def load_model(model_path: str,
suppress_warnings: bool = False) -> PyFuncModel:
"""Load a model that has python_function flavor.
Parameters
----------
model_path : str
Filepath of the model directory.
suppress_warnings : bool, optional
If Fatal, non-fatal warning messages associated with the model loading process
will be emitted, by default True
Returns
-------
PyFuncModel
A python_function model.
Raises
------
ClearboxWrapperException
If the model does not have the python_function flavor.
"""
mlmodel = Model.load(os.path.join(model_path, MLMODEL_FILE_NAME))
pyfunc_flavor_configuration = mlmodel.flavors.get(FLAVOR_NAME)
if pyfunc_flavor_configuration is None:
raise ClearboxWrapperException(
'Model does not have the "{flavor_name}" flavor'.format(
flavor_name=FLAVOR_NAME))
model_python_version = pyfunc_flavor_configuration.get(PY_VERSION)
if not suppress_warnings:
_warn_potentially_incompatible_py_version_if_necessary(
model_py_version=model_python_version)
if CODE in pyfunc_flavor_configuration and pyfunc_flavor_configuration[
CODE]:
code_path = os.path.join(model_path, pyfunc_flavor_configuration[CODE])
_add_code_to_system_path(code_path=code_path)
data_path = (os.path.join(model_path, pyfunc_flavor_configuration[DATA]) if
(DATA in pyfunc_flavor_configuration) else model_path)
model_implementation = importlib.import_module(
pyfunc_flavor_configuration[MAIN])._load_pyfunc(data_path)
return PyFuncModel(model_meta=mlmodel, model_impl=model_implementation)
def _get_flavor_configuration(model_path: str, flavor_name: str) -> Dict:
"""Get the configuration for a specified flavor of a model.
Parameters
----------
model_path : str
Path to the model directory.
flavor_name : str
Name of the flavor configuration to load.
Returns
-------
Dict
Flavor configuration as a dictionary.
Raises
------
ClearboxWrapperException
If it couldn't find a MLmodel file or if the model doesn't contain
the specified flavor.
"""
mlmodel_path = os.path.join(model_path, MLMODEL_FILE_NAME)
if not os.path.exists(mlmodel_path):
raise ClearboxWrapperException(
'Could not find an "{}" configuration file at "{}"'.format(
MLMODEL_FILE_NAME, model_path
)
)
mlmodel = Model.load(mlmodel_path)
if flavor_name not in mlmodel.flavors:
raise ClearboxWrapperException(
'Model does not have the "{}" flavor'.format(flavor_name)
)
flavor_configuration_dict = mlmodel.flavors[flavor_name]
return flavor_configuration_dict
def load_model(model_path: str,
suppress_warnings: bool = False) -> WrapperModel:
"""Load a model that has python_function flavor.
Parameters
----------
model_path : str
Filepath of the model directory.
suppress_warnings : bool, optional
If Fatal, non-fatal warning messages associated with the model loading process
will be emitted, by default True
Returns
-------
PyFuncModel
A python_function model.
Raises
------
ClearboxWrapperException
If the model does not have the python_function flavor.
"""
preprocessing = None
data_preparation = None
mlmodel = Model.load(os.path.join(model_path, MLMODEL_FILE_NAME))
clearbox_flavor_configuration = mlmodel.flavors.get(FLAVOR_NAME)
if clearbox_flavor_configuration is None:
raise ClearboxWrapperException(
'Model does not have the "{flavor_name}" flavor'.format(
flavor_name=FLAVOR_NAME))
model_python_version = clearbox_flavor_configuration.get(PY_VERSION)
if not suppress_warnings:
_warn_potentially_incompatible_py_version_if_necessary(
model_py_version=model_python_version)
data_path = (os.path.join(model_path, clearbox_flavor_configuration[DATA])
if (DATA in clearbox_flavor_configuration) else model_path)
model_implementation = importlib.import_module(
clearbox_flavor_configuration[MAIN])._load_clearbox(data_path)
if PREPROCESSING in clearbox_flavor_configuration:
preprocessing_path = os.path.join(
model_path, clearbox_flavor_configuration[PREPROCESSING])
preprocessing = load_serialized_preprocessing(preprocessing_path)
if DATA_PREPARATION in clearbox_flavor_configuration:
data_preparation_path = os.path.join(
model_path, clearbox_flavor_configuration[DATA_PREPARATION])
data_preparation = load_serialized_data_preparation(
data_preparation_path)
loaded_model = WrapperModel(
model_meta=mlmodel,
model_impl=model_implementation,
preprocessing=preprocessing,
data_preparation=data_preparation,
)
return loaded_model