing and restoring a model.

The first (recommended) saves and loads only the model parameters:

torch.save(the_model.state_dict(), PATH)
Then later:

the_model = TheModelClass(*args, **kwargs)
the_model.load_state_dict(torch.load(PATH))
The second saves and loads the entire model:

torch.save(the_model, PATH)
Then later:

the_model = torch.load(PATH)
However in this case, the serialized data is bound to the specific classes and the exact directory structure used, so it can break in various ways when used in other projects, or after some serious refactors.

pytorch dill model save

import dill

model_copy=dill.dumps(model)
torch.save(model_copy,‘model_ignite_original.pt’)

model1 = torch.load(model_name)
model=dill.loads(model1)

save and load model during training pytorch

state = {
    'epoch': epoch,
    'state_dict': model.state_dict(),
    'optimizer': optimizer.state_dict(),
    ...
}
torch.save(state, filepath)