Init endpoint_config_name in Predictor's constructor.

Author: Chuyang Deng

src/sagemaker/predictor.py

@@ -56,7 +56,8 @@ def __init__(self, endpoint, sagemaker_session=None, serializer=None, deserializ
         self.deserializer = deserializer
         self.content_type = content_type or getattr(serializer, 'content_type', None)
         self.accept = accept or getattr(deserializer, 'accept', None)
-        self._model_names = self._get_model_names()
+        self._endpoint_config_name = self._get_endpoint_config_name()
+        self._model_names = self._endpoint_config_desc_and_model_names()
 
     def predict(self, data, initial_args=None):
         """Return the inference from the specified endpoint.
@@ -134,15 +135,14 @@ def delete_model(self):
         for model_name in self._model_names:
             self.sagemaker_session.delete_model(model_name)
 
-    def _get_endpoint_config_desc(self):
+    def _get_endpoint_config_name(self):
         endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=self.endpoint)
-        self._endpoint_config_name = endpoint_desc['EndpointConfigName']
+        endpoint_config_name = endpoint_desc['EndpointConfigName']
+        return endpoint_config_name
+
+    def _endpoint_config_desc_and_model_names(self):
         endpoint_config = self.sagemaker_session.sagemaker_client.describe_endpoint_config(
             EndpointConfigName=self._endpoint_config_name)
-        return endpoint_config
-
-    def _get_model_names(self):
-        endpoint_config = self._get_endpoint_config_desc()
         production_variants = endpoint_config['ProductionVariants']
         return map(lambda d: d['ModelName'], production_variants)