use oci for multi node test

tests/model_serving/model_server/kserve/multi_node/conftest.py

@@ -6,19 +6,19 @@
 from ocp_resources.inference_service import InferenceService
 from ocp_resources.namespace import Namespace
 from ocp_resources.node import Node
-from ocp_resources.persistent_volume_claim import PersistentVolumeClaim
 from ocp_resources.pod import Pod
 from ocp_resources.resource import ResourceEditor
 from ocp_resources.secret import Secret
 from ocp_resources.serving_runtime import ServingRuntime
 from pytest_testconfig import config as py_config
-from timeout_sampler import TimeoutSampler
 
 from tests.model_serving.model_server.kserve.multi_node.utils import (
     delete_multi_node_pod_by_role,
+    wait_for_vllm_health,
 )
+from timeout_sampler import TimeoutSampler
+
 from utilities.constants import KServeDeploymentType, Labels, Protocols, Timeout, ModelCarImage
-from utilities.general import download_model_data
 from utilities.inference_utils import create_isvc
 from utilities.infra import (
     get_pods_by_isvc_label,
@@ -33,37 +33,20 @@ def nvidia_gpu_nodes(nodes: list[Node]) -> list[Node]:
     return [node for node in nodes if "nvidia.com/gpu.present" in node.labels.keys()]
 
 
+@pytest.fixture(scope="session")
+def max_gpu_per_node(nvidia_gpu_nodes: list[Node]) -> int:
+    return max(
+        (int(node.instance.status.allocatable.get("nvidia.com/gpu", 0)) for node in nvidia_gpu_nodes),
+        default=0,
+    )
+
+
 @pytest.fixture(scope="session")
 def skip_if_no_gpu_nodes(nvidia_gpu_nodes: list[Node]) -> None:
     if len(nvidia_gpu_nodes) < 2:
         pytest.skip("Multi-node tests can only run on a Cluster with at least 2 GPU Worker nodes")
 
 
-@pytest.fixture(scope="class")
-def models_bucket_downloaded_model_data(
-    request: FixtureRequest,
-    admin_client: DynamicClient,
-    unprivileged_model_namespace: Namespace,
-    models_s3_bucket_name: str,
-    model_pvc: PersistentVolumeClaim,
-    aws_secret_access_key: str,
-    aws_access_key_id: str,
-    models_s3_bucket_endpoint: str,
-    models_s3_bucket_region: str,
-) -> str:
-    return download_model_data(
-        client=admin_client,
-        aws_access_key_id=aws_access_key_id,
-        aws_secret_access_key=aws_secret_access_key,
-        model_namespace=unprivileged_model_namespace.name,
-        model_pvc_name=model_pvc.name,
-        bucket_name=models_s3_bucket_name,
-        aws_endpoint_url=models_s3_bucket_endpoint,
-        aws_default_region=models_s3_bucket_region,
-        model_path=request.param["model-dir"],
-    )
-
-
 @pytest.fixture(scope="class")
 def multi_node_serving_runtime(
     request: FixtureRequest,
@@ -86,35 +69,6 @@ def multi_node_inference_service(
     request: FixtureRequest,
     unprivileged_client: DynamicClient,
     multi_node_serving_runtime: ServingRuntime,
-    model_pvc: PersistentVolumeClaim,
-    models_bucket_downloaded_model_data: str,
-) -> Generator[InferenceService, Any, Any]:
-    with create_isvc(
-        client=unprivileged_client,
-        name=request.param["name"],
-        namespace=multi_node_serving_runtime.namespace,
-        runtime=multi_node_serving_runtime.name,
-        storage_uri=f"pvc://{model_pvc.name}/{models_bucket_downloaded_model_data}",
-        model_format=multi_node_serving_runtime.instance.spec.supportedModelFormats[0].name,
-        deployment_mode=KServeDeploymentType.RAW_DEPLOYMENT,
-        autoscaler_mode="external",
-        multi_node_worker_spec={},
-        wait_for_predictor_pods=False,
-    ) as isvc:
-        wait_for_inference_deployment_replicas(
-            client=unprivileged_client,
-            isvc=isvc,
-            expected_num_deployments=2,
-            runtime_name=multi_node_serving_runtime.name,
-        )
-        yield isvc
-
-
-@pytest.fixture(scope="class")
-def multi_node_oci_inference_service(
-    request: FixtureRequest,
-    unprivileged_client: DynamicClient,
-    multi_node_serving_runtime: ServingRuntime,
 ) -> Generator[InferenceService, Any, Any]:
     resources = {
         "requests": {
@@ -136,7 +90,6 @@ def multi_node_oci_inference_service(
         ]
     }
 
-    # NOTE: In KServe v0.15, the autoscaler_mode needs to be updated to "none".
     with create_isvc(
         client=unprivileged_client,
         name=request.param["name"],
@@ -145,11 +98,10 @@ def multi_node_oci_inference_service(
         storage_uri=ModelCarImage.GRANITE_8B_CODE_INSTRUCT,
         model_format=multi_node_serving_runtime.instance.spec.supportedModelFormats[0].name,
         deployment_mode=KServeDeploymentType.RAW_DEPLOYMENT,
-        autoscaler_mode="external",
+        autoscaler_mode="none",
         resources=resources,
         multi_node_worker_spec=worker_resources,
         wait_for_predictor_pods=False,
-        external_route=True,
         timeout=Timeout.TIMEOUT_30MIN,
     ) as isvc:
         wait_for_inference_deployment_replicas(
@@ -177,6 +129,12 @@ def multi_node_predictor_pods_scope_class(
 def patched_multi_node_isvc_external_route(
     multi_node_inference_service: InferenceService,
 ) -> Generator[InferenceService, Any, Any]:
+    multi_node_inference_service.wait_for_condition(
+        condition=multi_node_inference_service.Condition.READY,
+        status=multi_node_inference_service.Condition.Status.TRUE,
+        timeout=Timeout.TIMEOUT_10MIN,
+    )
+
     with ResourceEditor(
         patches={
             multi_node_inference_service: {
@@ -185,7 +143,7 @@ def patched_multi_node_isvc_external_route(
         }
     ):
         for sample in TimeoutSampler(
-            wait_timeout=Timeout.TIMEOUT_1MIN,
+            wait_timeout=Timeout.TIMEOUT_5MIN,
             sleep=1,
             func=lambda: multi_node_inference_service.instance.status,
         ):
@@ -258,3 +216,8 @@ def deleted_multi_node_pod(
         isvc=multi_node_inference_service,
         timeout=Timeout.TIMEOUT_10MIN,
     )
+
+    wait_for_vllm_health(
+        client=unprivileged_client,
+        isvc=multi_node_inference_service,
+    )

tests/model_serving/model_server/kserve/multi_node/test_nvidia_multi_node.py

@@ -14,12 +14,12 @@
     verify_ray_status,
 )
 from tests.model_serving.model_server.utils import verify_inference_response
-from utilities.constants import Labels, Protocols, StorageClassName
+from utilities.constants import Labels, Protocols
 from utilities.manifests.vllm import VLLM_INFERENCE_CONFIG
 
 pytestmark = [
     pytest.mark.rawdeployment,
-    pytest.mark.usefixtures("skip_if_no_gpu_nodes", "skip_if_no_nfs_storage_class"),
+    pytest.mark.usefixtures("skip_if_no_gpu_nodes"),
     pytest.mark.model_server_gpu,
     pytest.mark.multinode,
     pytest.mark.gpu,
@@ -31,16 +31,10 @@
 
 
 @pytest.mark.parametrize(
-    "unprivileged_model_namespace, models_bucket_downloaded_model_data, model_pvc, multi_node_inference_service",
+    "unprivileged_model_namespace, multi_node_inference_service",
     [
         pytest.param(
             {"name": "gpu-multi-node"},
-            {"model-dir": "granite-8b-code-base"},
-            {
-                "access-modes": "ReadWriteMany",
-                "storage-class-name": StorageClassName.NFS,
-                "pvc-size": "40Gi",
-            },
             {"name": "multi-vllm"},
         )
     ],
@@ -52,10 +46,10 @@ def test_multi_node_ray_status(self, multi_node_predictor_pods_scope_class):
         verify_ray_status(pods=multi_node_predictor_pods_scope_class)
 
     def test_multi_node_nvidia_gpu_status(self, multi_node_predictor_pods_scope_class):
-        """Test multi node ray status"""
+        """Test multi node nvidia gpu status"""
         verify_nvidia_gpu_status(pod=multi_node_predictor_pods_scope_class[0])
 
-    def test_multi_node_default_config(self, multi_node_serving_runtime, multi_node_predictor_pods_scope_class):
+    def test_multi_node_default_config(self, multi_node_serving_runtime, multi_node_inference_service):
         """Test multi node inference service with default config"""
         runtime_worker_spec = multi_node_serving_runtime.instance.spec.workerSpec
 
@@ -179,17 +173,22 @@ def test_multi_node_basic_external_inference(self, patched_multi_node_isvc_exter
                 "spec": {
                     "workerSpec": {
                         "pipelineParallelSize": 2,
-                        "tensorParallelSize": 4,
+                        "tensorParallelSize": 2,
                     }
                 }
             })
         ],
         indirect=True,
     )
-    def test_multi_node_tensor_parallel_size_propagation(self, unprivileged_client, patched_multi_node_spec):
+    def test_multi_node_tensor_parallel_size_propagation(
+        self, unprivileged_client, patched_multi_node_spec, max_gpu_per_node
+    ):
         """Test multi node tensor parallel size (number of GPUs per pod) propagation to pod config"""
         isvc_parallel_size = str(patched_multi_node_spec.instance.spec.predictor.workerSpec.tensorParallelSize)
 
+        if int(isvc_parallel_size) > max_gpu_per_node:
+            pytest.skip(f"tensorParallelSize {isvc_parallel_size} exceeds max GPUs per node ({max_gpu_per_node})")
+
         failed_pods: list[dict[str, Any]] = []
 
         for pod in get_pods_by_isvc_generation(client=unprivileged_client, isvc=patched_multi_node_spec):

tests/model_serving/model_server/kserve/multi_node/utils.py

@@ -124,6 +124,53 @@ def get_pods_by_isvc_generation(client: DynamicClient, isvc: InferenceService) -
     raise ResourceNotFoundError(f"InferenceService {isvc.name} generation {isvc_generation} has no pods")
 
 
+@retry(wait_timeout=Timeout.TIMEOUT_10MIN, sleep=10)
+def wait_for_vllm_health(client: DynamicClient, isvc: InferenceService) -> bool:
+    """Wait for vLLM to serve inference successfully on the head pod.
+
+    After pod deletion and recovery, the vLLM /health endpoint and Ray
+    node count may report healthy before the distributed inference
+    pipeline is fully rebuilt. This function verifies the model can
+    serve an actual inference request via pod exec on the head pod.
+
+    Args:
+        client: Dynamic client.
+        isvc: InferenceService object.
+
+    Returns:
+        True when inference succeeds.
+
+    Raises:
+        RuntimeError: If inference check fails.
+    """
+    for pod in get_pods_by_isvc_label(client=client, isvc=isvc):
+        if WORKER_POD_ROLE not in pod.name:
+            result = pod.execute(
+                command=[
+                    "curl",
+                    "-s",
+                    "-o",
+                    "/dev/null",
+                    "-w",
+                    "%{http_code}",
+                    "-X",
+                    "POST",
+                    "http://localhost:8080/v1/completions",
+                    "-H",
+                    "Content-Type: application/json",
+                    "-d",
+                    f'{{"model":"{isvc.name}","prompt":"test","max_tokens":1}}',
+                ]
+            )
+            if result.strip().strip("'") != "200":
+                raise RuntimeError(f"vLLM inference check returned {result} on head pod {pod.name}")
+
+            LOGGER.info(f"vLLM inference check passed on head pod {pod.name}")
+            return True
+
+    raise RuntimeError(f"No head pod found for InferenceService {isvc.name}")
+
+
 def is_arg_in_model_spec(client: DynamicClient, isvc: InferenceService, arg: str) -> bool:
     """
     Check if arg is in model spec; spec.model.args are only added to head pod

utilities/manifests/vllm.py

@@ -1,7 +1,7 @@
 VLLM_INFERENCE_CONFIG = {
         "default_query_model": {
             "query_input": '"prompt": "At what temperature does Nitrogen boil?", "max_tokens": 100, "temperature": 0',
-            "query_output": r'{"id":"cmpl-[a-z0-9]+","object":"text_completion","created":\d+,"model":"$model_name","choices":\[{"index":0,"text":".*Theboilingpointofnitrogenis77.4.*","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}\],"usage":{"prompt_tokens":10,"total_tokens":110,"completion_tokens":100,"prompt_tokens_details":null}}',
+            "query_output": r'{"id":"cmpl-[a-z0-9]+","object":"text_completion","created":\d+,"model":"$model_name","choices":\[{"index":0,"text":".*Theboilingpointofnitrogenis77.4.*","logprobs":null,"finish_reason":"length","stop_reason":null(,"[a-z_]+":null)*}\](,"[a-z_]+":null)*,"usage":{"prompt_tokens":10,"total_tokens":110,"completion_tokens":100,"prompt_tokens_details":null}(,"[a-z_]+":null)*}',
             "use_regex": True
         },
         "completions": {