Address comments from Lauren

Author: yangaws

src/sagemaker/estimator.py

@@ -282,13 +282,13 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, **kw
     @property
     def model_data(self):
         """str: The model location in S3. Only set if Estimator has been ``fit()``."""
-        try:
+        if self.latest_training_job is not None:
             model_uri = self.sagemaker_session.sagemaker_client.describe_training_job(
                 TrainingJobName=self.latest_training_job.name)['ModelArtifacts']['S3ModelArtifacts']
-        except AttributeError:
+        else:
             logging.warning('No finished training job found associated with this estimator. Please make sure'
                             'this estimator is only used for building workflow config')
-            model_uri = os.path.join(self.output_path, self._current_job_name, 'output/model.tar.gz')
+            model_uri = os.path.join(self.output_path, self._current_job_name, 'output', 'model.tar.gz')
         return model_uri
 
     @abstractmethod

src/sagemaker/workflow/airflow.py

@@ -276,13 +276,13 @@ def tuning_config(tuner, inputs, job_name=None):
 
 
 def prepare_framework_container_def(model, instance_type, s3_operations):
-    """Prepare the framework model container information. Specify related s3 operations for Airflow to perform.
+    """Prepare the framework model container information. Specify related S3 operations for Airflow to perform.
     (Upload source_dir)
 
     Args:
         model (sagemaker.model.FrameworkModel): The framework model
         instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.
-        s3_operations (dict): The dict to specify s3 operations (upload source_dir).
+        s3_operations (dict): The dict to specify S3 operations (upload source_dir).
 
     Returns (dict):
         The container information of this framework model.

tests/unit/test_airflow.py

@@ -736,7 +736,8 @@ def test_model_config_from_amazon_alg_estimator(sagemaker_session):
         train_instance_type='ml.m4.xlarge',
         k=16,
         sample_size=128,
-        predictor_type='regressor')
+        predictor_type='regressor',
+        sagemaker_session=sagemaker_session)
 
     record = amazon_estimator.RecordSet("{{ record }}", 10000, 100, 'S3Prefix')
 
@@ -749,8 +750,7 @@ def test_model_config_from_amazon_alg_estimator(sagemaker_session):
         'PrimaryContainer': {
             'Image': '174872318107.dkr.ecr.us-west-2.amazonaws.com/knn:1',
             'Environment': {},
-            'ModelDataUrl': "s3://sagemaker-us-west-2-389309777320/knn-"
-                            "{{ execution_date.strftime('%Y-%m-%d-%H-%M-%S') }}/output/model.tar.gz"},
+            'ModelDataUrl': "s3://output/knn-{{ execution_date.strftime('%Y-%m-%d-%H-%M-%S') }}/output/model.tar.gz"},
         'ExecutionRoleArn': '{{ role }}'
     }