initialize endpoint config and model names within predictor

Author: Chuyang Deng

CHANGELOG.rst

@@ -6,6 +6,8 @@ CHANGELOG
 ==========
 
 * doc-fix: Remove incorrect parameter for EI TFS Python README
+* feature: ``Predictor``: delete SageMaker model
+* feature: ``Pipeline``: delete SageMaker model
 
 1.18.3.post1
 ============

src/sagemaker/predictor.py

@@ -133,11 +133,15 @@ def delete_model(self):
         for model_name in self._model_names:
             self.sagemaker_session.delete_model(model_name)
 
-    def _get_model_names(self):
+    def _get_endpoint_config_desc(self):
         endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=self.endpoint)
         self._endpoint_config_name = endpoint_desc['EndpointConfigName']
         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)