Added model upload integration test

tests/system/aiplatform/test_model_upload.py

@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import tempfile
+import importlib
+
+from google import auth as google_auth
+from google.cloud import aiplatform
+from google.cloud import storage
+from google.cloud.aiplatform import initializer
+
+# TODO(vinnys): Replace with env var `BUILD_SPECIFIC_GCP_PROJECT` once supported
+_, _TEST_PROJECT = google_auth.default()
+_TEST_LOCATION = "us-central1"
+
+_XGBOOST_MODEL_URI = "gs://ucaip-test-us-central1/models/iris_xgboost/model.bst"
+
+
+class TestModel:
+    def setup_method(self):
+        importlib.reload(initializer)
+        importlib.reload(aiplatform)
+
+    def test_upload_and_deploy_xgboost_model(self):
+        """Upload XGBoost model from local file and deploy it for prediction."""
+
+        aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)
+
+        storage_client = storage.Client(project=_TEST_PROJECT)
+        model_blob = storage.Blob.from_string(
+            uri=_XGBOOST_MODEL_URI, client=storage_client
+        )
+        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,)
+
+        # Currently we need to explicitly specify machine type.
+        # See https://github.com/googleapis/python-aiplatform/issues/773
+        endpoint = model.deploy(machine_type="n1-standard-2")
+        predict_response = endpoint.predict(instances=[[0, 0, 0]])
+        assert len(predict_response.predictions) == 1
+
+        endpoint.delete(force=True)
+        model.delete()