I have a trained NTS-NET that uses 108 MB on file storage. In my server I do not have enough space but is only some MB. So I compress “state_dict” using “tar.gz” and I arrive to 100 MB. It i just enought. So to load the model I use the funcion
import pickle
import tarfile

from torch.serialization import _load, _open_zipfile_reader


def torch_load_targz(filep_ath):
    tar = tarfile.open(filep_ath, "r:gz")
    member = tar.getmembers()[0]
    with tar.extractfile(member) as untar:
        with _open_zipfile_reader(untar) as zipfile:
            torch_loaded = _load(zipfile, None, pickle)
    return torch_loaded


if __name__ == '__main__':
    torch_load_targz("../models/nts_net_state.tar.gz")
    # equivalet for torch.load("../models/nts_net_state.pt")  for .tar.gz 

So at the end I read the torch model from tar.gz directly. But in this way the prediction are too slow.
Exist some better solution at this problem?
(I’m using torch-1.4.0, and python 3.6)