Added Tensorflow model REST protocol test on triton for Kserve

ods_ci/tests/Resources/Files/triton/triton_tensorflow_rest_servingruntime.yaml

@@ -0,0 +1,54 @@
+apiVersion: serving.kserve.io/v1alpha1
+kind: ServingRuntime
+metadata:
+  name: triton-kserve-rest
+spec:
+  annotations:
+    prometheus.kserve.io/path: /metrics
+    prometheus.kserve.io/port: "8002"
+  containers:
+    - args:
+        - tritonserver
+        - --model-store=/mnt/models
+        - --grpc-port=9000
+        - --http-port=8080
+        - --allow-grpc=true
+        - --allow-http=true
+      image: nvcr.io/nvidia/tritonserver:23.05-py3
+      name: kserve-container
+      resources:
+        limits:
+          cpu: "1"
+          memory: 2Gi
+        requests:
+          cpu: "1"
+          memory: 2Gi
+      ports:
+        - containerPort: 8080
+          protocol: TCP
+  protocolVersions:
+    - v2
+    - grpc-v2
+  supportedModelFormats:
+    - autoSelect: true
+      name: tensorrt
+      priority: 1
+      version: "8"
+    - autoSelect: true
+      name: tensorflow
+      priority: 1
+      version: "1"
+    - autoSelect: true
+      name: tensorflow
+      priority: 1
+      version: "2"
+    - autoSelect: true
+      name: onnx
+      priority: 1
+      version: "1"
+    - name: pytorch
+      version: "1"
+    - autoSelect: true
+      name: triton
+      priority: 1
+      version: "2"

ods_ci/tests/Tests/1000__model_serving/1009__model_serving_triton_on_kserve/1009__model_serving_triton_on_kserve.robot

@@ -39,7 +39,9 @@
 ${PYTORCH_RUNTIME_NAME}=    triton-kserve-rest
 ${PYTORCH_RUNTIME_FILEPATH}=    ${RESOURCES_DIRPATH}/triton_onnx_rest_servingruntime.yaml
 ${EXPECTED_INFERENCE_REST_OUTPUT_FILE_PYTORCH}=       tests/Resources/Files/triton/kserve-triton-resnet-rest-output.json
-
+${INFERENCE_REST_INPUT_TENSORFLOW}=    @tests/Resources/Files/triton/kserve-triton-tensorflow-rest-input.json
+${TENSORFLOW_RUNTIME_FILEPATH}=    ${RESOURCES_DIRPATH}/triton_tensorflow_rest_servingruntime.yaml
+${EXPECTED_INFERENCE_REST_OUTPUT_FILE_TENSORFLOW}=       tests/Resources/Files/triton/kserve-triton-tensorflow-rest-output.json
 
 
 *** Test Cases ***
@@ -153,6 +155,35 @@
     ...  Delete Serving Runtime Template From CLI    displayed_name=triton-kserve-grpc
 
 
+Test Tensorflow Model Rest Inference Via UI (Triton on Kserve)    # robocop: off=too-long-test-case
+    [Documentation]    Test the deployment of an onnx model in Kserve using Triton
+    [Tags]    Sanity    RHOAIENG-11568
+    Open Data Science Projects Home Page
+    Create Data Science Project    title=${PRJ_TITLE}    description=${PRJ_DESCRIPTION}
+    ...    existing_project=${FALSE}
+    Open Dashboard Settings    settings_page=Serving runtimes
+    Upload Serving Runtime Template    runtime_filepath=${TENSORFLOW_RUNTIME_FILEPATH}
+    ...    serving_platform=single      runtime_protocol=REST
+    Serving Runtime Template Should Be Listed    displayed_name=${PYTORCH_RUNTIME_NAME}
+    ...    serving_platform=single
+    Recreate S3 Data Connection    project_title=${PRJ_TITLE}    dc_name=model-serving-connection
+    ...            aws_access_key=${S3.AWS_ACCESS_KEY_ID}    aws_secret_access=${S3.AWS_SECRET_ACCESS_KEY}
+    ...            aws_bucket_name=ods-ci-s3
+    Deploy Kserve Model Via UI    model_name=${PYTORCH_MODEL_NAME}    serving_runtime=triton-kserve-rest
+    ...    data_connection=model-serving-connection    path=triton_resnet/model_repository/    model_framework=tensorflow - 2
+    Wait For Pods To Be Ready    label_selector=serving.kserve.io/inferenceservice=${ONNX_MODEL_LABEL}
+    ...    namespace=${PRJ_TITLE}
+    ${EXPECTED_INFERENCE_REST_OUTPUT_TENSORFLOW}=     Load Json File     file_path=${EXPECTED_INFERENCE_REST_OUTPUT_FILE_TENSORFLOW}
+    ...     as_string=${TRUE}
+    Run Keyword And Continue On Failure    Verify Model Inference With Retries
+    ...    ${PYTORCH_MODEL_NAME}    ${INFERENCE_REST_INPUT_TENSORFLOW}    ${EXPECTED_INFERENCE_REST_OUTPUT_TENSORFLOW}
+    ...    token_auth=${FALSE}    project_title=${PRJ_TITLE}
+    [Teardown]  Run Keywords    Get Kserve Events And Logs      model_name=${PYTORCH_MODEL_NAME}
+    ...  project_title=${PRJ_TITLE}
+    ...  AND
+    ...  Clean All Models Of Current User
+    ...  AND
+    ...  Delete Serving Runtime Template From CLI    displayed_name=triton-kserve-rest
 *** Keywords ***
 Triton On Kserve Suite Setup
     [Documentation]    Suite setup steps for testing Triton. It creates some test variables