def save_model(keras_model, path='./model/', conda_env=None):
"""
Save a Keras model to a path on the local file system.
:param keras_model: Keras model to be saved.
:param path: Path to a directory containing model data.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a conda environment yaml file.
"""
if (not path.endswith('/')):
path += '/'
if not os.path.exists(path):
os.makedirs(path)
keras_model.save(os.path.join(path, model_file_name))
if conda_env is None:
conda_env = _get_default_conda_env()
utils.save_conda_env(path, conda_env)
utils.save_model_spec(path, FLAVOR_NAME, model_file_name)
utils.generate_ilearner_files(path) # temp solution, to remove later
def save_model(pytorch_model,
path='./model/',
conda_env=None,
dependencies=[]):
"""
Save a PyTorch model to a path on the local file system.
:param pytorch_model: PyTorch model to be saved.
:param path: Path to a directory containing model data.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a conda environment yaml file.
"""
if (not path.endswith('/')):
path += '/'
if not os.path.exists(path):
os.makedirs(path)
# only save cpu version
_save_model(pytorch_model.to('cpu'), os.path.join(path, MODEL_FILE_NAME))
fn = os.path.join(path, MODEL_FILE_NAME)
print(f'MODEL_FILE: {fn}')
if conda_env is None:
conda_env = _get_default_conda_env()
print(f'path={path}, conda_env={conda_env}')
utils.save_conda_env(path, conda_env)
for dependency in dependencies:
shutil.copy(dependency, path)
forward_func = getattr(pytorch_model, 'forward')
args = inspect.getargspec(forward_func).args
if 'self' in args:
args.remove('self')
utils.save_model_spec(path, FLAVOR_NAME, MODEL_FILE_NAME, input_args=args)
utils.generate_ilearner_files(path) # temp solution, to remove later