Reverted skips

tests/system/aiplatform/e2e_base.py

@@ -20,7 +20,6 @@
 import os
 import pytest
 import uuid
-import subprocess
 
 from typing import Any, Dict, Generator
 

tests/system/aiplatform/test_dataset.py

@@ -56,7 +56,6 @@
 _TEST_IMAGE_OBJ_DET_IMPORT_SCHEMA = "gs://google-cloud-aiplatform/schema/dataset/ioformat/image_bounding_box_io_format_1.0.0.yaml"
 
 
-@pytest.mark.skip(reason="Debug")
 class TestDataset:
     def setup_method(self):
         importlib.reload(initializer)

tests/system/aiplatform/test_e2e_tabular.py

@@ -46,7 +46,6 @@
 }
 
 
-@pytest.mark.skip(reason="Debug")
 @pytest.mark.usefixtures("prepare_staging_bucket", "delete_staging_bucket")
 class TestEndToEndTabular(e2e_base.TestEndToEnd):
     """End to end system test of the Vertex SDK with tabular data adapted from

tests/system/aiplatform/test_featurestore.py

@@ -46,7 +46,6 @@
 _TEST_MOVIE_AVERAGE_RATING_FEATURE_ID = "average_rating"
 
 
-@pytest.mark.skip(reason="Debug")
 @pytest.mark.usefixtures(
     "prepare_staging_bucket",
     "delete_staging_bucket",
@@ -59,7 +58,8 @@ class TestFeaturestore(e2e_base.TestEndToEnd):
 
     def test_create_get_list_featurestore(self, shared_state):
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         featurestore_id = self._make_display_name(key=_TEST_FEATURESTORE_ID).replace(
@@ -92,7 +92,8 @@ def test_create_get_list_entity_types(self, shared_state):
         featurestore_name = shared_state["featurestore_name"]
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         # Users
@@ -135,7 +136,8 @@ def test_create_get_list_features(self, shared_state):
         user_entity_type_name = shared_state["user_entity_type_name"]
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         # User Features
@@ -165,7 +167,8 @@ def test_create_get_list_features(self, shared_state):
         )
 
         user_liked_genres_feature = user_entity_type.create_feature(
-            feature_id=_TEST_USER_LIKED_GENRES_FEATURE_ID, value_type="STRING_ARRAY",
+            feature_id=_TEST_USER_LIKED_GENRES_FEATURE_ID,
+            value_type="STRING_ARRAY",
         )
         shared_state[
             "user_liked_genres_feature_resource_name"
@@ -199,7 +202,8 @@ def test_ingest_feature_values(self, shared_state, caplog):
         caplog.set_level(logging.INFO)
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         user_entity_type.ingest_from_gcs(
@@ -224,7 +228,8 @@ def test_batch_create_features(self, shared_state):
         movie_entity_type = shared_state["movie_entity_type"]
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         movie_feature_configs = {
@@ -266,13 +271,15 @@ def test_ingest_feature_values_from_df_using_feature_time_column_and_online_read
         caplog.set_level(logging.INFO)
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         read_feature_ids = ["average_rating", "title", "genres"]
 
         movie_entity_views_df_before_ingest = movie_entity_type.read(
-            entity_ids=["movie_01", "movie_02"], feature_ids=read_feature_ids,
+            entity_ids=["movie_01", "movie_02"],
+            feature_ids=read_feature_ids,
         )
         expected_data_before_ingest = [
             {
@@ -330,7 +337,8 @@ def test_ingest_feature_values_from_df_using_feature_time_column_and_online_read
         )
 
         movie_entity_views_df_after_ingest = movie_entity_type.read(
-            entity_ids=["movie_01", "movie_02"], feature_ids=read_feature_ids,
+            entity_ids=["movie_01", "movie_02"],
+            feature_ids=read_feature_ids,
         )
         expected_data_after_ingest = [
             {
@@ -366,7 +374,8 @@ def test_ingest_feature_values_from_df_using_feature_time_datetime_and_online_re
         caplog.set_level(logging.INFO)
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         movies_df = pd.DataFrame(
@@ -406,7 +415,8 @@ def test_ingest_feature_values_from_df_using_feature_time_datetime_and_online_re
         )
 
         movie_entity_views_df_avg_rating = movie_entity_type.read(
-            entity_ids="movie_04", feature_ids="average_rating",
+            entity_ids="movie_04",
+            feature_ids="average_rating",
         )
         expected_data_avg_rating = [
             {"movie_id": "movie_04", "average_rating": 4.6},
@@ -426,7 +436,8 @@ def test_ingest_feature_values_from_df_using_feature_time_datetime_and_online_re
     def test_search_features(self, shared_state):
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         list_searched_features = aiplatform.Feature.search()
@@ -450,7 +461,8 @@ def test_batch_serve_to_df(self, shared_state, caplog):
         ]
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         caplog.set_level(logging.INFO)
@@ -527,7 +539,8 @@ def test_batch_serve_to_gcs(self, shared_state, caplog):
         ]
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         caplog.set_level(logging.INFO)
@@ -577,7 +590,8 @@ def test_batch_serve_to_bq(self, shared_state, caplog):
         ]
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         caplog.set_level(logging.INFO)

tests/system/aiplatform/test_metadata.py

@@ -17,14 +17,13 @@
 
 from google.cloud import aiplatform
 from tests.system.aiplatform import e2e_base
-import pytest
+
 
 PARAMS = {"sdk-param-test-1": 0.1, "sdk-param-test-2": 0.2}
 
 METRICS = {"sdk-metric-test-1": 0.8, "sdk-metric-test-2": 100}
 
 
-@pytest.mark.skip(reason="Debug")
 class TestMetadata(e2e_base.TestEndToEnd):
 
     _temp_prefix = "temp-vertex-sdk-e2e-test"

tests/system/aiplatform/test_model_upload.py

@@ -28,7 +28,6 @@
 _XGBOOST_MODEL_URI = "gs://cloud-samples-data-us-central1/vertex-ai/google-cloud-aiplatform-ci-artifacts/models/iris_xgboost/model.bst"
 
 
-@pytest.mark.skip(reason="Debug")
 @pytest.mark.usefixtures("delete_staging_bucket")
 class TestModel(e2e_base.TestEndToEnd):
 
@@ -38,7 +37,8 @@ def test_upload_and_deploy_xgboost_model(self, shared_state):
         """Upload XGBoost model from local file and deploy it for prediction. Additionally, update model name, description and labels"""
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         storage_client = storage.Client(project=e2e_base._PROJECT)
@@ -48,7 +48,9 @@ def test_upload_and_deploy_xgboost_model(self, shared_state):
         model_path = tempfile.mktemp() + ".my_model.xgb"
         model_blob.download_to_filename(filename=model_path)
 
-        model = aiplatform.Model.upload_xgboost_model_file(model_file_path=model_path,)
+        model = aiplatform.Model.upload_xgboost_model_file(
+            model_file_path=model_path,
+        )
         shared_state["resources"] = [model]
 
         staging_bucket = storage.Blob.from_string(

tests/system/aiplatform/test_project_id_inference.py

@@ -30,7 +30,6 @@
 """
 
 
-@pytest.mark.skip(reason="Debug")
 @pytest.mark.usefixtures("prepare_staging_bucket", "delete_staging_bucket")
 class TestProjectIDInference(e2e_base.TestEndToEnd):
 

tests/system/aiplatform/test_tensorboard.py

@@ -17,24 +17,24 @@
 
 from google.cloud import aiplatform
 from tests.system.aiplatform import e2e_base
-import pytest
 
 
-@pytest.mark.skip(reason="Debug")
 class TestTensorboard(e2e_base.TestEndToEnd):
 
     _temp_prefix = "temp-vertex-sdk-e2e-test"
 
     def test_create_and_get_tensorboard(self, shared_state):
 
         aiplatform.init(
-            project=e2e_base._PROJECT, location=e2e_base._LOCATION,
+            project=e2e_base._PROJECT,
+            location=e2e_base._LOCATION,
         )
 
         display_name = self._make_display_name("tensorboard")
 
         tb = aiplatform.Tensorboard.create(
-            display_name=display_name, create_request_timeout=None,
+            display_name=display_name,
+            create_request_timeout=None,
         )
 
         shared_state["resources"] = [tb]