('/workspace/source/test/e2e/explainer/test_art_explainer.py', 38, 'Skipped: ODH does not support art explainer at the moment')('/workspace/source/test/e2e/predictor/test_grpc.py', 35, 'Skipped: Not testable in ODH at the moment')('/workspace/source/test/e2e/predictor/test_torchserve.py', 34, 'Skipped: ODH does not support torchserve at the moment')@pytest.mark.llminferenceservice @pytest.mark.cluster_cpu @pytest.mark.cluster_single_node @log_execution def test_well_known_config_deletion_blocked_by_implicit_reference(): """Test that a well-known config is blocked from deletion by the finalizer while any LLMInferenceService exists in the same namespace. Uses a well-known-named config in the test namespace to avoid the webhook that prevents deletion of well-known configs in the kserve namespace. """ inject_k8s_proxy() kserve_client = _kserve_client() service_name = "e2e-wk-cfg-implicit-svc" extra_configs = [] llm_svc = None # Discover the well-known config name (handles custom prefixes) wk_config_name = _find_well_known_config(kserve_client, WELL_KNOWN_CONFIG_SUFFIX) assert wk_config_name is not None, ( f"No config ending with {WELL_KNOWN_CONFIG_SUFFIX!r} found in namespace {KSERVE_NAMESPACE}" ) print(f"Discovered well-known config name: {wk_config_name}") try: # Create a config with the well-known name in the test namespace. _create_config(kserve_client, wk_config_name) print( f"Created well-known config {wk_config_name} in namespace {KSERVE_TEST_NAMESPACE}" ) # Create an LLMInferenceService that does NOT explicitly reference the # well-known config. The controller treats well-known configs as implicitly # referenced by all services in the same namespace. model_config_name = f"{service_name}-model-cfg" _create_config(kserve_client, model_config_name) extra_configs.append(model_config_name) llm_svc, svc_extra = _create_llmisvc_with_config_ref( kserve_client, service_name, model_config_name, ) extra_configs.extend(svc_extra) # Wait for the well-known config to have a finalizer def assert_finalizer_present(): cfg = _get_config(kserve_client, wk_config_name) assert _config_has_finalizer(cfg), ( "Finalizer not yet present on well-known config" ) return True wait_for(assert_finalizer_present, timeout=60, interval=2.0) # Attempt to delete the well-known config (webhook allows it in test # namespace, but the finalizer should block it) print( f"Attempting to delete well-known config {wk_config_name} (should be blocked by finalizer)" ) _delete_config(kserve_client, wk_config_name) # The well-known config should be blocked from deletion def assert_deletion_blocked(): cfg = _get_config(kserve_client, wk_config_name) assert _config_has_deletion_timestamp(cfg), ( "Well-known config should have a deletionTimestamp after delete was called" ) assert _config_has_finalizer(cfg), ( "Finalizer should still be present while any service exists" ) in_use_cond = _get_condition(cfg, "ConfigInUse") assert in_use_cond is not None, "Expected ConfigInUse condition to be set" assert in_use_cond.get("status") == "True", ( f"Expected ConfigInUse=True, got {in_use_cond.get('status')}" ) assert in_use_cond.get("reason") == "DeletionBlocked", ( f"Expected reason=DeletionBlocked, got {in_use_cond.get('reason')}" ) referenced_by = cfg.get("status", {}).get("referencedBy", []) assert len(referenced_by) > 0, ( f"Expected referencedBy to list services, got {referenced_by}" ) return True wait_for(assert_deletion_blocked, timeout=60, interval=2.0) print( f"Well-known config {wk_config_name} deletion is correctly blocked " f"(ConfigInUse=True, reason=DeletionBlocked)" ) # Delete the service to unblock print(f"Deleting service {service_name} to unblock well-known config deletion") delete_llmisvc(kserve_client, llm_svc) llm_svc = None # The well-known config should now be deleted def assert_config_gone(): return _config_is_gone(kserve_client, wk_config_name) > wait_for(assert_config_gone, timeout=120, interval=2.0) llmisvc/test_llm_inference_service_config_deletion.py:568: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function test_well_known_config_deletion_blocked_by_implicit_reference.<locals>.assert_config_gone at 0x7f3e3e086700> timeout = 120, interval = 2.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def assert_config_gone(): > return _config_is_gone(kserve_client, wk_config_name) llmisvc/test_llm_inference_service_config_deletion.py:566: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7f3e3e024110> config_name = 'kserve-config-llm-template', namespace = 'kserve-ci-e2e-test' def _config_is_gone(kserve_client, config_name, namespace=KSERVE_TEST_NAMESPACE): """Assert that a config no longer exists (404).""" try: _get_config(kserve_client, config_name, namespace) > raise AssertionError(f"Config {config_name} still exists, expected 404") E AssertionError: Config kserve-config-llm-template still exists, expected 404 llmisvc/test_llm_inference_service_config_deletion.py:184: AssertionErrortest_case = LoRATestCase(base_refs=['router-no-scheduler', 'workload-single-cpu', 'model-fb-opt-125m-with-lora-hf'], prompt='What ...ice_name='lora-single-adapter-test', endpoint='/v1/completions', max_tokens=100, wait_timeout=900, response_timeout=60) @pytest.mark.parametrize( "test_case", [ pytest.param( LoRATestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], prompt="What is Kubernetes?", expected_adapter_names=["lora-adapter-1"], service_name="lora-single-adapter-test", ), marks=[ pytest.mark.llminferenceservice, pytest.mark.cluster_cpu, pytest.mark.lora, ], id="single-lora-adapter-hf", ), pytest.param( LoRATestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m-with-multiple-lora", ], prompt="Explain machine learning in simple terms.", expected_adapter_names=["lora-adapter-1", "lora-adapter-2"], service_name="lora-multiple-adapters-test", ), marks=[ pytest.mark.llminferenceservice, pytest.mark.cluster_cpu, pytest.mark.lora, ], id="multiple-lora-adapters", ), ], ) @log_execution def test_llm_with_lora_adapters(test_case: LoRATestCase): """Test LLMInferenceService with LoRA adapters.""" > run_lora_test(test_case) llmisvc/test_llm_lora_adapters.py:252: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ test_case = LoRATestCase(base_refs=['router-no-scheduler', 'workload-single-cpu', 'model-fb-opt-125m-with-lora-hf'], prompt='What ...ice_name='lora-single-adapter-test', endpoint='/v1/completions', max_tokens=100, wait_timeout=900, response_timeout=60) def run_lora_test(test_case: LoRATestCase): """Execute a LoRA adapter test case.""" inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) test_id = generate_test_id(test_case) service_name = test_case.service_name or f"lora-test-{test_id}" model_name = _get_model_name_from_configs(test_case.base_refs) created_configs = [] llm_service = None try: # Create unique LLMInferenceServiceConfig resources for each base ref unique_base_refs = [] for base_ref in test_case.base_refs: if base_ref not in LLMINFERENCESERVICE_CONFIGS: raise ValueError(f"Unknown base reference: {base_ref}") # Generate unique config name to avoid conflicts in parallel test runs unique_config_name = generate_k8s_safe_suffix(base_ref, [service_name]) unique_base_refs.append(unique_config_name) config = LLMINFERENCESERVICE_CONFIGS[base_ref] config_body = { "apiVersion": "serving.kserve.io/v1alpha1", "kind": "LLMInferenceServiceConfig", "metadata": { "name": unique_config_name, "namespace": KSERVE_TEST_NAMESPACE, }, "spec": config, } logger.info("Creating LLMInferenceServiceConfig: %s", unique_config_name) _create_or_update_llmisvc_config( kserve_client, config_body, KSERVE_TEST_NAMESPACE ) created_configs.append(unique_config_name) # Create the service with unique base refs llm_service = build_llm_service_from_refs(service_name, unique_base_refs) if not llm_service.metadata.annotations: llm_service.metadata.annotations = {} llm_service.metadata.annotations["security.opendatahub.io/enable-auth"] = ( "false" ) logger.info("Creating LLMInferenceService: %s", service_name) create_llmisvc(kserve_client, llm_service) # Wait for service to be ready logger.info("Waiting for service %s to be ready...", service_name) wait_for_llm_isvc_ready( kserve_client, llm_service, timeout_seconds=test_case.wait_timeout ) # Get inference URL base_url = get_llm_service_url(kserve_client, llm_service) inference_url = base_url + test_case.endpoint # Test base model inference logger.info("Testing base model inference...") base_payload = { "model": model_name, "prompt": test_case.prompt, "max_tokens": test_case.max_tokens, } base_response = post_with_retry( inference_url, json_data=base_payload, timeout=test_case.response_timeout, ) > assert_200_with_choices(base_response) llmisvc/test_llm_lora_adapters.py:156: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ response = <Response [503]> def assert_200_with_choices(response: requests.Response) -> None: """Assert 200 status code with choices in response.""" > assert ( response.status_code == 200 and response.json().get("choices") is not None and len(response.json().get("choices", [])) > 0 ), f"Expected 200 with choices, got {response.status_code}: {response.text}" E AssertionError: Expected 200 with choices, got 503: no healthy upstream E assert (503 == 200) E + where 503 = <Response [503]>.status_code llmisvc/test_llm_inference_service.py:55: AssertionErrortest_case = TestCase(base_refs=['router-no-scheduler', 'workload-single-cpu', 'model-fb-opt-125m'], prompt='What is KServe?', serv... {'name': 'model-fb-opt-125m-llmisvc-model-7a2ca70d'}]}, 'status': None}, model_name='facebook/opt-125m') @pytest.mark.llminferenceservice @pytest.mark.asyncio(loop_scope="session") @pytest.mark.parametrize( "test_case", [ pytest.param( TestCase( base_refs=[ "router-with-gateway-ref", "router-with-managed-route", "model-fb-opt-125m", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=create_response_assertion(with_field="choices"), expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="custom-route-timeout-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="router-with-refs-test", expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], routes=[ROUTER_ROUTES[0], ROUTER_ROUTES[1]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=["router-managed", "workload-pd-cpu", "model-fb-opt-125m"], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="custom-route-timeout-pd-test", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="router-with-refs-pd-test", response_assertion=assert_200_with_choices, expected_gateway=ROUTER_GATEWAYS[1], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[1]], routes=[ROUTER_ROUTES[2], ROUTER_ROUTES[3]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-dp-ep-gpu", "workload-dp-ep-prefill-gpu", "model-deepseek-v2-lite", ], prompt="Delve into the multifaceted implications of a fully disaggregated cloud architecture, specifically " "where the compute plane (P) and the data plane (D) are independently deployed and managed for a " "geographically distributed, high-throughput, low-latency microservices ecosystem. Beyond the " "fundamental challenges of network latency and data consistency, elaborate on the advanced " "considerations and trade-offs inherent in such a setup: 1. Network Architecture and Protocols: " "How would the network fabric and underlying protocols (e.g., RDMA, custom transport layers) need to " "evolve to support optimal performance and minimize inter-plane communication overhead, especially for " "synchronous operations? Discuss the role of network programmability (e.g., SDN, P4) in dynamically " "optimizing routing and traffic flow between P and D. 2. Advanced Data Consistency and Durability: " "Explore sophisticated data consistency models (e.g., causal consistency, strong eventual consistency) " "and their applicability in balancing performance and data integrity across a globally distributed data plane. " "Detail strategies for ensuring data durability and fault tolerance, including multi-region replication, " "intelligent partitioning, and recovery mechanisms in the event of partial or full plane failures. " "3. Dynamic Resource Orchestration and Cost Optimization: Analyze how an orchestration layer would intelligently " "manage the independent scaling of compute (P) and data (D) resources, considering fluctuating workloads, " "cost efficiency, and performance targets (e.g., using predictive analytics for resource provisioning). " "Discuss mechanisms for dynamically reallocating compute nodes to different data partitions based on " "workload patterns and data locality, potentially involving live migration strategies. " "4. Security and Compliance in a Distributed Landscape: Address the enhanced security perimeter " "challenges, including securing communication channels between P and D (encryption in transit, mutual TLS), " "fine-grained access control to data at rest and in motion, and identity management across disaggregated " "components. Discuss how such an architecture impacts compliance with regulatory frameworks (e.g., GDPR, HIPAA) " "concerning data sovereignty, privacy, and auditability. 5. Operational Complexity and Observability: " "Examine the increased complexity in monitoring, logging, and tracing across highly decoupled compute and " "data planes. What specialized tooling and practices (e.g., distributed tracing with OpenTelemetry, advanced AIOps) " "would be essential? How would incident response and troubleshooting differ in this disaggregated environment " "compared to traditional integrated systems? Consider the challenges of pinpointing root causes across " "independent failures. 6. Real-world Applicability and Future Trends: Identify specific industries " "or use cases (e.g., high-frequency trading, IoT edge processing, large language model inference) " "where the benefits of P/D disaggregation would strongly outweigh its complexities. " "Conclude by speculating on emerging technologies or paradigms (e.g., serverless compute functions " "directly interacting with object storage, in-memory disaggregation) that could further drive or " "transform P/D disaggregation in cloud computing.", max_tokens=2000, ), marks=[ pytest.mark.cluster_gpu, pytest.mark.cluster_nvidia, pytest.mark.cluster_nvidia_roce, ], ), pytest.param( TestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m", ], prompt="What is KServe?", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.no_scheduler, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-fb-opt-125m", ], prompt="This test simulates DP+EP that can run on CPU, the idea is to test the LWS-based deployment, " "but without the resources requirements for DP+EP (GPUs and ROCe/IB).", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node], ), # Scheduler config tests pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-inline-config", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-inline-config-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Chat completions endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], model_name="Qwen/Qwen2.5-0.5B-Instruct", endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=create_response_assertion(with_field="choices"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-configmap-ref", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-configmap-ref-test", before_test=[create_scheduler_configmap], after_test=[delete_scheduler_configmap], ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-replicas", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-ha-replicas-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-custom-template", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-custom-template-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Scheduler v0.6 → v0.7 migration tests. # Deploy v0.6-style configs and verify the controller migrates them # so the v0.7 scheduler boots successfully. pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-pd-config-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-pd-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-nonzero-threshold-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-threshold-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Precise prefix KV cache routing test pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-precise-prefix-cache-inline-config", "workload-llmd-simulator-kvcache", ], prompt="KServe is a", service_name="precise-prefix-cache-test", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Models endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/models", response_assertion=create_response_assertion(with_field="data"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/chat/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — LoRA adapter pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/completions", prompt="KServe is a", model_name=f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/models (base + LoRA) pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/models", response_assertion=assert_models_contains( "facebook/opt-125m", f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", "lora-adapter-1", f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # PVC storage tests -- validate direct PVC volume mount with real vLLM serving pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-pd-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-pvc", ], prompt="KServe is a", before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node, pytest.mark.pvc_storage, ], ), ], indirect=["test_case"], ids=generate_test_id, ) @log_execution def test_llm_inference_service(test_case: TestCase): # noqa: F811 inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) service_name = test_case.llm_service.metadata.name if not test_case.llm_service.metadata.annotations: test_case.llm_service.metadata.annotations = {} test_case.llm_service.metadata.annotations[ "security.opendatahub.io/enable-auth" ] = "false" prefix = test_case.log_prefix test_failed = False try: print(f"{prefix} Creating LLMInferenceService {service_name}") create_llmisvc(kserve_client, test_case.llm_service) print(f"{prefix} Waiting for LLMInferenceService {service_name} to be ready") wait_for_llm_isvc_ready( kserve_client, test_case.llm_service, test_case.wait_timeout ) print(f"{prefix} Waiting for model response from {service_name}") > wait_for_model_response( kserve_client, test_case, test_case.wait_timeout, extra_headers=test_case.extra_headers, ) llmisvc/test_llm_inference_service.py:811: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<kserve.api.kserve_client.KServeClient object at 0x7feb3531e410>, TestCase(base_refs=['router-no-scheduler', 'workloa... {'name': 'model-fb-opt-125m-llmisvc-model-7a2ca70d'}]}, 'status': None}, model_name='facebook/opt-125m'), 900) kwargs = {'extra_headers': None}, func_name = 'wait_for_model_response' timestamp_start = '2026-07-14T15:16:19.241677', start_time = 1784042179.241983 duration = 1102.880018234253, timestamp_end = '2026-07-14T15:34:42.122004' @functools.wraps(func) def wrapper(*args, **kwargs): func_name = func.__name__ timestamp_start = datetime.now().isoformat() logger.info( f"[{func_name}] [{timestamp_start}] start - args={args}, kwargs={kwargs}" ) start_time = time.time() try: > result = func(*args, **kwargs) llmisvc/logging.py:40: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb3531e410> test_case = TestCase(base_refs=['router-no-scheduler', 'workload-single-cpu', 'model-fb-opt-125m'], prompt='What is KServe?', serv... {'name': 'model-fb-opt-125m-llmisvc-model-7a2ca70d'}]}, 'status': None}, model_name='facebook/opt-125m') timeout_seconds = 900, extra_headers = None @log_execution def wait_for_model_response( kserve_client: KServeClient, test_case: TestCase, # noqa: F811 timeout_seconds: int = 900, extra_headers: Optional[Dict[str, str]] = None, ) -> str: def get_successful_response(): try: if test_case.url_getter: service_url = test_case.url_getter(kserve_client, test_case.llm_service) else: service_url = get_llm_service_url(kserve_client, test_case.llm_service) except Exception as e: raise AssertionError(f"❌ Failed to get service URL: {e}") from e model_url = service_url + test_case.endpoint headers = {"Content-Type": "application/json"} if extra_headers: headers.update(extra_headers) if test_case.payload_formatter is not None: test_payload = test_case.payload_formatter(test_case) elif test_case.prompt is not None: test_payload = { "model": test_case.model_name if not extra_headers or MODEL_ROUTING_HEADER not in extra_headers else extra_headers[MODEL_ROUTING_HEADER], "prompt": test_case.prompt, "max_tokens": test_case.max_tokens, } else: test_payload = None logger.info(f"Calling LLM service at {model_url} with payload {test_payload}") try: if test_payload is not None: response = post_with_retry( model_url, headers=headers, json_data=test_payload, timeout=test_case.response_timeout, ) else: response = get_with_retry( model_url, headers=headers, timeout=test_case.response_timeout, ) except Exception as e: logger.error(f"❌ Failed to call model: {e}") raise AssertionError(f"❌ Failed to call model: {e}") from e logger.info(f"Model response is {response.status_code}: {response.text[:500]}") if 200 <= response.status_code < 300: return response raise AssertionError( f"Service returned {response.status_code}: {response.text}" ) > response = wait_for(get_successful_response, timeout=timeout_seconds, interval=5.0) llmisvc/test_llm_inference_service.py:1120: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function wait_for_model_response.<locals>.get_successful_response at 0x7feb354d87c0> timeout = 900, interval = 5.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def get_successful_response(): try: if test_case.url_getter: service_url = test_case.url_getter(kserve_client, test_case.llm_service) else: service_url = get_llm_service_url(kserve_client, test_case.llm_service) except Exception as e: raise AssertionError(f"❌ Failed to get service URL: {e}") from e model_url = service_url + test_case.endpoint headers = {"Content-Type": "application/json"} if extra_headers: headers.update(extra_headers) if test_case.payload_formatter is not None: test_payload = test_case.payload_formatter(test_case) elif test_case.prompt is not None: test_payload = { "model": test_case.model_name if not extra_headers or MODEL_ROUTING_HEADER not in extra_headers else extra_headers[MODEL_ROUTING_HEADER], "prompt": test_case.prompt, "max_tokens": test_case.max_tokens, } else: test_payload = None logger.info(f"Calling LLM service at {model_url} with payload {test_payload}") try: if test_payload is not None: response = post_with_retry( model_url, headers=headers, json_data=test_payload, timeout=test_case.response_timeout, ) else: response = get_with_retry( model_url, headers=headers, timeout=test_case.response_timeout, ) except Exception as e: logger.error(f"❌ Failed to call model: {e}") raise AssertionError(f"❌ Failed to call model: {e}") from e logger.info(f"Model response is {response.status_code}: {response.text[:500]}") if 200 <= response.status_code < 300: return response > raise AssertionError( f"Service returned {response.status_code}: {response.text}" ) E AssertionError: Service returned 503: no healthy upstream llmisvc/test_llm_inference_service.py:1116: AssertionErrortest_case = LoRATestCase(base_refs=['router-no-scheduler', 'workload-single-cpu', 'model-fb-opt-125m-with-multiple-lora'], prompt=..._name='lora-multiple-adapters-test', endpoint='/v1/completions', max_tokens=100, wait_timeout=900, response_timeout=60) @pytest.mark.parametrize( "test_case", [ pytest.param( LoRATestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], prompt="What is Kubernetes?", expected_adapter_names=["lora-adapter-1"], service_name="lora-single-adapter-test", ), marks=[ pytest.mark.llminferenceservice, pytest.mark.cluster_cpu, pytest.mark.lora, ], id="single-lora-adapter-hf", ), pytest.param( LoRATestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m-with-multiple-lora", ], prompt="Explain machine learning in simple terms.", expected_adapter_names=["lora-adapter-1", "lora-adapter-2"], service_name="lora-multiple-adapters-test", ), marks=[ pytest.mark.llminferenceservice, pytest.mark.cluster_cpu, pytest.mark.lora, ], id="multiple-lora-adapters", ), ], ) @log_execution def test_llm_with_lora_adapters(test_case: LoRATestCase): """Test LLMInferenceService with LoRA adapters.""" > run_lora_test(test_case) llmisvc/test_llm_lora_adapters.py:252: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ test_case = LoRATestCase(base_refs=['router-no-scheduler', 'workload-single-cpu', 'model-fb-opt-125m-with-multiple-lora'], prompt=..._name='lora-multiple-adapters-test', endpoint='/v1/completions', max_tokens=100, wait_timeout=900, response_timeout=60) def run_lora_test(test_case: LoRATestCase): """Execute a LoRA adapter test case.""" inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) test_id = generate_test_id(test_case) service_name = test_case.service_name or f"lora-test-{test_id}" model_name = _get_model_name_from_configs(test_case.base_refs) created_configs = [] llm_service = None try: # Create unique LLMInferenceServiceConfig resources for each base ref unique_base_refs = [] for base_ref in test_case.base_refs: if base_ref not in LLMINFERENCESERVICE_CONFIGS: raise ValueError(f"Unknown base reference: {base_ref}") # Generate unique config name to avoid conflicts in parallel test runs unique_config_name = generate_k8s_safe_suffix(base_ref, [service_name]) unique_base_refs.append(unique_config_name) config = LLMINFERENCESERVICE_CONFIGS[base_ref] config_body = { "apiVersion": "serving.kserve.io/v1alpha1", "kind": "LLMInferenceServiceConfig", "metadata": { "name": unique_config_name, "namespace": KSERVE_TEST_NAMESPACE, }, "spec": config, } logger.info("Creating LLMInferenceServiceConfig: %s", unique_config_name) _create_or_update_llmisvc_config( kserve_client, config_body, KSERVE_TEST_NAMESPACE ) created_configs.append(unique_config_name) # Create the service with unique base refs llm_service = build_llm_service_from_refs(service_name, unique_base_refs) if not llm_service.metadata.annotations: llm_service.metadata.annotations = {} llm_service.metadata.annotations["security.opendatahub.io/enable-auth"] = ( "false" ) logger.info("Creating LLMInferenceService: %s", service_name) create_llmisvc(kserve_client, llm_service) # Wait for service to be ready logger.info("Waiting for service %s to be ready...", service_name) wait_for_llm_isvc_ready( kserve_client, llm_service, timeout_seconds=test_case.wait_timeout ) # Get inference URL base_url = get_llm_service_url(kserve_client, llm_service) inference_url = base_url + test_case.endpoint # Test base model inference logger.info("Testing base model inference...") base_payload = { "model": model_name, "prompt": test_case.prompt, "max_tokens": test_case.max_tokens, } base_response = post_with_retry( inference_url, json_data=base_payload, timeout=test_case.response_timeout, ) > assert_200_with_choices(base_response) llmisvc/test_llm_lora_adapters.py:156: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ response = <Response [503]> def assert_200_with_choices(response: requests.Response) -> None: """Assert 200 status code with choices in response.""" > assert ( response.status_code == 200 and response.json().get("choices") is not None and len(response.json().get("choices", [])) > 0 ), f"Expected 200 with choices, got {response.status_code}: {response.text}" E AssertionError: Expected 200 with choices, got 503: no healthy upstream E assert (503 == 200) E + where 503 = <Response [503]>.status_code llmisvc/test_llm_inference_service.py:55: AssertionErrortest_case = TestCase(base_refs=['router-managed', 'workload-single-cpu', 'model-fb-opt-125m'], prompt='KServe is a', service_name=... {'name': 'model-fb-opt-125m-tls-verificat-7c5fca28'}]}, 'status': None}, model_name='facebook/opt-125m') @pytest.mark.llminferenceservice @pytest.mark.asyncio(loop_scope="session") @pytest.mark.parametrize( "test_case", [ pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", payload_formatter=completions_payload, response_assertion=create_response_assertion(with_field="choices"), service_name="tls-verification-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), ], indirect=["test_case"], ids=generate_test_id, ) @log_execution def test_llm_tls_resources(test_case: TestCase): """Verify that TLS-related resources (DestinationRules, cert secrets, service port) are correctly present or absent based on the enableLLMInferenceServiceTLS flag.""" inject_k8s_proxy() tls_enabled = _is_tls_enabled() logger.info(f"enableLLMInferenceServiceTLS = {tls_enabled}") kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) service_name = test_case.llm_service.metadata.name if not test_case.llm_service.metadata.annotations: test_case.llm_service.metadata.annotations = {} test_case.llm_service.metadata.annotations[ "security.opendatahub.io/enable-auth" ] = "false" try: create_llmisvc(kserve_client, test_case.llm_service) wait_for_llm_isvc_ready( kserve_client, test_case.llm_service, test_case.wait_timeout ) > wait_for_model_response(kserve_client, test_case, test_case.wait_timeout) llmisvc/test_llm_tls.py:143: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<kserve.api.kserve_client.KServeClient object at 0x7f3e3def7c10>, TestCase(base_refs=['router-managed', 'workload-sin... {'name': 'model-fb-opt-125m-tls-verificat-7c5fca28'}]}, 'status': None}, model_name='facebook/opt-125m'), 900) kwargs = {}, func_name = 'wait_for_model_response' timestamp_start = '2026-07-14T15:24:38.938784', start_time = 1784042678.93908 duration = 1102.913336277008, timestamp_end = '2026-07-14T15:43:01.852419' @functools.wraps(func) def wrapper(*args, **kwargs): func_name = func.__name__ timestamp_start = datetime.now().isoformat() logger.info( f"[{func_name}] [{timestamp_start}] start - args={args}, kwargs={kwargs}" ) start_time = time.time() try: > result = func(*args, **kwargs) llmisvc/logging.py:40: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7f3e3def7c10> test_case = TestCase(base_refs=['router-managed', 'workload-single-cpu', 'model-fb-opt-125m'], prompt='KServe is a', service_name=... {'name': 'model-fb-opt-125m-tls-verificat-7c5fca28'}]}, 'status': None}, model_name='facebook/opt-125m') timeout_seconds = 900, extra_headers = None @log_execution def wait_for_model_response( kserve_client: KServeClient, test_case: TestCase, # noqa: F811 timeout_seconds: int = 900, extra_headers: Optional[Dict[str, str]] = None, ) -> str: def get_successful_response(): try: if test_case.url_getter: service_url = test_case.url_getter(kserve_client, test_case.llm_service) else: service_url = get_llm_service_url(kserve_client, test_case.llm_service) except Exception as e: raise AssertionError(f"❌ Failed to get service URL: {e}") from e model_url = service_url + test_case.endpoint headers = {"Content-Type": "application/json"} if extra_headers: headers.update(extra_headers) if test_case.payload_formatter is not None: test_payload = test_case.payload_formatter(test_case) elif test_case.prompt is not None: test_payload = { "model": test_case.model_name if not extra_headers or MODEL_ROUTING_HEADER not in extra_headers else extra_headers[MODEL_ROUTING_HEADER], "prompt": test_case.prompt, "max_tokens": test_case.max_tokens, } else: test_payload = None logger.info(f"Calling LLM service at {model_url} with payload {test_payload}") try: if test_payload is not None: response = post_with_retry( model_url, headers=headers, json_data=test_payload, timeout=test_case.response_timeout, ) else: response = get_with_retry( model_url, headers=headers, timeout=test_case.response_timeout, ) except Exception as e: logger.error(f"❌ Failed to call model: {e}") raise AssertionError(f"❌ Failed to call model: {e}") from e logger.info(f"Model response is {response.status_code}: {response.text[:500]}") if 200 <= response.status_code < 300: return response raise AssertionError( f"Service returned {response.status_code}: {response.text}" ) > response = wait_for(get_successful_response, timeout=timeout_seconds, interval=5.0) llmisvc/test_llm_inference_service.py:1120: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function wait_for_model_response.<locals>.get_successful_response at 0x7f3e3e0cfd80> timeout = 900, interval = 5.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def get_successful_response(): try: if test_case.url_getter: service_url = test_case.url_getter(kserve_client, test_case.llm_service) else: service_url = get_llm_service_url(kserve_client, test_case.llm_service) except Exception as e: raise AssertionError(f"❌ Failed to get service URL: {e}") from e model_url = service_url + test_case.endpoint headers = {"Content-Type": "application/json"} if extra_headers: headers.update(extra_headers) if test_case.payload_formatter is not None: test_payload = test_case.payload_formatter(test_case) elif test_case.prompt is not None: test_payload = { "model": test_case.model_name if not extra_headers or MODEL_ROUTING_HEADER not in extra_headers else extra_headers[MODEL_ROUTING_HEADER], "prompt": test_case.prompt, "max_tokens": test_case.max_tokens, } else: test_payload = None logger.info(f"Calling LLM service at {model_url} with payload {test_payload}") try: if test_payload is not None: response = post_with_retry( model_url, headers=headers, json_data=test_payload, timeout=test_case.response_timeout, ) else: response = get_with_retry( model_url, headers=headers, timeout=test_case.response_timeout, ) except Exception as e: logger.error(f"❌ Failed to call model: {e}") raise AssertionError(f"❌ Failed to call model: {e}") from e logger.info(f"Model response is {response.status_code}: {response.text[:500]}") if 200 <= response.status_code < 300: return response > raise AssertionError( f"Service returned {response.status_code}: {response.text}" ) E AssertionError: Service returned 503: no healthy upstream llmisvc/test_llm_inference_service.py:1116: AssertionErrortest_case = TestCase(base_refs=['router-managed', 'workload-llmd-simulator', 'model-fb-opt-125m'], prompt='KServe is a', service_n... {'name': 'model-fb-opt-125m-rolling-upgra-30e65219'}]}, 'status': None}, model_name='facebook/opt-125m') @pytest.mark.llminferenceservice @pytest.mark.parametrize( "test_case", [ pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-fb-opt-125m", ], prompt="KServe is a", service_name="rolling-upgrade-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), ], indirect=["test_case"], ids=generate_test_id, ) @log_execution def test_rolling_upgrade_coordination(test_case: TestCase): """ Verify the service recovers cleanly after a workload rolling update. Triggers a rolling update by patching a pod-template annotation on the LLMInferenceService. After the rollout completes the workload Deployment must be Available and the overall service must be Ready and able to serve inference requests — confirming the workload-before-EPP update ordering left the system in a healthy state. """ inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) service_name = test_case.llm_service.metadata.name namespace = test_case.llm_service.metadata.namespace test_failed = False if not test_case.llm_service.metadata.annotations: test_case.llm_service.metadata.annotations = {} test_case.llm_service.metadata.annotations[ "security.opendatahub.io/enable-auth" ] = "false" try: print(f"Creating LLMInferenceService {service_name}") create_llmisvc(kserve_client, test_case.llm_service) print(f"Waiting for {service_name} to be ready") > wait_for_llm_isvc_ready( kserve_client, test_case.llm_service, test_case.wait_timeout ) llmisvc/test_rolling_upgrade.py:94: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<kserve.api.kserve_client.KServeClient object at 0x7f3e3e15e250>, {'api_version': 'serving.kserve.io/v1alpha1', 'kin...lling-9660aa23'}, {'name': 'model-fb-opt-125m-rolling-upgra-30e65219'}]}, 'status': None}, 900) kwargs = {}, func_name = 'wait_for_llm_isvc_ready' timestamp_start = '2026-07-14T15:43:50.954125', start_time = 1784043830.9544346 duration = 900.5611088275909, timestamp_end = '2026-07-14T15:58:51.515546' @functools.wraps(func) def wrapper(*args, **kwargs): func_name = func.__name__ timestamp_start = datetime.now().isoformat() logger.info( f"[{func_name}] [{timestamp_start}] start - args={args}, kwargs={kwargs}" ) start_time = time.time() try: > result = func(*args, **kwargs) llmisvc/logging.py:40: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7f3e3e15e250> given = {'api_version': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceService', 'metadata': {'annotations': {'security....tor-rolling-9660aa23'}, {'name': 'model-fb-opt-125m-rolling-upgra-30e65219'}]}, 'status': None} timeout_seconds = 900 @log_execution def wait_for_llm_isvc_ready( kserve_client: KServeClient, given: V1alpha1LLMInferenceService, timeout_seconds: int = 900, ) -> str: def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) return True > return wait_for(assert_llm_isvc_ready, timeout=timeout_seconds, interval=1.0) llmisvc/test_llm_inference_service.py:1205: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function wait_for_llm_isvc_ready.<locals>.assert_llm_isvc_ready at 0x7f3e3df794e0> timeout = 900, interval = 1.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: > raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) E AssertionError: Missing true conditions: {'WorkloadsReady', 'Ready'}, expected {'WorkloadsReady', 'Ready', 'RouterReady'}, got [{'lastTransitionTime': '2026-07-14T15:44:47Z', 'severity': 'Info', 'status': 'True', 'type': 'HTTPRoutesReady'}, {'lastTransitionTime': '2026-07-14T15:44:47Z', 'severity': 'Info', 'status': 'True', 'type': 'InferencePoolReady'}, {'lastTransitionTime': '2026-07-14T15:44:47Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'severity': 'Info', 'status': 'False', 'type': 'MainWorkloadReady'}, {'lastTransitionTime': '2026-07-14T15:44:05Z', 'severity': 'Info', 'status': 'True', 'type': 'PresetsCombined'}, {'lastTransitionTime': '2026-07-14T15:44:47Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'status': 'False', 'type': 'Ready'}, {'lastTransitionTime': '2026-07-14T15:44:47Z', 'status': 'True', 'type': 'RouterReady'}, {'lastTransitionTime': '2026-07-14T15:44:47Z', 'severity': 'Info', 'status': 'True', 'type': 'SchedulerWorkloadReady'}, {'lastTransitionTime': '2026-07-14T15:44:47Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'status': 'False', 'type': 'WorkloadsReady'}] llmisvc/test_llm_inference_service.py:1200: AssertionErrortest_case = TestCase(base_refs=['router-managed', 'scheduler-with-inline-config', 'workload-llmd-simulator'], prompt='KServe is a'... {'name': 'workload-llmd-simulator-schedul-39bca5db'}]}, 'status': None}, model_name='facebook/opt-125m') @pytest.mark.llminferenceservice @pytest.mark.asyncio(loop_scope="session") @pytest.mark.parametrize( "test_case", [ pytest.param( TestCase( base_refs=[ "router-with-gateway-ref", "router-with-managed-route", "model-fb-opt-125m", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=create_response_assertion(with_field="choices"), expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="custom-route-timeout-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="router-with-refs-test", expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], routes=[ROUTER_ROUTES[0], ROUTER_ROUTES[1]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=["router-managed", "workload-pd-cpu", "model-fb-opt-125m"], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="custom-route-timeout-pd-test", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="router-with-refs-pd-test", response_assertion=assert_200_with_choices, expected_gateway=ROUTER_GATEWAYS[1], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[1]], routes=[ROUTER_ROUTES[2], ROUTER_ROUTES[3]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-dp-ep-gpu", "workload-dp-ep-prefill-gpu", "model-deepseek-v2-lite", ], prompt="Delve into the multifaceted implications of a fully disaggregated cloud architecture, specifically " "where the compute plane (P) and the data plane (D) are independently deployed and managed for a " "geographically distributed, high-throughput, low-latency microservices ecosystem. Beyond the " "fundamental challenges of network latency and data consistency, elaborate on the advanced " "considerations and trade-offs inherent in such a setup: 1. Network Architecture and Protocols: " "How would the network fabric and underlying protocols (e.g., RDMA, custom transport layers) need to " "evolve to support optimal performance and minimize inter-plane communication overhead, especially for " "synchronous operations? Discuss the role of network programmability (e.g., SDN, P4) in dynamically " "optimizing routing and traffic flow between P and D. 2. Advanced Data Consistency and Durability: " "Explore sophisticated data consistency models (e.g., causal consistency, strong eventual consistency) " "and their applicability in balancing performance and data integrity across a globally distributed data plane. " "Detail strategies for ensuring data durability and fault tolerance, including multi-region replication, " "intelligent partitioning, and recovery mechanisms in the event of partial or full plane failures. " "3. Dynamic Resource Orchestration and Cost Optimization: Analyze how an orchestration layer would intelligently " "manage the independent scaling of compute (P) and data (D) resources, considering fluctuating workloads, " "cost efficiency, and performance targets (e.g., using predictive analytics for resource provisioning). " "Discuss mechanisms for dynamically reallocating compute nodes to different data partitions based on " "workload patterns and data locality, potentially involving live migration strategies. " "4. Security and Compliance in a Distributed Landscape: Address the enhanced security perimeter " "challenges, including securing communication channels between P and D (encryption in transit, mutual TLS), " "fine-grained access control to data at rest and in motion, and identity management across disaggregated " "components. Discuss how such an architecture impacts compliance with regulatory frameworks (e.g., GDPR, HIPAA) " "concerning data sovereignty, privacy, and auditability. 5. Operational Complexity and Observability: " "Examine the increased complexity in monitoring, logging, and tracing across highly decoupled compute and " "data planes. What specialized tooling and practices (e.g., distributed tracing with OpenTelemetry, advanced AIOps) " "would be essential? How would incident response and troubleshooting differ in this disaggregated environment " "compared to traditional integrated systems? Consider the challenges of pinpointing root causes across " "independent failures. 6. Real-world Applicability and Future Trends: Identify specific industries " "or use cases (e.g., high-frequency trading, IoT edge processing, large language model inference) " "where the benefits of P/D disaggregation would strongly outweigh its complexities. " "Conclude by speculating on emerging technologies or paradigms (e.g., serverless compute functions " "directly interacting with object storage, in-memory disaggregation) that could further drive or " "transform P/D disaggregation in cloud computing.", max_tokens=2000, ), marks=[ pytest.mark.cluster_gpu, pytest.mark.cluster_nvidia, pytest.mark.cluster_nvidia_roce, ], ), pytest.param( TestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m", ], prompt="What is KServe?", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.no_scheduler, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-fb-opt-125m", ], prompt="This test simulates DP+EP that can run on CPU, the idea is to test the LWS-based deployment, " "but without the resources requirements for DP+EP (GPUs and ROCe/IB).", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node], ), # Scheduler config tests pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-inline-config", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-inline-config-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Chat completions endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], model_name="Qwen/Qwen2.5-0.5B-Instruct", endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=create_response_assertion(with_field="choices"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-configmap-ref", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-configmap-ref-test", before_test=[create_scheduler_configmap], after_test=[delete_scheduler_configmap], ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-replicas", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-ha-replicas-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-custom-template", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-custom-template-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Scheduler v0.6 → v0.7 migration tests. # Deploy v0.6-style configs and verify the controller migrates them # so the v0.7 scheduler boots successfully. pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-pd-config-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-pd-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-nonzero-threshold-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-threshold-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Precise prefix KV cache routing test pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-precise-prefix-cache-inline-config", "workload-llmd-simulator-kvcache", ], prompt="KServe is a", service_name="precise-prefix-cache-test", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Models endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/models", response_assertion=create_response_assertion(with_field="data"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/chat/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — LoRA adapter pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/completions", prompt="KServe is a", model_name=f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/models (base + LoRA) pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/models", response_assertion=assert_models_contains( "facebook/opt-125m", f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", "lora-adapter-1", f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # PVC storage tests -- validate direct PVC volume mount with real vLLM serving pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-pd-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-pvc", ], prompt="KServe is a", before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node, pytest.mark.pvc_storage, ], ), ], indirect=["test_case"], ids=generate_test_id, ) @log_execution def test_llm_inference_service(test_case: TestCase): # noqa: F811 inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) service_name = test_case.llm_service.metadata.name if not test_case.llm_service.metadata.annotations: test_case.llm_service.metadata.annotations = {} test_case.llm_service.metadata.annotations[ "security.opendatahub.io/enable-auth" ] = "false" prefix = test_case.log_prefix test_failed = False try: print(f"{prefix} Creating LLMInferenceService {service_name}") create_llmisvc(kserve_client, test_case.llm_service) print(f"{prefix} Waiting for LLMInferenceService {service_name} to be ready") > wait_for_llm_isvc_ready( kserve_client, test_case.llm_service, test_case.wait_timeout ) llmisvc/test_llm_inference_service.py:807: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<kserve.api.kserve_client.KServeClient object at 0x7feb3511c210>, {'api_version': 'serving.kserve.io/v1alpha1', 'kin...ig-sc-9914b4fd'}, {'name': 'workload-llmd-simulator-schedul-39bca5db'}]}, 'status': None}, 900) kwargs = {}, func_name = 'wait_for_llm_isvc_ready' timestamp_start = '2026-07-14T15:43:55.432507', start_time = 1784043835.4327602 duration = 900.6432683467865, timestamp_end = '2026-07-14T15:58:56.076046' @functools.wraps(func) def wrapper(*args, **kwargs): func_name = func.__name__ timestamp_start = datetime.now().isoformat() logger.info( f"[{func_name}] [{timestamp_start}] start - args={args}, kwargs={kwargs}" ) start_time = time.time() try: > result = func(*args, **kwargs) llmisvc/logging.py:40: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb3511c210> given = {'api_version': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceService', 'metadata': {'annotations': {'security....e-config-sc-9914b4fd'}, {'name': 'workload-llmd-simulator-schedul-39bca5db'}]}, 'status': None} timeout_seconds = 900 @log_execution def wait_for_llm_isvc_ready( kserve_client: KServeClient, given: V1alpha1LLMInferenceService, timeout_seconds: int = 900, ) -> str: def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) return True > return wait_for(assert_llm_isvc_ready, timeout=timeout_seconds, interval=1.0) llmisvc/test_llm_inference_service.py:1205: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function wait_for_llm_isvc_ready.<locals>.assert_llm_isvc_ready at 0x7feb354d9f80> timeout = 900, interval = 1.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: > raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) E AssertionError: Missing true conditions: {'WorkloadsReady', 'Ready'}, expected {'WorkloadsReady', 'RouterReady', 'Ready'}, got [{'lastTransitionTime': '2026-07-14T15:44:52Z', 'severity': 'Info', 'status': 'True', 'type': 'HTTPRoutesReady'}, {'lastTransitionTime': '2026-07-14T15:44:52Z', 'severity': 'Info', 'status': 'True', 'type': 'InferencePoolReady'}, {'lastTransitionTime': '2026-07-14T15:44:52Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'severity': 'Info', 'status': 'False', 'type': 'MainWorkloadReady'}, {'lastTransitionTime': '2026-07-14T15:44:20Z', 'severity': 'Info', 'status': 'True', 'type': 'PresetsCombined'}, {'lastTransitionTime': '2026-07-14T15:44:52Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'status': 'False', 'type': 'Ready'}, {'lastTransitionTime': '2026-07-14T15:44:53Z', 'status': 'True', 'type': 'RouterReady'}, {'lastTransitionTime': '2026-07-14T15:44:53Z', 'severity': 'Info', 'status': 'True', 'type': 'SchedulerWorkloadReady'}, {'lastTransitionTime': '2026-07-14T15:44:52Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'status': 'False', 'type': 'WorkloadsReady'}] llmisvc/test_llm_inference_service.py:1200: AssertionErrortest_case = TestCase(base_refs=['router-managed', 'workload-llmd-simulator', 'model-qwen2.5-0.5b'], prompt='What is KServe?', serv... {'name': 'model-qwen2-5-0-5b-llmisvc-mode-8ec1f6c6'}]}, 'status': None}, model_name='Qwen/Qwen2.5-0.5B-Instruct') @pytest.mark.llminferenceservice @pytest.mark.asyncio(loop_scope="session") @pytest.mark.parametrize( "test_case", [ pytest.param( TestCase( base_refs=[ "router-with-gateway-ref", "router-with-managed-route", "model-fb-opt-125m", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=create_response_assertion(with_field="choices"), expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="custom-route-timeout-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="router-with-refs-test", expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], routes=[ROUTER_ROUTES[0], ROUTER_ROUTES[1]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=["router-managed", "workload-pd-cpu", "model-fb-opt-125m"], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="custom-route-timeout-pd-test", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="router-with-refs-pd-test", response_assertion=assert_200_with_choices, expected_gateway=ROUTER_GATEWAYS[1], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[1]], routes=[ROUTER_ROUTES[2], ROUTER_ROUTES[3]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-dp-ep-gpu", "workload-dp-ep-prefill-gpu", "model-deepseek-v2-lite", ], prompt="Delve into the multifaceted implications of a fully disaggregated cloud architecture, specifically " "where the compute plane (P) and the data plane (D) are independently deployed and managed for a " "geographically distributed, high-throughput, low-latency microservices ecosystem. Beyond the " "fundamental challenges of network latency and data consistency, elaborate on the advanced " "considerations and trade-offs inherent in such a setup: 1. Network Architecture and Protocols: " "How would the network fabric and underlying protocols (e.g., RDMA, custom transport layers) need to " "evolve to support optimal performance and minimize inter-plane communication overhead, especially for " "synchronous operations? Discuss the role of network programmability (e.g., SDN, P4) in dynamically " "optimizing routing and traffic flow between P and D. 2. Advanced Data Consistency and Durability: " "Explore sophisticated data consistency models (e.g., causal consistency, strong eventual consistency) " "and their applicability in balancing performance and data integrity across a globally distributed data plane. " "Detail strategies for ensuring data durability and fault tolerance, including multi-region replication, " "intelligent partitioning, and recovery mechanisms in the event of partial or full plane failures. " "3. Dynamic Resource Orchestration and Cost Optimization: Analyze how an orchestration layer would intelligently " "manage the independent scaling of compute (P) and data (D) resources, considering fluctuating workloads, " "cost efficiency, and performance targets (e.g., using predictive analytics for resource provisioning). " "Discuss mechanisms for dynamically reallocating compute nodes to different data partitions based on " "workload patterns and data locality, potentially involving live migration strategies. " "4. Security and Compliance in a Distributed Landscape: Address the enhanced security perimeter " "challenges, including securing communication channels between P and D (encryption in transit, mutual TLS), " "fine-grained access control to data at rest and in motion, and identity management across disaggregated " "components. Discuss how such an architecture impacts compliance with regulatory frameworks (e.g., GDPR, HIPAA) " "concerning data sovereignty, privacy, and auditability. 5. Operational Complexity and Observability: " "Examine the increased complexity in monitoring, logging, and tracing across highly decoupled compute and " "data planes. What specialized tooling and practices (e.g., distributed tracing with OpenTelemetry, advanced AIOps) " "would be essential? How would incident response and troubleshooting differ in this disaggregated environment " "compared to traditional integrated systems? Consider the challenges of pinpointing root causes across " "independent failures. 6. Real-world Applicability and Future Trends: Identify specific industries " "or use cases (e.g., high-frequency trading, IoT edge processing, large language model inference) " "where the benefits of P/D disaggregation would strongly outweigh its complexities. " "Conclude by speculating on emerging technologies or paradigms (e.g., serverless compute functions " "directly interacting with object storage, in-memory disaggregation) that could further drive or " "transform P/D disaggregation in cloud computing.", max_tokens=2000, ), marks=[ pytest.mark.cluster_gpu, pytest.mark.cluster_nvidia, pytest.mark.cluster_nvidia_roce, ], ), pytest.param( TestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m", ], prompt="What is KServe?", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.no_scheduler, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-fb-opt-125m", ], prompt="This test simulates DP+EP that can run on CPU, the idea is to test the LWS-based deployment, " "but without the resources requirements for DP+EP (GPUs and ROCe/IB).", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node], ), # Scheduler config tests pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-inline-config", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-inline-config-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Chat completions endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], model_name="Qwen/Qwen2.5-0.5B-Instruct", endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=create_response_assertion(with_field="choices"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-configmap-ref", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-configmap-ref-test", before_test=[create_scheduler_configmap], after_test=[delete_scheduler_configmap], ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-replicas", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-ha-replicas-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-custom-template", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-custom-template-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Scheduler v0.6 → v0.7 migration tests. # Deploy v0.6-style configs and verify the controller migrates them # so the v0.7 scheduler boots successfully. pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-pd-config-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-pd-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-nonzero-threshold-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-threshold-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Precise prefix KV cache routing test pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-precise-prefix-cache-inline-config", "workload-llmd-simulator-kvcache", ], prompt="KServe is a", service_name="precise-prefix-cache-test", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Models endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/models", response_assertion=create_response_assertion(with_field="data"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/chat/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — LoRA adapter pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/completions", prompt="KServe is a", model_name=f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/models (base + LoRA) pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/models", response_assertion=assert_models_contains( "facebook/opt-125m", f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", "lora-adapter-1", f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # PVC storage tests -- validate direct PVC volume mount with real vLLM serving pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-pd-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-pvc", ], prompt="KServe is a", before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node, pytest.mark.pvc_storage, ], ), ], indirect=["test_case"], ids=generate_test_id, ) @log_execution def test_llm_inference_service(test_case: TestCase): # noqa: F811 inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) service_name = test_case.llm_service.metadata.name if not test_case.llm_service.metadata.annotations: test_case.llm_service.metadata.annotations = {} test_case.llm_service.metadata.annotations[ "security.opendatahub.io/enable-auth" ] = "false" prefix = test_case.log_prefix test_failed = False try: print(f"{prefix} Creating LLMInferenceService {service_name}") create_llmisvc(kserve_client, test_case.llm_service) print(f"{prefix} Waiting for LLMInferenceService {service_name} to be ready") > wait_for_llm_isvc_ready( kserve_client, test_case.llm_service, test_case.wait_timeout ) llmisvc/test_llm_inference_service.py:807: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<kserve.api.kserve_client.KServeClient object at 0x7feb3549c950>, {'api_version': 'serving.kserve.io/v1alpha1', 'kin...misvc-947004c3'}, {'name': 'model-qwen2-5-0-5b-llmisvc-mode-8ec1f6c6'}]}, 'status': None}, 900) kwargs = {}, func_name = 'wait_for_llm_isvc_ready' timestamp_start = '2026-07-14T15:58:58.146504', start_time = 1784044738.14681 duration = 900.3308358192444, timestamp_end = '2026-07-14T16:13:58.477650' @functools.wraps(func) def wrapper(*args, **kwargs): func_name = func.__name__ timestamp_start = datetime.now().isoformat() logger.info( f"[{func_name}] [{timestamp_start}] start - args={args}, kwargs={kwargs}" ) start_time = time.time() try: > result = func(*args, **kwargs) llmisvc/logging.py:40: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb3549c950> given = {'api_version': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceService', 'metadata': {'annotations': {'security....tor-llmisvc-947004c3'}, {'name': 'model-qwen2-5-0-5b-llmisvc-mode-8ec1f6c6'}]}, 'status': None} timeout_seconds = 900 @log_execution def wait_for_llm_isvc_ready( kserve_client: KServeClient, given: V1alpha1LLMInferenceService, timeout_seconds: int = 900, ) -> str: def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) return True > return wait_for(assert_llm_isvc_ready, timeout=timeout_seconds, interval=1.0) llmisvc/test_llm_inference_service.py:1205: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function wait_for_llm_isvc_ready.<locals>.assert_llm_isvc_ready at 0x7feb354dbba0> timeout = 900, interval = 1.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: > raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) E AssertionError: Missing true conditions: {'WorkloadsReady', 'Ready'}, expected {'WorkloadsReady', 'RouterReady', 'Ready'}, got [{'lastTransitionTime': '2026-07-14T15:59:20Z', 'severity': 'Info', 'status': 'True', 'type': 'HTTPRoutesReady'}, {'lastTransitionTime': '2026-07-14T15:59:20Z', 'severity': 'Info', 'status': 'True', 'type': 'InferencePoolReady'}, {'lastTransitionTime': '2026-07-14T15:59:20Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'severity': 'Info', 'status': 'False', 'type': 'MainWorkloadReady'}, {'lastTransitionTime': '2026-07-14T15:59:02Z', 'severity': 'Info', 'status': 'True', 'type': 'PresetsCombined'}, {'lastTransitionTime': '2026-07-14T15:59:20Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'status': 'False', 'type': 'Ready'}, {'lastTransitionTime': '2026-07-14T15:59:32Z', 'status': 'True', 'type': 'RouterReady'}, {'lastTransitionTime': '2026-07-14T15:59:32Z', 'severity': 'Info', 'status': 'True', 'type': 'SchedulerWorkloadReady'}, {'lastTransitionTime': '2026-07-14T15:59:20Z', 'message': 'Deployment does not have minimum availability.', 'reason': 'MinimumReplicasUnavailable', 'status': 'False', 'type': 'WorkloadsReady'}] llmisvc/test_llm_inference_service.py:1200: AssertionErrorkserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb353aab90> name = 'scheduler-configmap-ref-test', namespace = 'kserve-ci-e2e-test' version = 'v1alpha1' def get_llmisvc( kserve_client: KServeClient, name, namespace, version=constants.KSERVE_V1ALPHA1_VERSION, ): try: > return kserve_client.api_instance.get_namespaced_custom_object( constants.KSERVE_GROUP, version, namespace, KSERVE_PLURAL_LLMINFERENCESERVICE, name, ) llmisvc/test_llm_inference_service.py:1044: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api.custom_objects_api.CustomObjectsApi object at 0x7feb3533c590> group = 'serving.kserve.io', version = 'v1alpha1' namespace = 'kserve-ci-e2e-test', plural = 'llminferenceservices' name = 'scheduler-configmap-ref-test', kwargs = {'_return_http_data_only': True} def get_namespaced_custom_object(self, group, version, namespace, plural, name, **kwargs): # noqa: E501 """get_namespaced_custom_object # noqa: E501 Returns a namespace scoped custom object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_namespaced_custom_object(group, version, namespace, plural, name, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str group: the custom resource's group (required) :param str version: the custom resource's version (required) :param str namespace: The custom resource's namespace (required) :param str plural: the custom resource's plural name. For TPRs this would be lowercase plural kind. (required) :param str name: the custom object's name (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: object If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True > return self.get_namespaced_custom_object_with_http_info(group, version, namespace, plural, name, **kwargs) # noqa: E501 ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api/custom_objects_api.py:1632: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api.custom_objects_api.CustomObjectsApi object at 0x7feb3533c590> group = 'serving.kserve.io', version = 'v1alpha1' namespace = 'kserve-ci-e2e-test', plural = 'llminferenceservices' name = 'scheduler-configmap-ref-test', kwargs = {'_return_http_data_only': True} local_var_params = {'_return_http_data_only': True, 'all_params': ['group', 'version', 'namespace', 'plural', 'name', 'async_req', ...], 'auth_settings': ['BearerToken'], 'body_params': None, ...} all_params = ['group', 'version', 'namespace', 'plural', 'name', 'async_req', ...] key = '_return_http_data_only', val = True, collection_formats = {} path_params = {'group': 'serving.kserve.io', 'name': 'scheduler-configmap-ref-test', 'namespace': 'kserve-ci-e2e-test', 'plural': 'llminferenceservices', ...} query_params = [] def get_namespaced_custom_object_with_http_info(self, group, version, namespace, plural, name, **kwargs): # noqa: E501 """get_namespaced_custom_object # noqa: E501 Returns a namespace scoped custom object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_namespaced_custom_object_with_http_info(group, version, namespace, plural, name, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str group: the custom resource's group (required) :param str version: the custom resource's version (required) :param str namespace: The custom resource's namespace (required) :param str plural: the custom resource's plural name. For TPRs this would be lowercase plural kind. (required) :param str name: the custom object's name (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(object, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'group', 'version', 'namespace', 'plural', 'name' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_namespaced_custom_object" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'group' is set if self.api_client.client_side_validation and ('group' not in local_var_params or # noqa: E501 local_var_params['group'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `group` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'version' is set if self.api_client.client_side_validation and ('version' not in local_var_params or # noqa: E501 local_var_params['version'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `version` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'namespace' is set if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501 local_var_params['namespace'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `namespace` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'plural' is set if self.api_client.client_side_validation and ('plural' not in local_var_params or # noqa: E501 local_var_params['plural'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `plural` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'name' is set if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501 local_var_params['name'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `name` when calling `get_namespaced_custom_object`") # noqa: E501 collection_formats = {} path_params = {} if 'group' in local_var_params: path_params['group'] = local_var_params['group'] # noqa: E501 if 'version' in local_var_params: path_params['version'] = local_var_params['version'] # noqa: E501 if 'namespace' in local_var_params: path_params['namespace'] = local_var_params['namespace'] # noqa: E501 if 'plural' in local_var_params: path_params['plural'] = local_var_params['plural'] # noqa: E501 if 'name' in local_var_params: path_params['name'] = local_var_params['name'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 > return self.api_client.call_api( '/apis/{group}/{version}/namespaces/{namespace}/{plural}/{name}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='object', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api/custom_objects_api.py:1739: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3533f110> resource_path = '/apis/{group}/{version}/namespaces/{namespace}/{plural}/{name}' method = 'GET' path_params = {'group': 'serving.kserve.io', 'name': 'scheduler-configmap-ref-test', 'namespace': 'kserve-ci-e2e-test', 'plural': 'llminferenceservices', ...} query_params = [] header_params = {'Accept': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = None, post_params = [], files = {}, response_type = 'object' auth_settings = ['BearerToken'], async_req = None, _return_http_data_only = True collection_formats = {}, _preload_content = True, _request_timeout = None _host = None def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, async_req=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None): """Makes the HTTP request (synchronous) and returns deserialized data. To make an async_req request, set the async_req parameter. :param resource_path: Path to method endpoint. :param method: Method to call. :param path_params: Path parameters in the url. :param query_params: Query parameters in the url. :param header_params: Header parameters to be placed in the request header. :param body: Request body. :param post_params dict: Request post form parameters, for `application/x-www-form-urlencoded`, `multipart/form-data`. :param auth_settings list: Auth Settings names for the request. :param response: Response data type. :param files dict: key -> filename, value -> filepath, for `multipart/form-data`. :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param collection_formats: dict of collection formats for path, query, header, and post parameters. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: If async_req parameter is True, the request will be called asynchronously. The method will return the request thread. If parameter async_req is False or missing, then the method will return the response directly. """ if not async_req: > return self.__call_api(resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:348: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3533f110> resource_path = '/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceservices/scheduler-configmap-ref-test' method = 'GET' path_params = [('group', 'serving.kserve.io'), ('version', 'v1alpha1'), ('namespace', 'kserve-ci-e2e-test'), ('plural', 'llminferenceservices'), ('name', 'scheduler-configmap-ref-test')] query_params = [] header_params = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = None, post_params = [], files = {}, response_type = 'object' auth_settings = ['BearerToken'], _return_http_data_only = True collection_formats = {}, _preload_content = True, _request_timeout = None _host = None def __call_api( self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None): config = self.configuration # header parameters header_params = header_params or {} header_params.update(self.default_headers) if self.cookie: header_params['Cookie'] = self.cookie if header_params: header_params = self.sanitize_for_serialization(header_params) header_params = dict(self.parameters_to_tuples(header_params, collection_formats)) # path parameters if path_params: path_params = self.sanitize_for_serialization(path_params) path_params = self.parameters_to_tuples(path_params, collection_formats) for k, v in path_params: # specified safe chars, encode everything resource_path = resource_path.replace( '{%s}' % k, quote(str(v), safe=config.safe_chars_for_path_param) ) # query parameters if query_params: query_params = self.sanitize_for_serialization(query_params) query_params = self.parameters_to_tuples(query_params, collection_formats) # post parameters if post_params or files: post_params = post_params if post_params else [] post_params = self.sanitize_for_serialization(post_params) post_params = self.parameters_to_tuples(post_params, collection_formats) post_params.extend(self.files_parameters(files)) # auth setting self.update_params_for_auth(header_params, query_params, auth_settings) # body if body: body = self.sanitize_for_serialization(body) # request url if _host is None: url = self.configuration.host + resource_path else: # use server/host defined in path or operation instead url = _host + resource_path # perform request and return response > response_data = self.request( method, url, query_params=query_params, headers=header_params, post_params=post_params, body=body, _preload_content=_preload_content, _request_timeout=_request_timeout) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:180: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3533f110> method = 'GET' url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceservices/scheduler-configmap-ref-test' query_params = [] headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} post_params = [], body = None, _preload_content = True, _request_timeout = None def request(self, method, url, query_params=None, headers=None, post_params=None, body=None, _preload_content=True, _request_timeout=None): """Makes the HTTP request using RESTClient.""" if method == "GET": > return self.rest_client.GET(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:373: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.rest.RESTClientObject object at 0x7feb3533fc10> url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceservices/scheduler-configmap-ref-test' headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} query_params = [], _preload_content = True, _request_timeout = None def GET(self, url, headers=None, query_params=None, _preload_content=True, _request_timeout=None): > return self.request("GET", url, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout, query_params=query_params) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/rest.py:244: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.rest.RESTClientObject object at 0x7feb3533fc10> method = 'GET' url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceservices/scheduler-configmap-ref-test' query_params = [] headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = None, post_params = {}, _preload_content = True, _request_timeout = None def request(self, method, url, query_params=None, headers=None, body=None, post_params=None, _preload_content=True, _request_timeout=None): """Perform requests. :param method: http request method :param url: http request url :param query_params: query parameters in the url :param headers: http request headers :param body: request json body, for `application/json` :param post_params: request post parameters, `application/x-www-form-urlencoded` and `multipart/form-data` :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. """ method = method.upper() assert method in ['GET', 'HEAD', 'DELETE', 'POST', 'PUT', 'PATCH', 'OPTIONS'] if post_params and body: raise ApiValueError( "body parameter cannot be used with post_params parameter." ) post_params = post_params or {} headers = headers or {} timeout = None if _request_timeout: if isinstance(_request_timeout, (int, ) if six.PY3 else (int, long)): # noqa: E501,F821 timeout = urllib3.Timeout(total=_request_timeout) elif (isinstance(_request_timeout, tuple) and len(_request_timeout) == 2): timeout = urllib3.Timeout( connect=_request_timeout[0], read=_request_timeout[1]) if 'Content-Type' not in headers: headers['Content-Type'] = 'application/json' try: # For `POST`, `PUT`, `PATCH`, `OPTIONS`, `DELETE` if method in ['POST', 'PUT', 'PATCH', 'OPTIONS', 'DELETE']: if query_params: url += '?' + urlencode(query_params) if (re.search('json', headers['Content-Type'], re.IGNORECASE) or headers['Content-Type'] == 'application/apply-patch+yaml'): if headers['Content-Type'] == 'application/json-patch+json': if not isinstance(body, list): headers['Content-Type'] = \ 'application/strategic-merge-patch+json' request_body = None if body is not None: request_body = json.dumps(body) r = self.pool_manager.request( method, url, body=request_body, preload_content=_preload_content, timeout=timeout, headers=headers) elif headers['Content-Type'] == 'application/x-www-form-urlencoded': # noqa: E501 r = self.pool_manager.request( method, url, fields=post_params, encode_multipart=False, preload_content=_preload_content, timeout=timeout, headers=headers) elif headers['Content-Type'] == 'multipart/form-data': # must del headers['Content-Type'], or the correct # Content-Type which generated by urllib3 will be # overwritten. del headers['Content-Type'] r = self.pool_manager.request( method, url, fields=post_params, encode_multipart=True, preload_content=_preload_content, timeout=timeout, headers=headers) # Pass a `string` parameter directly in the body to support # other content types than Json when `body` argument is # provided in serialized form elif isinstance(body, str) or isinstance(body, bytes): request_body = body r = self.pool_manager.request( method, url, body=request_body, preload_content=_preload_content, timeout=timeout, headers=headers) else: # Cannot generate the request from given parameters msg = """Cannot prepare a request message for provided arguments. Please check that your arguments match declared content type.""" raise ApiException(status=0, reason=msg) # For `GET`, `HEAD` else: r = self.pool_manager.request(method, url, fields=query_params, preload_content=_preload_content, timeout=timeout, headers=headers) except urllib3.exceptions.SSLError as e: msg = "{0}\n{1}".format(type(e).__name__, str(e)) raise ApiException(status=0, reason=msg) if _preload_content: r = RESTResponse(r) # In the python 3, the response.data is bytes. # we need to decode it to string. if six.PY3: r.data = r.data.decode('utf8') # log response body logger.debug("response body: %s", r.data) if not 200 <= r.status <= 299: > raise ApiException(http_resp=r) E kubernetes.client.exceptions.ApiException: (500) E Reason: Internal Server Error E HTTP response headers: HTTPHeaderDict({'Audit-Id': '0f8a21c4-7317-45e3-a15e-c82447ad1cc0', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload', 'X-Kubernetes-Pf-Flowschema-Uid': '2a999f06-9419-439a-ad27-f84423daf280', 'X-Kubernetes-Pf-Prioritylevel-Uid': '7646e888-06ea-42cb-b71d-ec0f1b0b2888', 'Date': 'Tue, 14 Jul 2026 16:20:11 GMT', 'Content-Length': '292'}) E HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"conversion webhook for serving.kserve.io/v1alpha2, Kind=LLMInferenceService failed: Post \"https://llmisvc-webhook-server-service.kserve.svc:443/convert?timeout=30s\": net/http: TLS handshake timeout","code":500} ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/rest.py:238: ApiException The above exception was the direct cause of the following exception: test_case = TestCase(base_refs=['router-managed', 'scheduler-with-configmap-ref', 'workload-llmd-simulator'], prompt='KServe is a'... {'name': 'workload-llmd-simulator-schedul-b2f159da'}]}, 'status': None}, model_name='facebook/opt-125m') @pytest.mark.llminferenceservice @pytest.mark.asyncio(loop_scope="session") @pytest.mark.parametrize( "test_case", [ pytest.param( TestCase( base_refs=[ "router-with-gateway-ref", "router-with-managed-route", "model-fb-opt-125m", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=create_response_assertion(with_field="choices"), expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="custom-route-timeout-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs", "scheduler-managed", "workload-single-cpu", "model-fb-opt-125m", ], prompt="KServe is a", service_name="router-with-refs-test", expected_gateway=ROUTER_GATEWAYS[0], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[0]], routes=[ROUTER_ROUTES[0], ROUTER_ROUTES[1]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=["router-managed", "workload-pd-cpu", "model-fb-opt-125m"], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-custom-route-timeout-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="custom-route-timeout-pd-test", response_assertion=assert_200_with_choices, ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-with-refs-pd", "scheduler-managed", "workload-pd-cpu", "model-fb-opt-125m", ], prompt="You are an expert in Kubernetes-native machine learning serving platforms, with deep knowledge of the KServe project. " "Explain the challenges of serving large-scale models, GPU scheduling, and how KServe integrates with capabilities like multi-model serving. " "Provide a detailed comparison with open source alternatives, focusing on operational trade-offs.", service_name="router-with-refs-pd-test", response_assertion=assert_200_with_choices, expected_gateway=ROUTER_GATEWAYS[1], before_test=[ lambda: create_router_resources( gateways=[ROUTER_GATEWAYS[1]], routes=[ROUTER_ROUTES[2], ROUTER_ROUTES[3]], ) ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.custom_gateway, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-dp-ep-gpu", "workload-dp-ep-prefill-gpu", "model-deepseek-v2-lite", ], prompt="Delve into the multifaceted implications of a fully disaggregated cloud architecture, specifically " "where the compute plane (P) and the data plane (D) are independently deployed and managed for a " "geographically distributed, high-throughput, low-latency microservices ecosystem. Beyond the " "fundamental challenges of network latency and data consistency, elaborate on the advanced " "considerations and trade-offs inherent in such a setup: 1. Network Architecture and Protocols: " "How would the network fabric and underlying protocols (e.g., RDMA, custom transport layers) need to " "evolve to support optimal performance and minimize inter-plane communication overhead, especially for " "synchronous operations? Discuss the role of network programmability (e.g., SDN, P4) in dynamically " "optimizing routing and traffic flow between P and D. 2. Advanced Data Consistency and Durability: " "Explore sophisticated data consistency models (e.g., causal consistency, strong eventual consistency) " "and their applicability in balancing performance and data integrity across a globally distributed data plane. " "Detail strategies for ensuring data durability and fault tolerance, including multi-region replication, " "intelligent partitioning, and recovery mechanisms in the event of partial or full plane failures. " "3. Dynamic Resource Orchestration and Cost Optimization: Analyze how an orchestration layer would intelligently " "manage the independent scaling of compute (P) and data (D) resources, considering fluctuating workloads, " "cost efficiency, and performance targets (e.g., using predictive analytics for resource provisioning). " "Discuss mechanisms for dynamically reallocating compute nodes to different data partitions based on " "workload patterns and data locality, potentially involving live migration strategies. " "4. Security and Compliance in a Distributed Landscape: Address the enhanced security perimeter " "challenges, including securing communication channels between P and D (encryption in transit, mutual TLS), " "fine-grained access control to data at rest and in motion, and identity management across disaggregated " "components. Discuss how such an architecture impacts compliance with regulatory frameworks (e.g., GDPR, HIPAA) " "concerning data sovereignty, privacy, and auditability. 5. Operational Complexity and Observability: " "Examine the increased complexity in monitoring, logging, and tracing across highly decoupled compute and " "data planes. What specialized tooling and practices (e.g., distributed tracing with OpenTelemetry, advanced AIOps) " "would be essential? How would incident response and troubleshooting differ in this disaggregated environment " "compared to traditional integrated systems? Consider the challenges of pinpointing root causes across " "independent failures. 6. Real-world Applicability and Future Trends: Identify specific industries " "or use cases (e.g., high-frequency trading, IoT edge processing, large language model inference) " "where the benefits of P/D disaggregation would strongly outweigh its complexities. " "Conclude by speculating on emerging technologies or paradigms (e.g., serverless compute functions " "directly interacting with object storage, in-memory disaggregation) that could further drive or " "transform P/D disaggregation in cloud computing.", max_tokens=2000, ), marks=[ pytest.mark.cluster_gpu, pytest.mark.cluster_nvidia, pytest.mark.cluster_nvidia_roce, ], ), pytest.param( TestCase( base_refs=[ "router-no-scheduler", "workload-single-cpu", "model-fb-opt-125m", ], prompt="What is KServe?", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.no_scheduler, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-fb-opt-125m", ], prompt="This test simulates DP+EP that can run on CPU, the idea is to test the LWS-based deployment, " "but without the resources requirements for DP+EP (GPUs and ROCe/IB).", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node], ), # Scheduler config tests pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-inline-config", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-inline-config-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Chat completions endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], model_name="Qwen/Qwen2.5-0.5B-Instruct", endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=create_response_assertion(with_field="choices"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-configmap-ref", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-configmap-ref-test", before_test=[create_scheduler_configmap], after_test=[delete_scheduler_configmap], ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-replicas", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-ha-replicas-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-custom-template", "workload-llmd-simulator", ], prompt="KServe is a", service_name="scheduler-custom-template-test", ), marks=[pytest.mark.cluster_cpu, pytest.mark.cluster_single_node], ), # Scheduler v0.6 → v0.7 migration tests. # Deploy v0.6-style configs and verify the controller migrates them # so the v0.7 scheduler boots successfully. pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-pd-config-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-pd-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-v06-nonzero-threshold-migration", "workload-llmd-simulator-pd", ], prompt="KServe is a", service_name="scheduler-v06-threshold-migration-test", response_assertion=assert_200_with_choices, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Precise prefix KV cache routing test pytest.param( TestCase( base_refs=[ "router-managed", "scheduler-with-precise-prefix-cache-inline-config", "workload-llmd-simulator-kvcache", ], prompt="KServe is a", service_name="precise-prefix-cache-test", ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Models endpoint coverage pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/models", response_assertion=create_response_assertion(with_field="data"), ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/completions", prompt="KServe is a", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/chat/completions pytest.param( TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches("facebook/opt-125m"), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, peers=[ TestCase( base_refs=[ "router-managed", "workload-llmd-simulator", "model-qwen2.5-0.5b", ], endpoint="/v1/chat/completions", prompt="What is KServe?", payload_formatter=chat_completions_payload, response_assertion=assert_model_field_matches( "Qwen/Qwen2.5-0.5B-Instruct" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/Qwen/Qwen2.5-0.5B-Instruct", }, ), ], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.llmd_simulator, pytest.mark.model_routing, ], ), # Model-based routing via X-Gateway-Model-Name header — LoRA adapter pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/completions", prompt="KServe is a", model_name=f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", payload_formatter=completions_payload, response_assertion=assert_model_field_matches( f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1" ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # Model-based routing via X-Gateway-Model-Name header — /v1/models (base + LoRA) pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-fb-opt-125m-with-lora-hf", ], endpoint="/v1/models", response_assertion=assert_models_contains( "facebook/opt-125m", f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", "lora-adapter-1", f"publishers/{KSERVE_TEST_NAMESPACE}/models/lora-adapter-1", ), url_getter=get_model_routing_url, extra_headers={ MODEL_ROUTING_HEADER: f"publishers/{KSERVE_TEST_NAMESPACE}/models/facebook/opt-125m", }, ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.model_routing, pytest.mark.lora, ], ), # PVC storage tests -- validate direct PVC volume mount with real vLLM serving pytest.param( TestCase( base_refs=[ "router-managed", "workload-single-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-pd-cpu", "model-pvc", ], prompt="KServe is a", response_assertion=assert_200_with_choices, before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_single_node, pytest.mark.pvc_storage, ], ), pytest.param( TestCase( base_refs=[ "router-managed", "workload-simulated-dp-ep-cpu", "model-pvc", ], prompt="KServe is a", before_test=[ensure_pvc_with_model], ), marks=[ pytest.mark.cluster_cpu, pytest.mark.cluster_multi_node, pytest.mark.pvc_storage, ], ), ], indirect=["test_case"], ids=generate_test_id, ) @log_execution def test_llm_inference_service(test_case: TestCase): # noqa: F811 inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) service_name = test_case.llm_service.metadata.name if not test_case.llm_service.metadata.annotations: test_case.llm_service.metadata.annotations = {} test_case.llm_service.metadata.annotations[ "security.opendatahub.io/enable-auth" ] = "false" prefix = test_case.log_prefix test_failed = False try: print(f"{prefix} Creating LLMInferenceService {service_name}") create_llmisvc(kserve_client, test_case.llm_service) print(f"{prefix} Waiting for LLMInferenceService {service_name} to be ready") > wait_for_llm_isvc_ready( kserve_client, test_case.llm_service, test_case.wait_timeout ) llmisvc/test_llm_inference_service.py:807: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<kserve.api.kserve_client.KServeClient object at 0x7feb353aab90>, {'api_version': 'serving.kserve.io/v1alpha1', 'kin...ef-sc-67492bcd'}, {'name': 'workload-llmd-simulator-schedul-b2f159da'}]}, 'status': None}, 900) kwargs = {}, func_name = 'wait_for_llm_isvc_ready' timestamp_start = '2026-07-14T16:14:00.651748', start_time = 1784045640.6520207 duration = 370.385338306427, timestamp_end = '2026-07-14T16:20:11.037362' @functools.wraps(func) def wrapper(*args, **kwargs): func_name = func.__name__ timestamp_start = datetime.now().isoformat() logger.info( f"[{func_name}] [{timestamp_start}] start - args={args}, kwargs={kwargs}" ) start_time = time.time() try: > result = func(*args, **kwargs) llmisvc/logging.py:40: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb353aab90> given = {'api_version': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceService', 'metadata': {'annotations': {'security....gmap-ref-sc-67492bcd'}, {'name': 'workload-llmd-simulator-schedul-b2f159da'}]}, 'status': None} timeout_seconds = 900 @log_execution def wait_for_llm_isvc_ready( kserve_client: KServeClient, given: V1alpha1LLMInferenceService, timeout_seconds: int = 900, ) -> str: def assert_llm_isvc_ready(): out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) if "status" not in out: raise AssertionError("No status found in LLM inference service") status = out["status"] if "conditions" not in status: raise AssertionError("No conditions found in status") expected_true_conditions = {"Ready", "WorkloadsReady", "RouterReady"} got_true_conditions = set() conditions = status["conditions"] for condition in conditions: if condition.get("status") == "True": got_true_conditions.add(condition.get("type")) missing_conditions = expected_true_conditions - got_true_conditions if missing_conditions: raise AssertionError( f"Missing true conditions: {missing_conditions}, expected {expected_true_conditions}, got {conditions}" ) return True > return wait_for(assert_llm_isvc_ready, timeout=timeout_seconds, interval=1.0) llmisvc/test_llm_inference_service.py:1205: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ assertion_fn = <function wait_for_llm_isvc_ready.<locals>.assert_llm_isvc_ready at 0x7feb354da480> timeout = 900, interval = 1.0 def wait_for( assertion_fn: Callable[[], Any], timeout: float = 5.0, interval: float = 0.1 ) -> Any: """Wait for the assertion to succeed within timeout.""" deadline = time.time() + timeout last_msg = None while True: try: > return assertion_fn() llmisvc/test_llm_inference_service.py:1216: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ def assert_llm_isvc_ready(): > out = get_llmisvc( kserve_client, given.metadata.name, given.metadata.namespace, given.api_version.split("/")[1], ) llmisvc/test_llm_inference_service.py:1175: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb353aab90> name = 'scheduler-configmap-ref-test', namespace = 'kserve-ci-e2e-test' version = 'v1alpha1' def get_llmisvc( kserve_client: KServeClient, name, namespace, version=constants.KSERVE_V1ALPHA1_VERSION, ): try: return kserve_client.api_instance.get_namespaced_custom_object( constants.KSERVE_GROUP, version, namespace, KSERVE_PLURAL_LLMINFERENCESERVICE, name, ) except client.rest.ApiException as e: > raise RuntimeError( f"❌ Exception when calling CustomObjectsApi->" f"get_namespaced_custom_object for LLMInferenceService: {e}" ) from e E RuntimeError: ❌ Exception when calling CustomObjectsApi->get_namespaced_custom_object for LLMInferenceService: (500) E Reason: Internal Server Error E HTTP response headers: HTTPHeaderDict({'Audit-Id': '0f8a21c4-7317-45e3-a15e-c82447ad1cc0', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload', 'X-Kubernetes-Pf-Flowschema-Uid': '2a999f06-9419-439a-ad27-f84423daf280', 'X-Kubernetes-Pf-Prioritylevel-Uid': '7646e888-06ea-42cb-b71d-ec0f1b0b2888', 'Date': 'Tue, 14 Jul 2026 16:20:11 GMT', 'Content-Length': '292'}) E HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"conversion webhook for serving.kserve.io/v1alpha2, Kind=LLMInferenceService failed: Post \"https://llmisvc-webhook-server-service.kserve.svc:443/convert?timeout=30s\": net/http: TLS handshake timeout","code":500} llmisvc/test_llm_inference_service.py:1052: RuntimeErrorkserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb35387810> llm_config = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} namespace = 'kserve-ci-e2e-test' def _create_or_update_llmisvc_config(kserve_client, llm_config, namespace=None): """Create or update an LLMInferenceServiceConfig resource.""" version = llm_config["apiVersion"].split("/")[1] if namespace is None: namespace = llm_config.get("metadata", {}).get("namespace", "default") name = llm_config.get("metadata", {}).get("name") if not name: raise ValueError("LLMInferenceServiceConfig must have a name in metadata") logger.info(f"Checking LLMInferenceServiceConfig {name} in namespace {namespace}") try: > existing_config = kserve_client.api_instance.get_namespaced_custom_object( constants.KSERVE_GROUP, version, namespace, KSERVE_PLURAL_LLMINFERENCESERVICECONFIG, name, ) llmisvc/fixtures.py:1671: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api.custom_objects_api.CustomObjectsApi object at 0x7feb3510c0d0> group = 'serving.kserve.io', version = 'v1alpha1' namespace = 'kserve-ci-e2e-test', plural = 'llminferenceserviceconfigs' name = 'router-managed-scheduler-ha-rep-bf59a7b5' kwargs = {'_return_http_data_only': True} def get_namespaced_custom_object(self, group, version, namespace, plural, name, **kwargs): # noqa: E501 """get_namespaced_custom_object # noqa: E501 Returns a namespace scoped custom object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_namespaced_custom_object(group, version, namespace, plural, name, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str group: the custom resource's group (required) :param str version: the custom resource's version (required) :param str namespace: The custom resource's namespace (required) :param str plural: the custom resource's plural name. For TPRs this would be lowercase plural kind. (required) :param str name: the custom object's name (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: object If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True > return self.get_namespaced_custom_object_with_http_info(group, version, namespace, plural, name, **kwargs) # noqa: E501 ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api/custom_objects_api.py:1632: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api.custom_objects_api.CustomObjectsApi object at 0x7feb3510c0d0> group = 'serving.kserve.io', version = 'v1alpha1' namespace = 'kserve-ci-e2e-test', plural = 'llminferenceserviceconfigs' name = 'router-managed-scheduler-ha-rep-bf59a7b5' kwargs = {'_return_http_data_only': True} local_var_params = {'_return_http_data_only': True, 'all_params': ['group', 'version', 'namespace', 'plural', 'name', 'async_req', ...], 'auth_settings': ['BearerToken'], 'body_params': None, ...} all_params = ['group', 'version', 'namespace', 'plural', 'name', 'async_req', ...] key = '_return_http_data_only', val = True, collection_formats = {} path_params = {'group': 'serving.kserve.io', 'name': 'router-managed-scheduler-ha-rep-bf59a7b5', 'namespace': 'kserve-ci-e2e-test', 'plural': 'llminferenceserviceconfigs', ...} query_params = [] def get_namespaced_custom_object_with_http_info(self, group, version, namespace, plural, name, **kwargs): # noqa: E501 """get_namespaced_custom_object # noqa: E501 Returns a namespace scoped custom object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_namespaced_custom_object_with_http_info(group, version, namespace, plural, name, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str group: the custom resource's group (required) :param str version: the custom resource's version (required) :param str namespace: The custom resource's namespace (required) :param str plural: the custom resource's plural name. For TPRs this would be lowercase plural kind. (required) :param str name: the custom object's name (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(object, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'group', 'version', 'namespace', 'plural', 'name' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_namespaced_custom_object" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'group' is set if self.api_client.client_side_validation and ('group' not in local_var_params or # noqa: E501 local_var_params['group'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `group` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'version' is set if self.api_client.client_side_validation and ('version' not in local_var_params or # noqa: E501 local_var_params['version'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `version` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'namespace' is set if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501 local_var_params['namespace'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `namespace` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'plural' is set if self.api_client.client_side_validation and ('plural' not in local_var_params or # noqa: E501 local_var_params['plural'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `plural` when calling `get_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'name' is set if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501 local_var_params['name'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `name` when calling `get_namespaced_custom_object`") # noqa: E501 collection_formats = {} path_params = {} if 'group' in local_var_params: path_params['group'] = local_var_params['group'] # noqa: E501 if 'version' in local_var_params: path_params['version'] = local_var_params['version'] # noqa: E501 if 'namespace' in local_var_params: path_params['namespace'] = local_var_params['namespace'] # noqa: E501 if 'plural' in local_var_params: path_params['plural'] = local_var_params['plural'] # noqa: E501 if 'name' in local_var_params: path_params['name'] = local_var_params['name'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 > return self.api_client.call_api( '/apis/{group}/{version}/namespaces/{namespace}/{plural}/{name}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='object', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api/custom_objects_api.py:1739: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3510fa90> resource_path = '/apis/{group}/{version}/namespaces/{namespace}/{plural}/{name}' method = 'GET' path_params = {'group': 'serving.kserve.io', 'name': 'router-managed-scheduler-ha-rep-bf59a7b5', 'namespace': 'kserve-ci-e2e-test', 'plural': 'llminferenceserviceconfigs', ...} query_params = [] header_params = {'Accept': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = None, post_params = [], files = {}, response_type = 'object' auth_settings = ['BearerToken'], async_req = None, _return_http_data_only = True collection_formats = {}, _preload_content = True, _request_timeout = None _host = None def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, async_req=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None): """Makes the HTTP request (synchronous) and returns deserialized data. To make an async_req request, set the async_req parameter. :param resource_path: Path to method endpoint. :param method: Method to call. :param path_params: Path parameters in the url. :param query_params: Query parameters in the url. :param header_params: Header parameters to be placed in the request header. :param body: Request body. :param post_params dict: Request post form parameters, for `application/x-www-form-urlencoded`, `multipart/form-data`. :param auth_settings list: Auth Settings names for the request. :param response: Response data type. :param files dict: key -> filename, value -> filepath, for `multipart/form-data`. :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param collection_formats: dict of collection formats for path, query, header, and post parameters. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: If async_req parameter is True, the request will be called asynchronously. The method will return the request thread. If parameter async_req is False or missing, then the method will return the response directly. """ if not async_req: > return self.__call_api(resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:348: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3510fa90> resource_path = '/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs/router-managed-scheduler-ha-rep-bf59a7b5' method = 'GET' path_params = [('group', 'serving.kserve.io'), ('version', 'v1alpha1'), ('namespace', 'kserve-ci-e2e-test'), ('plural', 'llminferenceserviceconfigs'), ('name', 'router-managed-scheduler-ha-rep-bf59a7b5')] query_params = [] header_params = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = None, post_params = [], files = {}, response_type = 'object' auth_settings = ['BearerToken'], _return_http_data_only = True collection_formats = {}, _preload_content = True, _request_timeout = None _host = None def __call_api( self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None): config = self.configuration # header parameters header_params = header_params or {} header_params.update(self.default_headers) if self.cookie: header_params['Cookie'] = self.cookie if header_params: header_params = self.sanitize_for_serialization(header_params) header_params = dict(self.parameters_to_tuples(header_params, collection_formats)) # path parameters if path_params: path_params = self.sanitize_for_serialization(path_params) path_params = self.parameters_to_tuples(path_params, collection_formats) for k, v in path_params: # specified safe chars, encode everything resource_path = resource_path.replace( '{%s}' % k, quote(str(v), safe=config.safe_chars_for_path_param) ) # query parameters if query_params: query_params = self.sanitize_for_serialization(query_params) query_params = self.parameters_to_tuples(query_params, collection_formats) # post parameters if post_params or files: post_params = post_params if post_params else [] post_params = self.sanitize_for_serialization(post_params) post_params = self.parameters_to_tuples(post_params, collection_formats) post_params.extend(self.files_parameters(files)) # auth setting self.update_params_for_auth(header_params, query_params, auth_settings) # body if body: body = self.sanitize_for_serialization(body) # request url if _host is None: url = self.configuration.host + resource_path else: # use server/host defined in path or operation instead url = _host + resource_path # perform request and return response > response_data = self.request( method, url, query_params=query_params, headers=header_params, post_params=post_params, body=body, _preload_content=_preload_content, _request_timeout=_request_timeout) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:180: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3510fa90> method = 'GET' url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs/router-managed-scheduler-ha-rep-bf59a7b5' query_params = [] headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} post_params = [], body = None, _preload_content = True, _request_timeout = None def request(self, method, url, query_params=None, headers=None, post_params=None, body=None, _preload_content=True, _request_timeout=None): """Makes the HTTP request using RESTClient.""" if method == "GET": > return self.rest_client.GET(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:373: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.rest.RESTClientObject object at 0x7feb3510e910> url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs/router-managed-scheduler-ha-rep-bf59a7b5' headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} query_params = [], _preload_content = True, _request_timeout = None def GET(self, url, headers=None, query_params=None, _preload_content=True, _request_timeout=None): > return self.request("GET", url, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout, query_params=query_params) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/rest.py:244: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.rest.RESTClientObject object at 0x7feb3510e910> method = 'GET' url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs/router-managed-scheduler-ha-rep-bf59a7b5' query_params = [] headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = None, post_params = {}, _preload_content = True, _request_timeout = None def request(self, method, url, query_params=None, headers=None, body=None, post_params=None, _preload_content=True, _request_timeout=None): """Perform requests. :param method: http request method :param url: http request url :param query_params: query parameters in the url :param headers: http request headers :param body: request json body, for `application/json` :param post_params: request post parameters, `application/x-www-form-urlencoded` and `multipart/form-data` :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. """ method = method.upper() assert method in ['GET', 'HEAD', 'DELETE', 'POST', 'PUT', 'PATCH', 'OPTIONS'] if post_params and body: raise ApiValueError( "body parameter cannot be used with post_params parameter." ) post_params = post_params or {} headers = headers or {} timeout = None if _request_timeout: if isinstance(_request_timeout, (int, ) if six.PY3 else (int, long)): # noqa: E501,F821 timeout = urllib3.Timeout(total=_request_timeout) elif (isinstance(_request_timeout, tuple) and len(_request_timeout) == 2): timeout = urllib3.Timeout( connect=_request_timeout[0], read=_request_timeout[1]) if 'Content-Type' not in headers: headers['Content-Type'] = 'application/json' try: # For `POST`, `PUT`, `PATCH`, `OPTIONS`, `DELETE` if method in ['POST', 'PUT', 'PATCH', 'OPTIONS', 'DELETE']: if query_params: url += '?' + urlencode(query_params) if (re.search('json', headers['Content-Type'], re.IGNORECASE) or headers['Content-Type'] == 'application/apply-patch+yaml'): if headers['Content-Type'] == 'application/json-patch+json': if not isinstance(body, list): headers['Content-Type'] = \ 'application/strategic-merge-patch+json' request_body = None if body is not None: request_body = json.dumps(body) r = self.pool_manager.request( method, url, body=request_body, preload_content=_preload_content, timeout=timeout, headers=headers) elif headers['Content-Type'] == 'application/x-www-form-urlencoded': # noqa: E501 r = self.pool_manager.request( method, url, fields=post_params, encode_multipart=False, preload_content=_preload_content, timeout=timeout, headers=headers) elif headers['Content-Type'] == 'multipart/form-data': # must del headers['Content-Type'], or the correct # Content-Type which generated by urllib3 will be # overwritten. del headers['Content-Type'] r = self.pool_manager.request( method, url, fields=post_params, encode_multipart=True, preload_content=_preload_content, timeout=timeout, headers=headers) # Pass a `string` parameter directly in the body to support # other content types than Json when `body` argument is # provided in serialized form elif isinstance(body, str) or isinstance(body, bytes): request_body = body r = self.pool_manager.request( method, url, body=request_body, preload_content=_preload_content, timeout=timeout, headers=headers) else: # Cannot generate the request from given parameters msg = """Cannot prepare a request message for provided arguments. Please check that your arguments match declared content type.""" raise ApiException(status=0, reason=msg) # For `GET`, `HEAD` else: r = self.pool_manager.request(method, url, fields=query_params, preload_content=_preload_content, timeout=timeout, headers=headers) except urllib3.exceptions.SSLError as e: msg = "{0}\n{1}".format(type(e).__name__, str(e)) raise ApiException(status=0, reason=msg) if _preload_content: r = RESTResponse(r) # In the python 3, the response.data is bytes. # we need to decode it to string. if six.PY3: r.data = r.data.decode('utf8') # log response body logger.debug("response body: %s", r.data) if not 200 <= r.status <= 299: > raise ApiException(http_resp=r) E kubernetes.client.exceptions.ApiException: (404) E Reason: Not Found E HTTP response headers: HTTPHeaderDict({'Audit-Id': 'e3ab75ba-f660-4d18-84be-736c88a71c31', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload', 'X-Kubernetes-Pf-Flowschema-Uid': '2a999f06-9419-439a-ad27-f84423daf280', 'X-Kubernetes-Pf-Prioritylevel-Uid': '7646e888-06ea-42cb-b71d-ec0f1b0b2888', 'Date': 'Tue, 14 Jul 2026 16:20:13 GMT', 'Content-Length': '338'}) E HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"llminferenceserviceconfigs.serving.kserve.io \"router-managed-scheduler-ha-rep-bf59a7b5\" not found","reason":"NotFound","details":{"name":"router-managed-scheduler-ha-rep-bf59a7b5","group":"serving.kserve.io","kind":"llminferenceserviceconfigs"},"code":404} ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/rest.py:238: ApiException During handling of the above exception, another exception occurred: request = <SubRequest 'ensure_gateway_proxy_memory' for <Function test_llm_inference_service[router-managed-scheduler-with-replicas-workload-llmd-simulator]>> @pytest.fixture(autouse=True) def ensure_gateway_proxy_memory(request): """After test setup creates gateways, patch them for proxy memory.""" if not GATEWAY_PROXY_MEMORY: return # Establish ordering: if the test uses test_case (llmisvc), let it run first if "test_case" in request.fixturenames: > request.getfixturevalue("test_case") common/gateway_proxy_istio.py:159: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ request = <SubRequest 'test_case' for <Function test_llm_inference_service[router-managed-scheduler-with-replicas-workload-llmd-simulator]>> @pytest.fixture(scope="function") def test_case(request): tc = request.param inject_k8s_proxy() kserve_client = KServeClient( config_file=os.environ.get("KUBECONFIG", "~/.kube/config"), client_configuration=client.Configuration(), ) # Execute before test hooks try: for func in tc.before_test: func() except Exception as before_test_error: raise RuntimeError( f"Failed to execute before test hook: {before_test_error}" ) from before_test_error try: > _setup_test_case_service(kserve_client, tc, request.node.name) llmisvc/fixtures.py:1558: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb35387810> tc = TestCase(base_refs=['router-managed', 'scheduler-with-replicas', 'workload-llmd-simulator'], prompt='KServe is a', ser...None, expected_gateway=None, before_test=[], after_test=[], peers=[], llm_service=None, model_name='facebook/opt-125m') test_node_name = 'test_llm_inference_service[router-managed-scheduler-with-replicas-workload-llmd-simulator]' peer_index = None def _setup_test_case_service(kserve_client, tc, test_node_name, peer_index=None): """Create LLMInferenceServiceConfigs and build the LLMInferenceService for a TestCase. Returns a list of created config names for cleanup tracking. """ missing_refs = [ ref for ref in tc.base_refs if ref not in LLMINFERENCESERVICE_CONFIGS ] if missing_refs: raise ValueError( f"Missing base_refs in LLMINFERENCESERVICE_CONFIGS: {missing_refs}" ) if not tc.service_name: suffix = f"-peer-{peer_index}" if peer_index is not None else "" tc.service_name = generate_service_name(test_node_name + suffix, tc.base_refs) if tc.model_name == "default/model": tc.model_name = _get_model_name_from_configs(tc.base_refs) created_configs = [] unique_base_refs = [] for base_ref in tc.base_refs: unique_config_name = generate_k8s_safe_suffix(base_ref, [tc.service_name]) unique_base_refs.append(unique_config_name) unique_config_body = { "apiVersion": "serving.kserve.io/v1alpha1", "kind": "LLMInferenceServiceConfig", "metadata": { "name": unique_config_name, "namespace": KSERVE_TEST_NAMESPACE, }, "spec": LLMINFERENCESERVICE_CONFIGS[base_ref], } > _create_or_update_llmisvc_config( kserve_client, unique_config_body, KSERVE_TEST_NAMESPACE ) llmisvc/fixtures.py:1518: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ kserve_client = <kserve.api.kserve_client.KServeClient object at 0x7feb35387810> llm_config = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} namespace = 'kserve-ci-e2e-test' def _create_or_update_llmisvc_config(kserve_client, llm_config, namespace=None): """Create or update an LLMInferenceServiceConfig resource.""" version = llm_config["apiVersion"].split("/")[1] if namespace is None: namespace = llm_config.get("metadata", {}).get("namespace", "default") name = llm_config.get("metadata", {}).get("name") if not name: raise ValueError("LLMInferenceServiceConfig must have a name in metadata") logger.info(f"Checking LLMInferenceServiceConfig {name} in namespace {namespace}") try: existing_config = kserve_client.api_instance.get_namespaced_custom_object( constants.KSERVE_GROUP, version, namespace, KSERVE_PLURAL_LLMINFERENCESERVICECONFIG, name, ) llm_config["metadata"] = existing_config["metadata"] outputs = kserve_client.api_instance.replace_namespaced_custom_object( constants.KSERVE_GROUP, version, namespace, KSERVE_PLURAL_LLMINFERENCESERVICECONFIG, name, llm_config, ) logger.info(f"✓ Successfully updated LLMInferenceServiceConfig {name}") return outputs except client.rest.ApiException as e: if e.status == 404: # Not found - create it logger.info( f"Resource not found, creating LLMInferenceServiceConfig {name}" ) > outputs = kserve_client.api_instance.create_namespaced_custom_object( constants.KSERVE_GROUP, version, namespace, KSERVE_PLURAL_LLMINFERENCESERVICECONFIG, llm_config, ) llmisvc/fixtures.py:1697: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api.custom_objects_api.CustomObjectsApi object at 0x7feb3510c0d0> group = 'serving.kserve.io', version = 'v1alpha1' namespace = 'kserve-ci-e2e-test', plural = 'llminferenceserviceconfigs' body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} kwargs = {'_return_http_data_only': True} def create_namespaced_custom_object(self, group, version, namespace, plural, body, **kwargs): # noqa: E501 """create_namespaced_custom_object # noqa: E501 Creates a namespace scoped Custom object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_namespaced_custom_object(group, version, namespace, plural, body, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str group: The custom resource's group name (required) :param str version: The custom resource's version (required) :param str namespace: The custom resource's namespace (required) :param str plural: The custom resource's plural name. For TPRs this would be lowercase plural kind. (required) :param object body: The JSON schema of the Resource to create. (required) :param str pretty: If 'true', then the output is pretty printed. :param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed :param str field_manager: fieldManager is a name associated with the actor or entity that is making these changes. The value must be less than or 128 characters long, and only contain printable characters, as defined by https://golang.org/pkg/unicode/#IsPrint. :param str field_validation: fieldValidation instructs the server on how to handle objects in the request (POST/PUT/PATCH) containing unknown or duplicate fields. Valid values are: - Ignore: This will ignore any unknown fields that are silently dropped from the object, and will ignore all but the last duplicate field that the decoder encounters. This is the default behavior prior to v1.23. - Warn: This will send a warning via the standard warning response header for each unknown field that is dropped from the object, and for each duplicate field that is encountered. The request will still succeed if there are no other errors, and will only persist the last of any duplicate fields. This is the default in v1.23+ - Strict: This will fail the request with a BadRequest error if any unknown fields would be dropped from the object, or if any duplicate fields are present. The error returned from the server will contain all unknown and duplicate fields encountered. (optional) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: object If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True > return self.create_namespaced_custom_object_with_http_info(group, version, namespace, plural, body, **kwargs) # noqa: E501 ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api/custom_objects_api.py:231: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api.custom_objects_api.CustomObjectsApi object at 0x7feb3510c0d0> group = 'serving.kserve.io', version = 'v1alpha1' namespace = 'kserve-ci-e2e-test', plural = 'llminferenceserviceconfigs' body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} kwargs = {'_return_http_data_only': True} local_var_params = {'_return_http_data_only': True, 'all_params': ['group', 'version', 'namespace', 'plural', 'body', 'pretty', ...], 'au...i-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [...]}}}}}, ...} all_params = ['group', 'version', 'namespace', 'plural', 'body', 'pretty', ...] key = '_return_http_data_only', val = True, collection_formats = {} path_params = {'group': 'serving.kserve.io', 'namespace': 'kserve-ci-e2e-test', 'plural': 'llminferenceserviceconfigs', 'version': 'v1alpha1'} query_params = [] def create_namespaced_custom_object_with_http_info(self, group, version, namespace, plural, body, **kwargs): # noqa: E501 """create_namespaced_custom_object # noqa: E501 Creates a namespace scoped Custom object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_namespaced_custom_object_with_http_info(group, version, namespace, plural, body, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str group: The custom resource's group name (required) :param str version: The custom resource's version (required) :param str namespace: The custom resource's namespace (required) :param str plural: The custom resource's plural name. For TPRs this would be lowercase plural kind. (required) :param object body: The JSON schema of the Resource to create. (required) :param str pretty: If 'true', then the output is pretty printed. :param str dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed :param str field_manager: fieldManager is a name associated with the actor or entity that is making these changes. The value must be less than or 128 characters long, and only contain printable characters, as defined by https://golang.org/pkg/unicode/#IsPrint. :param str field_validation: fieldValidation instructs the server on how to handle objects in the request (POST/PUT/PATCH) containing unknown or duplicate fields. Valid values are: - Ignore: This will ignore any unknown fields that are silently dropped from the object, and will ignore all but the last duplicate field that the decoder encounters. This is the default behavior prior to v1.23. - Warn: This will send a warning via the standard warning response header for each unknown field that is dropped from the object, and for each duplicate field that is encountered. The request will still succeed if there are no other errors, and will only persist the last of any duplicate fields. This is the default in v1.23+ - Strict: This will fail the request with a BadRequest error if any unknown fields would be dropped from the object, or if any duplicate fields are present. The error returned from the server will contain all unknown and duplicate fields encountered. (optional) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(object, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'group', 'version', 'namespace', 'plural', 'body', 'pretty', 'dry_run', 'field_manager', 'field_validation' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method create_namespaced_custom_object" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'group' is set if self.api_client.client_side_validation and ('group' not in local_var_params or # noqa: E501 local_var_params['group'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `group` when calling `create_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'version' is set if self.api_client.client_side_validation and ('version' not in local_var_params or # noqa: E501 local_var_params['version'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `version` when calling `create_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'namespace' is set if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501 local_var_params['namespace'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `namespace` when calling `create_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'plural' is set if self.api_client.client_side_validation and ('plural' not in local_var_params or # noqa: E501 local_var_params['plural'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `plural` when calling `create_namespaced_custom_object`") # noqa: E501 # verify the required parameter 'body' is set if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501 local_var_params['body'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `body` when calling `create_namespaced_custom_object`") # noqa: E501 collection_formats = {} path_params = {} if 'group' in local_var_params: path_params['group'] = local_var_params['group'] # noqa: E501 if 'version' in local_var_params: path_params['version'] = local_var_params['version'] # noqa: E501 if 'namespace' in local_var_params: path_params['namespace'] = local_var_params['namespace'] # noqa: E501 if 'plural' in local_var_params: path_params['plural'] = local_var_params['plural'] # noqa: E501 query_params = [] if 'pretty' in local_var_params and local_var_params['pretty'] is not None: # noqa: E501 query_params.append(('pretty', local_var_params['pretty'])) # noqa: E501 if 'dry_run' in local_var_params and local_var_params['dry_run'] is not None: # noqa: E501 query_params.append(('dryRun', local_var_params['dry_run'])) # noqa: E501 if 'field_manager' in local_var_params and local_var_params['field_manager'] is not None: # noqa: E501 query_params.append(('fieldManager', local_var_params['field_manager'])) # noqa: E501 if 'field_validation' in local_var_params and local_var_params['field_validation'] is not None: # noqa: E501 query_params.append(('fieldValidation', local_var_params['field_validation'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in local_var_params: body_params = local_var_params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 > return self.api_client.call_api( '/apis/{group}/{version}/namespaces/{namespace}/{plural}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='object', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api/custom_objects_api.py:354: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3510fa90> resource_path = '/apis/{group}/{version}/namespaces/{namespace}/{plural}' method = 'POST' path_params = {'group': 'serving.kserve.io', 'namespace': 'kserve-ci-e2e-test', 'plural': 'llminferenceserviceconfigs', 'version': 'v1alpha1'} query_params = [] header_params = {'Accept': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} post_params = [], files = {}, response_type = 'object' auth_settings = ['BearerToken'], async_req = None, _return_http_data_only = True collection_formats = {}, _preload_content = True, _request_timeout = None _host = None def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, async_req=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None): """Makes the HTTP request (synchronous) and returns deserialized data. To make an async_req request, set the async_req parameter. :param resource_path: Path to method endpoint. :param method: Method to call. :param path_params: Path parameters in the url. :param query_params: Query parameters in the url. :param header_params: Header parameters to be placed in the request header. :param body: Request body. :param post_params dict: Request post form parameters, for `application/x-www-form-urlencoded`, `multipart/form-data`. :param auth_settings list: Auth Settings names for the request. :param response: Response data type. :param files dict: key -> filename, value -> filepath, for `multipart/form-data`. :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param collection_formats: dict of collection formats for path, query, header, and post parameters. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: If async_req parameter is True, the request will be called asynchronously. The method will return the request thread. If parameter async_req is False or missing, then the method will return the response directly. """ if not async_req: > return self.__call_api(resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:348: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3510fa90> resource_path = '/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs' method = 'POST' path_params = [('group', 'serving.kserve.io'), ('version', 'v1alpha1'), ('namespace', 'kserve-ci-e2e-test'), ('plural', 'llminferenceserviceconfigs')] query_params = [] header_params = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} post_params = [], files = {}, response_type = 'object' auth_settings = ['BearerToken'], _return_http_data_only = True collection_formats = {}, _preload_content = True, _request_timeout = None _host = None def __call_api( self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None): config = self.configuration # header parameters header_params = header_params or {} header_params.update(self.default_headers) if self.cookie: header_params['Cookie'] = self.cookie if header_params: header_params = self.sanitize_for_serialization(header_params) header_params = dict(self.parameters_to_tuples(header_params, collection_formats)) # path parameters if path_params: path_params = self.sanitize_for_serialization(path_params) path_params = self.parameters_to_tuples(path_params, collection_formats) for k, v in path_params: # specified safe chars, encode everything resource_path = resource_path.replace( '{%s}' % k, quote(str(v), safe=config.safe_chars_for_path_param) ) # query parameters if query_params: query_params = self.sanitize_for_serialization(query_params) query_params = self.parameters_to_tuples(query_params, collection_formats) # post parameters if post_params or files: post_params = post_params if post_params else [] post_params = self.sanitize_for_serialization(post_params) post_params = self.parameters_to_tuples(post_params, collection_formats) post_params.extend(self.files_parameters(files)) # auth setting self.update_params_for_auth(header_params, query_params, auth_settings) # body if body: body = self.sanitize_for_serialization(body) # request url if _host is None: url = self.configuration.host + resource_path else: # use server/host defined in path or operation instead url = _host + resource_path # perform request and return response > response_data = self.request( method, url, query_params=query_params, headers=header_params, post_params=post_params, body=body, _preload_content=_preload_content, _request_timeout=_request_timeout) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:180: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.api_client.ApiClient object at 0x7feb3510fa90> method = 'POST' url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs' query_params = [] headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} post_params = [] body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} _preload_content = True, _request_timeout = None def request(self, method, url, query_params=None, headers=None, post_params=None, body=None, _preload_content=True, _request_timeout=None): """Makes the HTTP request using RESTClient.""" if method == "GET": return self.rest_client.GET(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers) elif method == "HEAD": return self.rest_client.HEAD(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers) elif method == "OPTIONS": return self.rest_client.OPTIONS(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout) elif method == "POST": > return self.rest_client.POST(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/api_client.py:391: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.rest.RESTClientObject object at 0x7feb3510e910> url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs' headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} query_params = [], post_params = [] body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} _preload_content = True, _request_timeout = None def POST(self, url, headers=None, query_params=None, post_params=None, body=None, _preload_content=True, _request_timeout=None): > return self.request("POST", url, headers=headers, query_params=query_params, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body) ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/rest.py:279: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <kubernetes.client.rest.RESTClientObject object at 0x7feb3510e910> method = 'POST' url = 'https://a135768385b8548aeb30cc8b7f8c4c78-4e67f7c4ac32c090.elb.us-east-1.amazonaws.com:6443/apis/serving.kserve.io/v1alpha1/namespaces/kserve-ci-e2e-test/llminferenceserviceconfigs' query_params = [] headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'User-Agent': 'OpenAPI-Generator/32.0.1/python'} body = {'apiVersion': 'serving.kserve.io/v1alpha1', 'kind': 'LLMInferenceServiceConfig', 'metadata': {'name': 'router-managed...ve-ci-e2e-test'}, 'spec': {'router': {'gateway': {}, 'route': {}, 'scheduler': {'template': {'containers': [{...}]}}}}} post_params = {}, _preload_content = True, _request_timeout = None def request(self, method, url, query_params=None, headers=None, body=None, post_params=None, _preload_content=True, _request_timeout=None): """Perform requests. :param method: http request method :param url: http request url :param query_params: query parameters in the url :param headers: http request headers :param body: request json body, for `application/json` :param post_params: request post parameters, `application/x-www-form-urlencoded` and `multipart/form-data` :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. """ method = method.upper() assert method in ['GET', 'HEAD', 'DELETE', 'POST', 'PUT', 'PATCH', 'OPTIONS'] if post_params and body: raise ApiValueError( "body parameter cannot be used with post_params parameter." ) post_params = post_params or {} headers = headers or {} timeout = None if _request_timeout: if isinstance(_request_timeout, (int, ) if six.PY3 else (int, long)): # noqa: E501,F821 timeout = urllib3.Timeout(total=_request_timeout) elif (isinstance(_request_timeout, tuple) and len(_request_timeout) == 2): timeout = urllib3.Timeout( connect=_request_timeout[0], read=_request_timeout[1]) if 'Content-Type' not in headers: headers['Content-Type'] = 'application/json' try: # For `POST`, `PUT`, `PATCH`, `OPTIONS`, `DELETE` if method in ['POST', 'PUT', 'PATCH', 'OPTIONS', 'DELETE']: if query_params: url += '?' + urlencode(query_params) if (re.search('json', headers['Content-Type'], re.IGNORECASE) or headers['Content-Type'] == 'application/apply-patch+yaml'): if headers['Content-Type'] == 'application/json-patch+json': if not isinstance(body, list): headers['Content-Type'] = \ 'application/strategic-merge-patch+json' request_body = None if body is not None: request_body = json.dumps(body) r = self.pool_manager.request( method, url, body=request_body, preload_content=_preload_content, timeout=timeout, headers=headers) elif headers['Content-Type'] == 'application/x-www-form-urlencoded': # noqa: E501 r = self.pool_manager.request( method, url, fields=post_params, encode_multipart=False, preload_content=_preload_content, timeout=timeout, headers=headers) elif headers['Content-Type'] == 'multipart/form-data': # must del headers['Content-Type'], or the correct # Content-Type which generated by urllib3 will be # overwritten. del headers['Content-Type'] r = self.pool_manager.request( method, url, fields=post_params, encode_multipart=True, preload_content=_preload_content, timeout=timeout, headers=headers) # Pass a `string` parameter directly in the body to support # other content types than Json when `body` argument is # provided in serialized form elif isinstance(body, str) or isinstance(body, bytes): request_body = body r = self.pool_manager.request( method, url, body=request_body, preload_content=_preload_content, timeout=timeout, headers=headers) else: # Cannot generate the request from given parameters msg = """Cannot prepare a request message for provided arguments. Please check that your arguments match declared content type.""" raise ApiException(status=0, reason=msg) # For `GET`, `HEAD` else: r = self.pool_manager.request(method, url, fields=query_params, preload_content=_preload_content, timeout=timeout, headers=headers) except urllib3.exceptions.SSLError as e: msg = "{0}\n{1}".format(type(e).__name__, str(e)) raise ApiException(status=0, reason=msg) if _preload_content: r = RESTResponse(r) # In the python 3, the response.data is bytes. # we need to decode it to string. if six.PY3: r.data = r.data.decode('utf8') # log response body logger.debug("response body: %s", r.data) if not 200 <= r.status <= 299: > raise ApiException(http_resp=r) E kubernetes.client.exceptions.ApiException: (500) E Reason: Internal Server Error E HTTP response headers: HTTPHeaderDict({'Audit-Id': 'd74318b4-4b3c-46ed-8d76-4bc09654e289', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload', 'X-Kubernetes-Pf-Flowschema-Uid': '2a999f06-9419-439a-ad27-f84423daf280', 'X-Kubernetes-Pf-Prioritylevel-Uid': '7646e888-06ea-42cb-b71d-ec0f1b0b2888', 'Date': 'Tue, 14 Jul 2026 16:20:13 GMT', 'Content-Length': '701'}) E HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"Internal error occurred: failed calling webhook \"llminferenceserviceconfig.kserve-webhook-server.v1alpha1.validator\": failed to call webhook: Post \"https://llmisvc-webhook-server-service.kserve.svc:443/validate-serving-kserve-io-v1alpha1-llminferenceserviceconfig?timeout=10s\": EOF","reason":"InternalError","details":{"causes":[{"message":"failed calling webhook \"llminferenceserviceconfig.kserve-webhook-server.v1alpha1.validator\": failed to call webhook: Post \"https://llmisvc-webhook-server-service.kserve.svc:443/validate-serving-kserve-io-v1alpha1-llminferenceserviceconfig?timeout=10s\": EOF"}]},"code":500} ../../python/kserve/.venv/lib64/python3.11/site-packages/kubernetes/client/rest.py:238: ApiException