Add test for xgboost

testing/resources/iris-xgboost-v2.yaml

@@ -0,0 +1,15 @@
+apiVersion: machinelearning.seldon.io/v1alpha2
+kind: SeldonDeployment
+metadata:
+  name: xgboost
+spec:
+  name: iris
+  protocol: kfserving
+  predictors:
+  - graph:
+      children: []
+      implementation: XGBOOST_SERVER
+      modelUri: gs://seldon-models/xgboost/iris
+      name: classifier
+    name: default
+    replicas: 1

testing/scripts/test_prepackaged_servers.py

@@ -13,7 +13,7 @@
     rest_request,
     log_sdep_logs,
 )
-from e2e_utils.v2_protocol import inference_request
+from e2e_utils import v2_protocol
 from conftest import SELDON_E2E_TESTS_USE_EXECUTOR
 
 skipif_engine = pytest.mark.skipif(
@@ -57,7 +57,7 @@ def test_sklearn_v2(self, namespace):
         time.sleep(1)
 
         logging.warning("Initial request")
-        r = inference_request(
+        r = v2_protocol.inference_request(
             model_name="sklearn",
             namespace=namespace,
             payload={
@@ -117,6 +117,32 @@ def test_xgboost(self, namespace):
         logging.warning("Success for test_prepack_xgboost")
         run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
 
+    @skipif_engine
+    def test_xgboost_v2(self, namespace):
+        spec = "../resources/iris-xgboost-v2.yaml"
+        retry_run(f"kubectl apply -f {spec} -n {namespace}")
+        wait_for_status("xgboost", namespace)
+        wait_for_rollout("xgboost", namespace)
+        time.sleep(1)
+
+        logging.warning("Initial request")
+        r = v2_protocol.inference_request(
+            model_name="xgboost",
+            namespace=namespace,
+            payload={
+                "inputs": [
+                    {
+                        "name": "input-0",
+                        "shape": [4],
+                        "datatype": "FP32",
+                        "data": [[0.1, 0.2, 0.3, 0.4]],
+                    }
+                ],
+            },
+        )
+        assert r.status_code == 200
+        run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
+
     # Test prepackaged server for MLflow
     @skipif_engine
     def test_mlflow(self, namespace):