change: refactor tests to use common retry method

Author: laurenyu

tests/integ/file_system_input_utils.py

@@ -18,12 +18,12 @@
 from os import path
 import stat
 import tempfile
-import time
 import uuid
 
 from botocore.exceptions import ClientError
 from fabric import Connection
 
+from tests.integ.retry import retries
 from tests.integ.vpc_test_utils import check_or_create_vpc_resources_efs_fsx
 
 VPC_NAME = "sagemaker-efs-fsx-vpc"
@@ -36,7 +36,6 @@
 AMI_ID = "ami-082b5a644766e0e6f"
 MIN_COUNT = 1
 MAX_COUNT = 1
-TIME_SLEEP_DURATION = 10
 
 RESOURCE_PATH = os.path.join(os.path.dirname(__file__), "..", "data")
 MNIST_RESOURCE_PATH = os.path.join(RESOURCE_PATH, "tensorflow_mnist")
@@ -307,21 +306,6 @@ def _instance_profile_exists(sagemaker_session):
     return True
 
 
-def retries(max_retry_count, exception_message_prefix):
-    current_retry_count = 0
-    while current_retry_count <= max_retry_count:
-        yield current_retry_count
-
-        current_retry_count += 1
-        time.sleep(TIME_SLEEP_DURATION)
-
-    raise Exception(
-        "{} has reached the maximum retry count {}".format(
-            exception_message_prefix, max_retry_count
-        )
-    )
-
-
 def tear_down(sagemaker_session, fs_resources):
     fsx_client = sagemaker_session.boto_session.client("fsx")
     file_system_fsx_id = fs_resources.file_system_fsx_id

tests/integ/test_inference_pipeline.py

@@ -1,4 +1,4 @@
-# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2017-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"). You
 # may not use this file except in compliance with the License. A copy of
@@ -14,7 +14,6 @@
 
 import json
 import os
-import time
 
 import pytest
 from tests.integ import DATA_DIR, TRANSFORM_DEFAULT_TIMEOUT_MINUTES
@@ -30,6 +29,7 @@
 from sagemaker.predictor import RealTimePredictor, json_serializer
 from sagemaker.sparkml.model import SparkMLModel
 from sagemaker.utils import sagemaker_timestamp
+from tests.integ.retry import retries
 
 SPARKML_DATA_PATH = os.path.join(DATA_DIR, "sparkml_model")
 XGBOOST_DATA_PATH = os.path.join(DATA_DIR, "xgboost_model")
@@ -190,16 +190,11 @@ def test_inference_pipeline_model_deploy_with_update_endpoint(
         model.deploy(1, cpu_instance_type, update_endpoint=True, endpoint_name=endpoint_name)
 
         # Wait for endpoint to finish updating
-        max_retry_count = 40  # Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
-        current_retry_count = 0
-        while current_retry_count <= max_retry_count:
-            if current_retry_count >= max_retry_count:
-                raise Exception("Endpoint status not 'InService' within expected timeout.")
-            time.sleep(30)
+        # Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
+        for _ in retries(40, "Waiting for 'InService' endpoint status", seconds_to_sleep=30):
             new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
                 EndpointName=endpoint_name
             )
-            current_retry_count += 1
             if new_endpoint["EndpointStatus"] == "InService":
                 break
 

tests/integ/test_mxnet_train.py

@@ -1,4 +1,4 @@
-# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2017-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"). You
 # may not use this file except in compliance with the License. A copy of
@@ -24,6 +24,7 @@
 from sagemaker.utils import sagemaker_timestamp
 from tests.integ import DATA_DIR, PYTHON_VERSION, TRAINING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.kms_utils import get_or_create_kms_key
+from tests.integ.retry import retries
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
 
@@ -182,16 +183,11 @@ def test_deploy_model_with_update_endpoint(
         model.deploy(1, cpu_instance_type, update_endpoint=True, endpoint_name=endpoint_name)
 
         # Wait for endpoint to finish updating
-        max_retry_count = 40  # Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
-        current_retry_count = 0
-        while current_retry_count <= max_retry_count:
-            if current_retry_count >= max_retry_count:
-                raise Exception("Endpoint status not 'InService' within expected timeout.")
-            time.sleep(30)
+        # Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
+        for _ in retries(40, "Waiting for 'InService' endpoint status", seconds_to_sleep=30):
             new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
                 EndpointName=endpoint_name
             )
-            current_retry_count += 1
             if new_endpoint["EndpointStatus"] == "InService":
                 break
 

tests/integ/test_tf_script_mode.py

@@ -23,6 +23,7 @@
 
 import tests.integ
 from tests.integ import timeout
+from tests.integ.retry import retries
 from tests.integ.s3_utils import assert_s3_files_exist
 
 ROLE = "SageMakerRole"
@@ -199,15 +200,13 @@ def test_deploy_with_input_handlers(sagemaker_session, instance_type):
         assert expected_result == result
 
 
-def _assert_tags_match(sagemaker_client, resource_arn, tags, retries=15):
-    actual_tags = None
-    for _ in range(retries):
+def _assert_tags_match(sagemaker_client, resource_arn, tags, retry_count=15):
+    # endpoint and training tags might take minutes to propagate.
+    for _ in retries(retry_count, "Getting endpoint tags", seconds_to_sleep=30):
         actual_tags = sagemaker_client.list_tags(ResourceArn=resource_arn)["Tags"]
         if actual_tags:
             break
-        else:
-            # endpoint and training tags might take minutes to propagate. Sleeping.
-            time.sleep(30)
+
     assert actual_tags == tags