refactor: rename tensorflow-example to tfserving-example, since we are actually deploying in kserve using the tf-serving runtime

Author: gabrielmscampos

deployment/kserve/scripts/deploy_tensorflow_model.bash

@@ -17,24 +17,24 @@ minio_api_node_port=$(kubectl get svc minio-service --namespace minio -o jsonpat
 minio_external_api_url="http://$minikube_ip:$minio_api_node_port"
 
 # Deploy model and make it accessible
-service_name="tensorflow-example"
+service_name="tfserving-example"
 host_model_path="../../../models/tensorflow/1"
-s3_model_dir="s3://test-bucket/tensorflow-example/1"
-storage_uri="\"s3://test-bucket/tensorflow-example\""
+s3_model_dir="s3://test-bucket/tfserving-example/1"
+storage_uri="\"s3://test-bucket/tfserving-example\""
 aws s3 cp --recursive "$host_model_path" $s3_model_dir --profile $minio_aws_profile_name --endpoint-url=$minio_external_api_url &> /dev/null
 sed -e "s/{{ inference_service_resource_name }}/$service_name/g" \
     -e "s/{{ service_account_resource_name }}/$service_account_resource_name/g" \
     -e "s|{{ s3_model_root_path }}|$storage_uri|g" \
-    $templates_path/tensorflow.yaml \
-    > $tmp_kubeconfigs_path/tensorflow-isvc.yaml
-deploy_service "default" "$tmp_kubeconfigs_path/tensorflow-isvc.yaml" "$service_name"
+    $templates_path/tfserving.yaml \
+    > $tmp_kubeconfigs_path/tfserving-isvc.yaml
+deploy_service "default" "$tmp_kubeconfigs_path/tfserving-isvc.yaml" "$service_name"
 wait_for_inference_service 300 5 "$service_name" "default"
 
 # Test predictions
 istio_node_port=$(kubectl get svc istio-ingressgateway --namespace istio-system -o jsonpath='{.spec.ports[?(@.name=="http2")].nodePort}')
 istio_base_url="http://$minikube_ip:$istio_node_port"
-model_name="tensorflow-example"
-service_name="tensorflow-example"
+model_name="tfserving-example"
+service_name="tfserving-example"
 namespace="default"
 url="${istio_base_url}/v1/models/${model_name}:predict"
 service_hostname=$(kubectl get inferenceservice ${service_name} --namespace "$namespace" -o jsonpath='{.status.url}' | cut -d "/" -f 3)

deployment/kserve/templates/tensorflow.yaml renamed to deployment/kserve/templates/tfserving.yaml

@@ -10,4 +10,5 @@ spec:
     model:
       modelFormat:
         name: tensorflow
+      runtime: kserve-tensorflow-serving
       storageUri: {{ s3_model_root_path }}

deployment/tensorflow/README.md

@@ -34,6 +34,6 @@ If using `minikube` to deploy the lightgbm model using MLServer behind KServe, y
 ```bash
 python test_predictions.py \
   -r 360950 \
-  -u "http://$(minikube ip):$(kubectl get svc istio-ingressgateway --namespace istio-system -o jsonpath='{.spec.ports[?(@.name=="http2")].nodePort}')/v1/models/tensorflow-example:predict" \
-  -H "Host=$(kubectl get inferenceservice tensorflow-example --namespace default -o jsonpath='{.status.url}' | cut -d "/" -f 3)"
+  -u "http://$(minikube ip):$(kubectl get svc istio-ingressgateway --namespace istio-system -o jsonpath='{.spec.ports[?(@.name=="http2")].nodePort}')/v1/models/tfserving-example:predict" \
+  -H "Host=$(kubectl get inferenceservice tfserving-example --namespace default -o jsonpath='{.status.url}' | cut -d "/" -f 3)"
 ```