change: remove unnecessary P3 tests from TFS integration tests (#885)

Author: mvsusp

tests/integ/test_tfs.py

@@ -26,25 +26,8 @@
 from sagemaker.tensorflow.serving import Model, Predictor
 
 
-@pytest.fixture(
-    scope="session",
-    params=[
-        "ml.c5.xlarge",
-        pytest.param(
-            "ml.p3.2xlarge",
-            marks=pytest.mark.skipif(
-                tests.integ.test_region() in tests.integ.HOSTING_NO_P3_REGIONS,
-                reason="no ml.p3 instances in this region",
-            ),
-        ),
-    ],
-)
-def instance_type(request):
-    return request.param
-
-
 @pytest.fixture(scope="module")
-def tfs_predictor(instance_type, sagemaker_session, tf_full_version):
+def tfs_predictor(sagemaker_session, tf_full_version):
     endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
     model_data = sagemaker_session.upload_data(
         path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
@@ -57,7 +40,7 @@ def tfs_predictor(instance_type, sagemaker_session, tf_full_version):
             framework_version=tf_full_version,
             sagemaker_session=sagemaker_session,
         )
-        predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
+        predictor = model.deploy(1, "ml.c5.xlarge", endpoint_name=endpoint_name)
         yield predictor
 
 
@@ -130,8 +113,6 @@ def tfs_predictor_with_model_and_entry_point_and_dependencies(
 @pytest.fixture(scope="module")
 def tfs_predictor_with_accelerator(sagemaker_session, tf_full_version):
     endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
-    instance_type = "ml.c4.large"
-    accelerator_type = "ml.eia1.medium"
     model_data = sagemaker_session.upload_data(
         path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
         key_prefix="tensorflow-serving/models",
@@ -144,13 +125,13 @@ def tfs_predictor_with_accelerator(sagemaker_session, tf_full_version):
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(
-            1, instance_type, endpoint_name=endpoint_name, accelerator_type=accelerator_type
+            1, "ml.c4.large", endpoint_name=endpoint_name, accelerator_type="ml.eia1.medium"
         )
         yield predictor
 
 
 @pytest.mark.canary_quick
-def test_predict(tfs_predictor, instance_type):  # pylint: disable=W0613
+def test_predict(tfs_predictor):  # pylint: disable=W0613
     input_data = {"instances": [1.0, 2.0, 5.0]}
     expected_result = {"predictions": [3.5, 4.0, 5.5]}