class PythonPredictor(BaseLanguagePredictor):
def __init__(self):
super(PythonPredictor, self).__init__()
self._model_adapter = None
self._mlops = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self._model_adapter = PythonModelAdapter(
model_dir=self._custom_model_path, target_type=self._target_type)
sys.path.append(self._custom_model_path)
self._model_adapter.load_custom_hooks()
self._model = self._model_adapter.load_model_from_artifact()
if self._model is None:
raise Exception("Failed to load model")
@property
def supported_payload_formats(self):
return self._model_adapter.supported_payload_formats
def predict(self, input_filename):
kwargs = {}
kwargs[TARGET_TYPE_ARG_KEYWORD] = self._target_type
if self._positive_class_label and self._negative_class_label:
kwargs[
POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self._positive_class_label
kwargs[
NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self._negative_class_label
if self._class_labels:
kwargs[CLASS_LABELS_ARG_KEYWORD] = self._class_labels
start_predict = time.time()
predictions = self._model_adapter.predict(input_filename,
model=self._model,
**kwargs)
end_predict = time.time()
execution_time_ms = (end_predict - start_predict) * 1000
self.monitor(input_filename, predictions, execution_time_ms)
return predictions
def transform(self, input_filename):
return self._model_adapter.transform(input_filename, model=self._model)
def predict_unstructured(self, data, **kwargs):
str_or_tuple = self._model_adapter.predict_unstructured(
model=self._model, data=data, **kwargs)
if isinstance(str_or_tuple, (str, bytes, type(None))):
ret = str_or_tuple, None
elif isinstance(str_or_tuple, tuple):
ret = str_or_tuple
else:
raise DrumCommonException(
"Wrong type returned in unstructured mode: {}".format(
type(str_or_tuple)))
return ret
class PythonPredictor(BaseLanguagePredictor):
def __init__(self):
super(PythonPredictor, self).__init__()
self._model_adapter = None
self._mlops = None
def configure(self, params):
super(PythonPredictor, self).configure(params)
self._model_adapter = PythonModelAdapter(model_dir=self._code_dir,
target_type=self._target_type)
sys.path.append(self._code_dir)
self._model_adapter.load_custom_hooks()
self._model = self._model_adapter.load_model_from_artifact()
if self._model is None:
raise Exception("Failed to load model")
@property
def supported_payload_formats(self):
return self._model_adapter.supported_payload_formats
def model_info(self):
model_info = super(PythonPredictor, self).model_info()
model_info.update(self._model_adapter.model_info())
return model_info
def has_read_input_data_hook(self):
return self._model_adapter.has_read_input_data_hook()
def _predict(self, **kwargs):
kwargs[TARGET_TYPE_ARG_KEYWORD] = self._target_type
if self._positive_class_label is not None and self._negative_class_label is not None:
kwargs[
POSITIVE_CLASS_LABEL_ARG_KEYWORD] = self._positive_class_label
kwargs[
NEGATIVE_CLASS_LABEL_ARG_KEYWORD] = self._negative_class_label
if self._class_labels:
kwargs[CLASS_LABELS_ARG_KEYWORD] = self._class_labels
predictions = self._model_adapter.predict(model=self._model, **kwargs)
return predictions
def transform(self, **kwargs):
return self._model_adapter.transform(model=self._model, **kwargs)
def predict_unstructured(self, data, **kwargs):
str_or_tuple = self._model_adapter.predict_unstructured(
model=self._model, data=data, **kwargs)
if isinstance(str_or_tuple, (str, bytes, type(None))):
ret = str_or_tuple, None
elif isinstance(str_or_tuple, tuple):
ret = str_or_tuple
else:
raise DrumCommonException(
"Wrong type returned in unstructured mode: {}".format(
type(str_or_tuple)))
return ret