def save(self, dst):
# save custom_objects for model
cloudpickle.dump(self._custom_objects,
open(self.spec._custom_objects_path(dst), "wb"))
# save keras model
self._model.save(self.spec._model_file_path(dst))
def save(self, dst):
# save the keras module name to be used when loading
with open(self.spec._keras_module_name_path(dst), "wb") as text_file:
text_file.write(self.spec._keras_module_name.encode("utf-8"))
# save custom_objects for model
cloudpickle.dump(self._custom_objects,
open(self.spec._custom_objects_path(dst), "wb"))
# save keras model using json and weights if requested
if self.spec._store_as_json_and_weights:
with open(self.spec._model_json_path(dst), "w") as json_file:
json_file.write(self._model.to_json())
self._model.save_weights(self.spec._model_weights_path(dst))
# otherwise, save standard keras model
else:
self._model.save(self.spec._model_file_path(dst))
def save(self, dst):
try:
import torch
except ImportError:
raise MissingDependencyException(
"torch package is required to use PytorchModelArtifact")
# If model is a TorchScriptModule, we cannot apply standard pickling
if isinstance(self._model, torch.jit.ScriptModule):
return torch.jit.save(self._model, self._file_path(dst))
return cloudpickle.dump(self._model, open(self._file_path(dst), "wb"))
def save(self, dst):
return cloudpickle.dump(self._model,
open(self.spec._file_path(dst), "wb"))
