improve e2e lambda test, fix lambda deployment bug (#449)

* small fix * remove unused import * add notes
bentoml · Dec 20, 2019 · 87bf227 · 87bf227
1 parent 104698d
commit 87bf227
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Showing 2 changed files with 36 additions and 20 deletions.
diff --git a/bentoml/deployment/aws_lambda/utils.py b/bentoml/deployment/aws_lambda/utils.py
@@ -393,8 +393,17 @@ def ensure_is_ready_to_deploy_to_cloud_formation(stack_name, region):
                     'Stack "%s" is in a "bad" status(%s), deleting the stack '
                     'before deployment',
                     stack_name,
-                    stack_result['StackStatus']
+                    stack_result['StackStatus'],
                 )
                 cf_client.delete_stack(StackName=stack_name)
     except ClientError as e:
-        raise BentoMLException(str(e))
+        # We are brutally parse and handle stack doesn't exist, since
+        # "AmazonCloudFormationException" currently is not implemented in boto3. Once
+        # the current error is implemented, we need to switch
+        error_response = e.response.get('Error', {})
+        error_code = error_response.get('Code')
+        error_message = error_response.get('Message', 'Unknown')
+        if error_code == 'ValidationError' and 'does not exist' in error_message:
+            pass
+        else:
+            raise BentoMLException(str(e))
diff --git a/scripts/e2e_tests/aws_lambda/e2e_lambda_deployment.py b/scripts/e2e_tests/aws_lambda/e2e_lambda_deployment.py
@@ -3,6 +3,7 @@
 import subprocess
 import logging
 import uuid
+import sys
 
 import requests
 import json
@@ -25,27 +26,33 @@ def predict(self, df):
 
 
 if __name__ == '__main__':
-    logger.info('Training iris classifier with sklearn..')
-    clf = svm.SVC(gamma='scale')
-    iris = datasets.load_iris()
-    X, y = iris.data, iris.target
-    clf.fit(X, y)
-
-    logger.info('Creating iris classifier BentoService bundle..')
-    iris_clf_service = IrisClassifier()
-    iris_clf_service.pack('clf', clf)
-    saved_path = iris_clf_service.save()
-
-    loaded_service = load(saved_path)
-    sample_data = X[0:1]
-
-    logger.info(
-        'Result from sample data is: %s', str(loaded_service.predict(sample_data))
-    )
     deployment_failed = False
-    bento_name = f'{loaded_service.name}:{loaded_service.version}'
     random_hash = uuid.uuid4().hex[:6]
     deployment_name = f'tests-lambda-e2e-{random_hash}'
+
+    args = sys.argv
+    bento_name = None
+    if len(args) > 1:
+        bento_name = args[1]
+    if bento_name is None:
+        logger.info('Training iris classifier with sklearn..')
+        clf = svm.SVC(gamma='scale')
+        iris = datasets.load_iris()
+        X, y = iris.data, iris.target
+        clf.fit(X, y)
+
+        logger.info('Creating iris classifier BentoService bundle..')
+        iris_clf_service = IrisClassifier()
+        iris_clf_service.pack('clf', clf)
+        saved_path = iris_clf_service.save()
+
+        loaded_service = load(saved_path)
+        sample_data = X[0:1]
+
+        logger.info(
+            'Result from sample data is: %s', str(loaded_service.predict(sample_data))
+        )
+        bento_name = f'{loaded_service.name}:{loaded_service.version}'
     create_deployment_command = [
         'bentoml',
         '--verbose',