feat: Add garak benchmark tests for EvalHub with KFP provider

tests/fixtures/inference.py

@@ -21,6 +21,7 @@
     KServeDeploymentType,
     LLMdInferenceSimConfig,
     RuntimeTemplates,
+    Timeout,
     VLLMGPUConfig,
 )
 from utilities.inference_utils import create_isvc
@@ -197,7 +198,7 @@ def llm_d_inference_sim_isvc(
             deployment_mode=KServeDeploymentType.RAW_DEPLOYMENT,
             model_format=LLMdInferenceSimConfig.name,
             runtime=llm_d_inference_sim_serving_runtime.name,
-            wait_for_predictor_pods=True,
+            wait_for_predictor_pods=False,
             min_replicas=1,
             max_replicas=1,
             resources={
@@ -206,6 +207,12 @@ def llm_d_inference_sim_isvc(
             },
             teardown=teardown_resources,
         ) as isvc:
+            deployment = Deployment(
+                client=admin_client,
+                name=f"{isvc.name}-predictor",
+                namespace=model_namespace.name,
+            )
+            deployment.wait_for_replicas(timeout=Timeout.TIMEOUT_2MIN)
             yield isvc
 
 
@@ -326,3 +333,33 @@ def get_vllm_chat_config(namespace: str) -> dict[str, Any]:
             "port": VLLMGPUConfig.port,
         }
     }
+
+
+@pytest.fixture(scope="class")
+def patched_dsc_garak_kfp(admin_client) -> Generator[DataScienceCluster]:
+    """Configure the DataScienceCluster for Garak and KFP (Kubeflow Pipelines) testing.
+
+    This fixture patches the DataScienceCluster to enable:
+        - KServe in Headed mode (using Service port instead of Pod port)
+        - AI Pipelines component in Managed state
+        - MLflow operator in Managed state
+
+    Waits for the DSC to be ready before yielding.
+    """
+
+    dsc = get_data_science_cluster(client=admin_client)
+    with ResourceEditor(
+        patches={
+            dsc: {
+                "spec": {
+                    "components": {
+                        "kserve": {"rawDeploymentServiceConfig": "Headed"},
+                        "aipipelines": {"managementState": "Managed"},
+                        "mlflowoperator": {"managementState": "Managed"},
+                    }
+                }
+            }
+        }
+    ):
+        wait_for_dsc_status_ready(dsc_resource=dsc)
+        yield dsc