Migration of Keras Rest Protocol test on triton for Kserve (UI -> API) (

#2182) Co-authored-by: Tarun Kumar <takumar@redhat.com>
red-hat-data-services · Jan 20, 2025 · 7a1176f · 7a1176f
1 parent baee508
commit 7a1176f
Show file tree

Hide file tree

Showing 2 changed files with 47 additions and 2 deletions.
diff --git a/ods_ci/tests/Resources/Files/triton/kserve-triton-keras-rest-input.json b/ods_ci/tests/Resources/Files/triton/kserve-triton-keras-rest-input.json
diff --git a/...rving/1009__model_serving_triton_on_kserve/1009__model_serving_triton_on_kserve_api.robot b/...rving/1009__model_serving_triton_on_kserve/1009__model_serving_triton_on_kserve_api.robot
@@ -42,7 +42,9 @@ ${INFERENCESERVICE_FILLED_FILEPATH}=    ${INFERENCESERVICE_FILEPATH_NEW}/isvc_fi
 ${KSERVE_RUNTIME_REST_NAME}=  triton-kserve-runtime
 ${PYTORCH_MODEL_NAME}=    resnet50
 ${INFERENCE_REST_INPUT_PYTORCH}=    @tests/Resources/Files/triton/kserve-triton-resnet-rest-input.json
-${EXPECTED_INFERENCE_REST_OUTPUT_FILE__PYTORCH}=        tests/Resources/Files/triton/kserve-triton-resnet-rest-output.json
+${EXPECTED_INFERENCE_REST_OUTPUT_FILE_PYTORCH}=        tests/Resources/Files/triton/kserve-triton-resnet-rest-output.json
+${INFERENCE_REST_INPUT_KERAS}=    @tests/Resources/Files/triton/kserve-triton-keras-rest-input.json
+${EXPECTED_INFERENCE_REST_OUTPUT_FILE_KERAS}=        tests/Resources/Files/triton/kserve-triton-keras-rest-output.json
 ${PATTERN}=     https:\/\/([^\/:]+)
 ${PROTOBUFF_FILE}=      tests/Resources/Files/triton/grpc_predict_v2.proto
 
@@ -88,7 +90,7 @@ Test Python Model Rest Inference Via API (Triton on Kserve)    # robocop: off=to
     ...    Run Keyword If    "${KSERVE_MODE}"=="RawDeployment"    Terminate Process    triton-process    kill=true
 
 Test Pytorch Model Rest Inference Via API (Triton on Kserve)    # robocop: off=too-long-test-case
-    [Documentation]    Test the deployment of python model in Kserve using Triton
+    [Documentation]    Test the deployment of pytorch model in Kserve using Triton
     [Tags]    Tier2    RHOAIENG-16909
     Setup Test Variables    model_name=${PYTORCH_MODEL_NAME}    use_pvc=${FALSE}    use_gpu=${FALSE}
     ...    kserve_mode=${KSERVE_MODE}   model_path=triton/model_repository/
@@ -255,6 +257,48 @@ Test Onnx Model Grpc Inference Via API (Triton on Kserve)    # robocop: off=too-
     ...    isvc_names=${models_names}    wait_prj_deletion=${FALSE}    kserve_mode=${KSERVE_MODE}
     ...    AND
     ...    Run Keyword If    "${KSERVE_MODE}"=="RawDeployment"    Terminate Process    triton-process    kill=true
+
+Test Keras Model Rest Inference Via API (Triton on Kserve)    # robocop: off=too-long-test-case
+    [Documentation]    Test the deployment of keras model in Kserve using Triton
+    [Tags]    Tier2    RHOAIENG-16911
+    Setup Test Variables    model_name=${PYTORCH_MODEL_NAME}    use_pvc=${FALSE}    use_gpu=${FALSE}
+    ...    kserve_mode=${KSERVE_MODE}   model_path=tritonkeras/model_repository/
+    Set Project And Runtime    runtime=${KSERVE_RUNTIME_REST_NAME}     protocol=${PROTOCOL}     namespace=${test_namespace}
+    ...    download_in_pvc=${DOWNLOAD_IN_PVC}    model_name=${PYTORCH_MODEL_NAME}
+    ...    storage_size=100Mi    memory_request=100Mi
+    ${requests}=    Create Dictionary    memory=1Gi
+    Compile Inference Service YAML    isvc_name=${PYTORCH_MODEL_NAME}
+    ...    sa_name=models-bucket-sa
+    ...    model_storage_uri=${storage_uri}
+    ...    model_format=python  serving_runtime=${KSERVE_RUNTIME_REST_NAME}
+    ...    version="1"
+    ...    limits_dict=${limits}    requests_dict=${requests}    kserve_mode=${KSERVE_MODE}
+    Deploy Model Via CLI    isvc_filepath=${INFERENCESERVICE_FILLED_FILEPATH}
+    ...    namespace=${test_namespace}
+    # File is not needed anymore after applying
+    Remove File    ${INFERENCESERVICE_FILLED_FILEPATH}
+    Wait For Pods To Be Ready    label_selector=serving.kserve.io/inferenceservice=${PYTORCH_MODEL_NAME}
+    ...    namespace=${test_namespace}
+    ${pod_name}=  Get Pod Name    namespace=${test_namespace}
+    ...    label_selector=serving.kserve.io/inferenceservice=${PYTORCH_MODEL_NAME}
+    ${service_port}=    Extract Service Port    service_name=${PYTORCH_MODEL_NAME}-predictor    protocol=TCP
+    ...    namespace=${test_namespace}
+    IF   "${KSERVE_MODE}"=="RawDeployment"
+        Start Port-forwarding    namespace=${test_namespace}    pod_name=${pod_name}  local_port=${service_port}
+        ...    remote_port=${service_port}    process_alias=triton-process
+    END
+    ${EXPECTED_INFERENCE_REST_OUTPUT_KERAS}=     Load Json File
+    ...    file_path=${EXPECTED_INFERENCE_REST_OUTPUT_FILE_KERAS}    as_string=${TRUE}
+    Verify Model Inference With Retries   model_name=${PYTORCH_MODEL_NAME}    inference_input=${INFERENCE_REST_INPUT_KERAS}
+    ...    expected_inference_output=${EXPECTED_INFERENCE_REST_OUTPUT_KERAS}   project_title=${test_namespace}
+    ...    deployment_mode=Cli  kserve_mode=${KSERVE_MODE}    service_port=${service_port}
+    ...    end_point=/v2/models/${model_name}/infer   retries=3
+    [Teardown]    Run Keywords
+    ...    Clean Up Test Project    test_ns=${test_namespace}
+    ...    isvc_names=${models_names}    wait_prj_deletion=${FALSE}    kserve_mode=${KSERVE_MODE}
+    ...    AND
+    ...    Run Keyword If    "${KSERVE_MODE}"=="RawDeployment"    Terminate Process    triton-process    kill=true
+
 
 *** Keywords ***
 Suite Setup