e 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.

Note

The 1.6 release of PyTorch switched ` torch.save  ` to use a new zipfile-based
file format. ` torch.load  ` still retains the ability to load files in the
old format. If for any reason you want ` torch.save  ` to use the old format,
pass the kwarg ` _use_new_zipfile_serialization=False  ` .

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