Redoing everything seemed to help, the key is to make sure of consistency, same vocab, same encoder etc.,
I’m able to deploy on a separate machine, but inference takes a mighty long time – I’m using learn.save() as opposed to learn.export(). I was getting an error similar to here. The suggested solution was to upgrade the library, which I did ( now at fastai2 (0.0.16) and fastcore (0.1.16) ).
I now get this error instead:
learn.export()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-fa5b61306ef3> in <module>
----> 1 learn.export()
~/.local/lib/python3.6/site-packages/fastai2/learner.py in export(self, fname, pickle_protocol)
497 #To avoid the warning that come from PyTorch about model not being checked
498 warnings.simplefilter("ignore")
--> 499 torch.save(self, self.path/fname, pickle_protocol=pickle_protocol)
500 self.create_opt()
501 if state is not None: self.opt.load_state_dict(state)
~/.local/lib/python3.6/site-packages/torch/serialization.py in save(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)
368
369 with _open_file_like(f, 'wb') as opened_file:
--> 370 _legacy_save(obj, opened_file, pickle_module, pickle_protocol)
371
372
~/.local/lib/python3.6/site-packages/torch/serialization.py in _legacy_save(obj, f, pickle_module, pickle_protocol)
441 pickler = pickle_module.Pickler(f, protocol=pickle_protocol)
442 pickler.persistent_id = persistent_id
--> 443 pickler.dump(obj)
444
445 serialized_storage_keys = sorted(serialized_storages.keys())
TypeError: can't pickle SwigPyObject objects
How can I address this?