change: removing unnecessary tests cases

tests/integ/test_horovod.py

@@ -23,23 +23,12 @@
 import sagemaker.utils
 import tests.integ as integ
 from sagemaker.tensorflow import TensorFlow
-from tests.integ import test_region, timeout, HOSTING_NO_P3_REGIONS
+from tests.integ import timeout
 
 horovod_dir = os.path.join(os.path.dirname(__file__), "..", "data", "horovod")
 
 
-@pytest.fixture(
-    scope="session",
-    params=[
-        "ml.c4.xlarge",
-        pytest.param(
-            "ml.p3.2xlarge",
-            marks=pytest.mark.skipif(
-                test_region() in HOSTING_NO_P3_REGIONS, reason="no ml.p3 instances in this region"
-            ),
-        ),
-    ],
-)
+@pytest.fixture(scope="session", params=["ml.c4.xlarge"])
 def instance_type(request):
     return request.param
 

tests/integ/test_pytorch_train.py

@@ -13,11 +13,9 @@
 from __future__ import absolute_import
 
 import os
-import time
 
 import numpy
 import pytest
-import tests.integ
 from tests.integ import DATA_DIR, PYTHON_VERSION, TRAINING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
@@ -80,42 +78,6 @@ def test_deploy_model(pytorch_training_job, sagemaker_session):
         assert output.shape == (batch_size, 10)
 
 
-@pytest.mark.skipif(
-    tests.integ.test_region() in tests.integ.HOSTING_NO_P2_REGIONS
-    or tests.integ.test_region() in tests.integ.TRAINING_NO_P2_REGIONS,
-    reason="no ml.p2 instances in these regions",
-)
-def test_async_fit_deploy(sagemaker_session, pytorch_full_version):
-    training_job_name = ""
-    # TODO: add tests against local mode when it's ready to be used
-    instance_type = "ml.p2.xlarge"
-
-    with timeout(minutes=10):
-        pytorch = _get_pytorch_estimator(sagemaker_session, pytorch_full_version, instance_type)
-
-        pytorch.fit({"training": _upload_training_data(pytorch)}, wait=False)
-        training_job_name = pytorch.latest_training_job.name
-
-        print("Waiting to re-attach to the training job: %s" % training_job_name)
-        time.sleep(20)
-
-    if not _is_local_mode(instance_type):
-        endpoint_name = "test-pytorch-async-fit-attach-deploy-{}".format(sagemaker_timestamp())
-
-        with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
-            print("Re-attaching now to: %s" % training_job_name)
-            estimator = PyTorch.attach(
-                training_job_name=training_job_name, sagemaker_session=sagemaker_session
-            )
-            predictor = estimator.deploy(1, instance_type, endpoint_name=endpoint_name)
-
-            batch_size = 100
-            data = numpy.random.rand(batch_size, 1, 28, 28).astype(numpy.float32)
-            output = predictor.predict(data)
-
-            assert output.shape == (batch_size, 10)
-
-
 def _upload_training_data(pytorch):
     return pytorch.sagemaker_session.upload_data(
         path=os.path.join(MNIST_DIR, "training"),

tests/integ/test_tf_keras.py

@@ -28,10 +28,6 @@
 @pytest.mark.skipif(
     tests.integ.PYTHON_VERSION != "py2", reason="TensorFlow image supports only python 2."
 )
-@pytest.mark.skipif(
-    tests.integ.test_region() in tests.integ.HOSTING_NO_P2_REGIONS,
-    reason="no ml.p2 instances in these regions",
-)
 def test_keras(sagemaker_session):
     script_path = os.path.join(tests.integ.DATA_DIR, "cifar_10", "source")
     dataset_path = os.path.join(tests.integ.DATA_DIR, "cifar_10", "data")
@@ -60,7 +56,7 @@ def test_keras(sagemaker_session):
 
     endpoint_name = estimator.latest_training_job.name
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
-        predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.p2.xlarge")
+        predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.c4.xlarge")
 
         data = np.random.randn(32, 32, 3)
         predict_response = predictor.predict(data)

tests/integ/test_tf_script_mode.py

@@ -38,20 +38,7 @@
 TAGS = [{"Key": "some-key", "Value": "some-value"}]
 
 
-@pytest.fixture(
-    scope="session",
-    params=[
-        "ml.c4.xlarge",
-        pytest.param(
-            "ml.p2.xlarge",
-            marks=pytest.mark.skipif(
-                tests.integ.test_region() in tests.integ.HOSTING_NO_P2_REGIONS
-                or tests.integ.test_region() in tests.integ.TRAINING_NO_P2_REGIONS,
-                reason="no ml.p2 instances in this region",
-            ),
-        ),
-    ],
-)
+@pytest.fixture(scope="session", params=["ml.c4.xlarge"])
 def instance_type(request):
     return request.param
 
@@ -228,8 +215,15 @@ def _assert_s3_files_exist(s3_url, files):
             raise ValueError("File {} is not found under {}".format(f, s3_url))
 
 
-def _assert_tags_match(sagemaker_client, resource_arn, tags):
-    actual_tags = sagemaker_client.list_tags(ResourceArn=resource_arn)["Tags"]
+def _assert_tags_match(sagemaker_client, resource_arn, tags, retries=1):
+    actual_tags = None
+    for _ in range(retries):
+        actual_tags = sagemaker_client.list_tags(ResourceArn=resource_arn)["Tags"]
+        if actual_tags:
+            break
+        else:
+            # endpoint tags might take minutes to propagate. Sleeping.
+            time.sleep(30)
     assert actual_tags == tags
 
 
@@ -240,7 +234,8 @@ def _assert_model_tags_match(sagemaker_client, model_name, tags):
 
 def _assert_endpoint_tags_match(sagemaker_client, endpoint_name, tags):
     endpoint_description = sagemaker_client.describe_endpoint(EndpointName=endpoint_name)
-    _assert_tags_match(sagemaker_client, endpoint_description["EndpointArn"], tags)
+
+    _assert_tags_match(sagemaker_client, endpoint_description["EndpointArn"], tags, retries=10)
 
 
 def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):