def save_model(python_model,
path='./model/',
conda_env=None,
dependencies=[],
github=None,
module_path=None,
model_class=None):
"""
Save a generic python model to a path on the local file system.
:param python_model: Python model to be saved.
:param path: Path to a directory saving model data.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a conda environment yaml file.
:param dependencies: artifacts to be copied to model path.
"""
if (not path.endswith('/')):
path += '/'
if not os.path.exists(path):
os.makedirs(path)
# call model save function
python_model.save(path)
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)
func = getattr(python_model, 'predict')
if func is None:
raise RuntimeError('Cannot find predict function in model')
args = inspect.getargspec(func).args
if 'self' in args:
args.remove('self')
spec = utils.generate_default_model_spec(FLAVOR_NAME,
MODEL_FILE_NAME,
input_args=args)
pySpec = spec[FLAVOR_NAME]
if github is not None: pySpec["github"] = github
if module_path is not None: pySpec["module_path"] = module_path
if model_class is not None: pySpec["model_class"] = model_class
utils._save_model_spec(path, spec)
utils.generate_ilearner_files(path) # temp solution, to remove later
def save_model(sess,
input_tensor_list,
output_tensor_list,
graph_tags=None,
signature_name=None,
conda_env=None,
path='./model/'):
"""
Save a Tensorflow model to a path on the local file system.
:param sess: Tensorflow session.
:param input_tensor_list: list of input tensors.
:param output_tensor_list: list of output tensors.
:param graph_tags: list of graph tags (optional), if not specified, default its value would be [tf.saved_model.tag_constants.SERVING].
:param signature_name: signature name (optional), if not specified, default its value would be 'signature_name'.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a conda environment yaml file (optional).
:param path: Path to a directory containing model, spec, conda yaml data (optional).
"""
if (not path.endswith('/')):
path += '/'
if not os.path.exists(path):
os.makedirs(path)
if graph_tags == None or len(graph_tags) == 0:
graph_tags = [tf.saved_model.tag_constants.SERVING]
if signature_name is None or signature_name == '':
signature_name = 'signature_name'
model_file_path = 'model' # sub-directory containing the tensorflow model
_save_model(os.path.join(path, model_file_path), sess, input_tensor_list,
output_tensor_list, graph_tags, signature_name)
if conda_env is None:
conda_env = _get_default_conda_env()
utils.save_conda_env(path, conda_env)
_save_model_spec(path, model_file_path, graph_tags, signature_name)
utils.generate_ilearner_files(path) # temp solution, to remove later
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