diff --git a/cloudbuild.yaml b/cloudbuild.yaml
index 5d1c45bdc..bcace1643 100644
--- a/cloudbuild.yaml
+++ b/cloudbuild.yaml
@@ -13,376 +13,376 @@
 # limitations under the License.
 
 steps:
-  - id: 'validate platform'
-    name: 'gcr.io/$PROJECT_ID/terraform'
+  - id: "validate platform"
+    name: "gcr.io/$PROJECT_ID/terraform"
     script: |
       terraform init -no-color
       terraform validate -no-color
-    dir: 'infrastructure/'
-    waitFor: ['-']
-  
-  - id: 'validate ray'
-    name: 'gcr.io/$PROJECT_ID/terraform'
+    dir: "infrastructure/"
+    waitFor: ["-"]
+
+  - id: "validate ray"
+    name: "gcr.io/$PROJECT_ID/terraform"
     script: |
       terraform init -no-color
       terraform validate -no-color
-    dir: 'applications/ray/'
-    waitFor: ['validate platform']
-  
-  - id: 'validate jupyterhub'
-    name: 'gcr.io/$PROJECT_ID/terraform'
+    dir: "applications/ray/"
+    waitFor: ["validate platform"]
+
+  - id: "validate jupyterhub"
+    name: "gcr.io/$PROJECT_ID/terraform"
     script: |
       terraform init -no-color
       terraform validate -no-color
-    dir: 'applications/jupyter/'
-    waitFor: ['validate platform']
-  
-  - id: 'validate rag'
-    name: 'gcr.io/$PROJECT_ID/terraform'
+    dir: "applications/jupyter/"
+    waitFor: ["validate platform"]
+
+  - id: "validate rag"
+    name: "gcr.io/$PROJECT_ID/terraform"
     script: |
       terraform init -no-color
       terraform validate -no-color
-    dir: 'applications/rag/'
-    waitFor: ['validate platform']
+    dir: "applications/rag/"
+    waitFor: ["validate platform"]
 
   # Create cluster to test ray, jupyterhub, rag
-  - id: 'create gke cluster'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    env:
-      - "KUBE_LOAD_CONFIG_FILE=false"
-    entrypoint: 'sh'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        terraform apply \
-        -var-file=tfvars_tests/standard-gke-public.platform.tfvars \
-        -var=project_id=$PROJECT_ID \
-        -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
-        -var=subnetwork_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
-        -var=subnetwork_region=$_REGION \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=autopilot_cluster=$_AUTOPILOT_CLUSTER \
-        -var=cluster_location=$_REGION \
-        -var='cpu_pools=[{initial_node_count=2,name="cpu-pool",machine_type="n1-standard-16",autoscaling=true,min_count=1,max_count=3,disk_size_gb=100,disk_type="pd-standard",}]' \
-        -var='gpu_pools=[{initial_node_count=2,name="gpu-pool",machine_type="g2-standard-24",autoscaling=true,min_count=1,max_count=3,disk_size_gb=100,disk_type="pd-balanced",accelerator_count=2,accelerator_type="nvidia-l4",gpu_driver_version="DEFAULT",}]' \
-        -auto-approve -no-color 
-        echo "pass" > /workspace/gke_cluster_result.txt
-    dir: 'infrastructure/'
-    allowFailure: true
-    waitFor: ['validate platform', 'validate ray', 'validate jupyterhub', validate rag]
-
-  - id: 'test ray cluster'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'sh'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        # Get kube config
-        gcloud container clusters get-credentials \
-        ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        --location $_REGION \
-        --project $PROJECT_ID 
-
-        cd /workspace/applications/ray/
-        terraform apply \
-        -var-file=workloads.tfvars \
-        -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=cluster_location=$_REGION \
-        -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-ray \
-        -var=workload_identity_service_account=ray-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=gcs_bucket=gke-aieco-ray-$SHORT_SHA-$_BUILD_ID \
-        -var=enable_gpu=true \
-        -auto-approve -no-color 
-        echo "pass" > /workspace/user_result.txt
-
-        chmod +x /workspace/scripts/ci/wait_for_pods.sh
-        /workspace/scripts/ci/wait_for_pods.sh ml-$SHORT_SHA-$_BUILD_ID-ray 3000
-      
-        kubectl wait --all pods -n ml-$SHORT_SHA-$_BUILD_ID-ray --for=condition=Ready --timeout=1200s
-        # Ray head's readinessProbe is not probing the head service today. Therefore the wait for ready above is not reliable.
-        sleep 60s
-        kubectl port-forward -n ml-$SHORT_SHA-$_BUILD_ID-ray service/ray-cluster-kuberay-head-svc 8265:8265 &
-        # Wait port-forwarding to take its place
-        sleep 10s
-
-        ray job submit \
-        --address=http://127.0.0.1:8265 -- python -c "import ray; ray.init(); print(ray.cluster_resources())"
-        echo "pass" > /workspace/ray_result.txt
-    allowFailure: true
-    waitFor: ['create gke cluster']
-
-  - id: 'cleanup ray cluster'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'bash'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        cd /workspace/applications/ray/
-        terraform destroy \
-        -var-file=workloads.tfvars \
-        -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=cluster_location=$_REGION \
-        -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-ray \
-        -var=workload_identity_service_account=ray-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=gcs_bucket=gke-aieco-ray-$SHORT_SHA-$_BUILD_ID \
-        -var=enable_gpu=true \
-        -auto-approve -no-color
-    allowFailure: true
-    waitFor: ['test ray cluster']
-
-  - id: 'test jupyterhub'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'bash'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        cd /workspace/modules/jupyter/tests
-        python3 change_jupyter_config.py $_AUTOPILOT_CLUSTER
-
-        cd /workspace/applications/jupyter
-        terraform apply \
-        -var-file=workloads-without-iap.example.tfvars \
-        -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=cluster_location=$_REGION \
-        -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-jupyter \
-        -var=workload_identity_service_account=jupyter-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=gcs_bucket=gke-aieco-jupyter-$SHORT_SHA-$_BUILD_ID \
-        -auto-approve -no-color 
-        echo "pass" > /workspace/jupyterhub_tf_result.txt
-
-        kubectl wait --for=condition=Ready pods -n ml-$SHORT_SHA-$_BUILD_ID-jupyter -l 'component!=continuous-image-puller' --timeout=1800s
-        kubectl get services -n ml-$SHORT_SHA-$_BUILD_ID-jupyter
-        kubectl port-forward -n ml-$SHORT_SHA-$_BUILD_ID-jupyter service/proxy-public 9442:80 &
-        # Wait port-forwarding to take its place
-        sleep 5s
-
-        cd /workspace/modules/jupyter/tests
-        python3 test_hub.py "127.0.0.1:9442" $_AUTOPILOT_CLUSTER
-        echo "pass" > /workspace/jupyterhub_test_result.txt
-    allowFailure: true
-    waitFor: ['create gke cluster']
-
-  - id: 'cleanup jupyterhub'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'bash'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        cd /workspace/applications/jupyter/
-        terraform destroy \
-        -var-file=workloads-without-iap.example.tfvars \
-        -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=cluster_location=$_REGION \
-        -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-jupyter \
-        -var=workload_identity_service_account=jupyter-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=gcs_bucket=gke-aieco-jupyter-$SHORT_SHA-$_BUILD_ID \
-        -auto-approve -no-color
-    allowFailure: true
-    waitFor: ['test jupyterhub']
-
-  - id: 'test rag'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'sh'
-    secretEnv: ['KAGGLE_USERNAME', 'KAGGLE_KEY']
-    args:
-      - '-c'
-      - |
-        set -e
-
-        # Get kube config
-        gcloud container clusters get-credentials \
-        ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        --location $_REGION \
-        --project $PROJECT_ID
-
-        cd /workspace/modules/jupyter/tests
-        python3 change_jupyter_config.py $_AUTOPILOT_CLUSTER
-
-        cd /workspace/applications/rag/
-        terraform apply \
-        -var-file=workloads.tfvars \
-        -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
-        -var=create_cluster=false \
-        -var=jupyter_add_auth=false \
-        -var=frontend_add_auth=false \
-        -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=cluster_location=$_REGION \
-        -var=kubernetes_namespace=rag-$SHORT_SHA-$_BUILD_ID \
-        -var=gcs_bucket=gke-aieco-rag-$SHORT_SHA-$_BUILD_ID \
-        -var=ray_service_account=ray-sa-4-rag-$SHORT_SHA-$_BUILD_ID \
-        -var=rag_service_account=rag-sa-4-rag-$SHORT_SHA-$_BUILD_ID \
-        -var=jupyter_service_account=jupyter-sa-4-rag-$SHORT_SHA-$_BUILD_ID \
-        -var=cloudsql_instance=pgvector-instance-$SHORT_SHA-$_BUILD_ID \
-        -auto-approve -no-color 
-        echo "pass" > /workspace/rag_tf_result.txt
-
-        # Validate Ray: Make sure pods are running
-        kubectl wait --for=condition=Ready pods -n rag-$SHORT_SHA-$_BUILD_ID -l 'component!=continuous-image-puller' --timeout=1200s
-        kubectl port-forward -n rag-$SHORT_SHA-$_BUILD_ID service/ray-cluster-kuberay-head-svc 8262:8265 &
-        # Wait port-forwarding to take its place
-        sleep 5s
-
-        # Validate Ray: Check dashboard
-        ray job submit --working-dir ./tests \
-        --address=http://127.0.0.1:8262 -- python -c "import ray; ray.init(); print(ray.cluster_resources())"
-        echo "pass" > /workspace/rag_ray_dashboard_result.txt
-
-        # Validate JupyterHub: Get hub url
-        kubectl get services -n rag-$SHORT_SHA-$_BUILD_ID
-        kubectl port-forward -n rag-$SHORT_SHA-$_BUILD_ID service/proxy-public 9443:80 &
-        # Wait port-forwarding to take its place
-        sleep 5s
-
-        # Validate JupyterHub: Test Hub
-        cd /workspace/modules/jupyter/tests
-        python3 test_hub.py "127.0.0.1:9443" $_AUTOPILOT_CLUSTER
-        echo "pass" > /workspace/rag_jupyterhub_test_result.txt
-
-        # Validate RAG: Test rag frontend
-        kubectl port-forward -n rag-$SHORT_SHA-$_BUILD_ID service/rag-frontend 8081:8080 &
-        # Wait port-forwarding to take its place
-        sleep 5s
-
-        cd /workspace/applications/rag/tests
-        python3 test_frontend.py "127.0.0.1:8081"
-        echo "pass" > /workspace/rag_frontend_result.txt
-
-        cd /workspace/
-        sed -i "s/<username>/$$KAGGLE_USERNAME/g" ./applications/rag/example_notebooks/rag-kaggle-ray-sql-interactive.ipynb
-        sed -i "s/<token>/$$KAGGLE_KEY/g" ./applications/rag/example_notebooks/rag-kaggle-ray-sql-interactive.ipynb
-        gsutil cp ./applications/rag/example_notebooks/rag-kaggle-ray-sql-interactive.ipynb gs://gke-aieco-rag-$SHORT_SHA-$_BUILD_ID/
-        kubectl exec -it -n rag-$SHORT_SHA-$_BUILD_ID $(kubectl get pod -l app=jupyterhub,component=hub -n rag-$SHORT_SHA-$_BUILD_ID -o jsonpath="{.items[0].metadata.name}") -- jupyterhub token admin --log-level=CRITICAL | xargs python3 ./applications/rag/notebook_starter.py
-        # Wait for jupyterhub to trigger notebook pod startup
-        sleep 5s
-        kubectl wait --for=condition=Ready pod/jupyter-admin -n rag-$SHORT_SHA-$_BUILD_ID --timeout=500s
-        kubectl exec -it -n rag-$SHORT_SHA-$_BUILD_ID jupyter-admin -c notebook -- jupyter nbconvert --to script /data/rag-kaggle-ray-sql-interactive.ipynb
-        kubectl exec -it -n rag-$SHORT_SHA-$_BUILD_ID jupyter-admin -c notebook -- ipython /data/rag-kaggle-ray-sql-interactive.py
-
-        python3 ./applications/rag/tests/test_rag.py "http://127.0.0.1:8081/prompt"
-        echo "pass" > /workspace/rag_prompt_result.txt
-
-    allowFailure: true
-    waitFor: ['create gke cluster']
-
-  - id: 'cleanup rag'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'bash'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        cd /workspace/applications/rag/
-        terraform destroy \
-        -var-file=workloads.tfvars \
-        -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
-        -var=create_cluster=false \
-        -var=jupyter_add_auth=false \
-        -var=frontend_add_auth=false \
-        -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=cluster_location=$_REGION \
-        -var=kubernetes_namespace=rag-$SHORT_SHA-$_BUILD_ID \
-        -var=gcs_bucket=gke-aieco-rag-$SHORT_SHA-$_BUILD_ID \
-        -var=ray_service_account=ray-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=rag_service_account=rag-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=jupyter_service_account=jupyter-sa-$SHORT_SHA-$_BUILD_ID \
-        -var=cloudsql_instance=pgvector-instance-$SHORT_SHA-$_BUILD_ID \
-        -auto-approve -no-color
-    allowFailure: true
-    waitFor: ['test rag']
-
-  - id: 'cleanup gke cluster'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'bash'
-    args:
-      - '-c'
-      - |
-        set -e
-
-        cd /workspace/infrastructure
-        terraform destroy -var-file=tfvars_tests/standard-gke-public.platform.tfvars -var=project_id=$PROJECT_ID \
-        -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
-        -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
-        -var=subnetwork_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
-        -var=autopilot_cluster=$_AUTOPILOT_CLUSTER \
-        -var=cluster_location=$_REGION -auto-approve -no-color
-
-    allowFailure: true
-    waitFor: ['cleanup rag', 'cleanup jupyterhub', 'cleanup ray cluster']
-
-  - id: 'check result'
-    name: 'gcr.io/$PROJECT_ID/terraform'
-    entrypoint: 'bash'
-    args:
-      - '-c'
-      - |
-        if [[ $(cat /workspace/gke_cluster_result.txt) != "pass" ]]; then
-          echo "gke cluster creation failed"
-          exit 1
-        fi
-        
-        if [[ $(cat /workspace/ray_result.txt) != "pass" ]]; then
-          echo "ray API run failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/user_result.txt) != "pass" ]]; then
-          echo "ray cluster failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/jupyterhub_tf_result.txt) != "pass" ]]; then
-          echo "jupyterhub tf failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/jupyterhub_test_result.txt) != "pass" ]]; then
-          echo "jupyterhub test failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/rag_tf_result.txt) != "pass" ]]; then
-          echo "rag tf failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/rag_ray_dashboard_result.txt) != "pass" ]]; then
-          echo "rag ray dashboard test failed"
-          exit 1
-        fi
-        
-        if [[ $(cat /workspace/rag_jupyterhub_test_result.txt) != "pass" ]]; then
-          echo "rag jupyterhub test failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/rag_frontend_result.txt) != "pass" ]]; then
-          echo "rag frontend test failed"
-          exit 1
-        fi
-
-        if [[ $(cat /workspace/rag_prompt_result.txt) != "pass" ]]; then
-          echo "rag prompt test failed"
-          exit 1
-        fi
-    waitFor: ['cleanup gke cluster']
-        
+  # - id: 'create gke cluster'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   env:
+  #     - "KUBE_LOAD_CONFIG_FILE=false"
+  #   entrypoint: 'sh'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       terraform apply \
+  #       -var-file=tfvars_tests/standard-gke-public.platform.tfvars \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
+  #       -var=subnetwork_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
+  #       -var=subnetwork_region=$_REGION \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=autopilot_cluster=$_AUTOPILOT_CLUSTER \
+  #       -var=cluster_location=$_REGION \
+  #       -var='cpu_pools=[{initial_node_count=2,name="cpu-pool",machine_type="n1-standard-16",autoscaling=true,min_count=1,max_count=3,disk_size_gb=100,disk_type="pd-standard",}]' \
+  #       -var='gpu_pools=[{initial_node_count=2,name="gpu-pool",machine_type="g2-standard-24",autoscaling=true,min_count=1,max_count=3,disk_size_gb=100,disk_type="pd-balanced",accelerator_count=2,accelerator_type="nvidia-l4",gpu_driver_version="DEFAULT",}]' \
+  #       -auto-approve -no-color
+  #       echo "pass" > /workspace/gke_cluster_result.txt
+  #   dir: 'infrastructure/'
+  #   allowFailure: true
+  #   waitFor: ['validate platform', 'validate ray', 'validate jupyterhub', validate rag]
+
+  # - id: 'test ray cluster'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'sh'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       # Get kube config
+  #       gcloud container clusters get-credentials \
+  #       ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       --location $_REGION \
+  #       --project $PROJECT_ID
+
+  #       cd /workspace/applications/ray/
+  #       terraform apply \
+  #       -var-file=workloads.tfvars \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=cluster_location=$_REGION \
+  #       -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-ray \
+  #       -var=workload_identity_service_account=ray-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=gcs_bucket=gke-aieco-ray-$SHORT_SHA-$_BUILD_ID \
+  #       -var=enable_gpu=true \
+  #       -auto-approve -no-color
+  #       echo "pass" > /workspace/user_result.txt
+
+  #       chmod +x /workspace/scripts/ci/wait_for_pods.sh
+  #       /workspace/scripts/ci/wait_for_pods.sh ml-$SHORT_SHA-$_BUILD_ID-ray 3000
+
+  #       kubectl wait --all pods -n ml-$SHORT_SHA-$_BUILD_ID-ray --for=condition=Ready --timeout=1200s
+  #       # Ray head's readinessProbe is not probing the head service today. Therefore the wait for ready above is not reliable.
+  #       sleep 60s
+  #       kubectl port-forward -n ml-$SHORT_SHA-$_BUILD_ID-ray service/ray-cluster-kuberay-head-svc 8265:8265 &
+  #       # Wait port-forwarding to take its place
+  #       sleep 10s
+
+  #       ray job submit \
+  #       --address=http://127.0.0.1:8265 -- python -c "import ray; ray.init(); print(ray.cluster_resources())"
+  #       echo "pass" > /workspace/ray_result.txt
+  #   allowFailure: true
+  #   waitFor: ['create gke cluster']
+
+  # - id: 'cleanup ray cluster'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'bash'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       cd /workspace/applications/ray/
+  #       terraform destroy \
+  #       -var-file=workloads.tfvars \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=cluster_location=$_REGION \
+  #       -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-ray \
+  #       -var=workload_identity_service_account=ray-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=gcs_bucket=gke-aieco-ray-$SHORT_SHA-$_BUILD_ID \
+  #       -var=enable_gpu=true \
+  #       -auto-approve -no-color
+  #   allowFailure: true
+  #   waitFor: ['test ray cluster']
+
+  # - id: 'test jupyterhub'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'bash'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       cd /workspace/modules/jupyter/tests
+  #       python3 change_jupyter_config.py $_AUTOPILOT_CLUSTER
+
+  #       cd /workspace/applications/jupyter
+  #       terraform apply \
+  #       -var-file=workloads-without-iap.example.tfvars \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=cluster_location=$_REGION \
+  #       -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-jupyter \
+  #       -var=workload_identity_service_account=jupyter-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=gcs_bucket=gke-aieco-jupyter-$SHORT_SHA-$_BUILD_ID \
+  #       -auto-approve -no-color
+  #       echo "pass" > /workspace/jupyterhub_tf_result.txt
+
+  #       kubectl wait --for=condition=Ready pods -n ml-$SHORT_SHA-$_BUILD_ID-jupyter -l 'component!=continuous-image-puller' --timeout=1800s
+  #       kubectl get services -n ml-$SHORT_SHA-$_BUILD_ID-jupyter
+  #       kubectl port-forward -n ml-$SHORT_SHA-$_BUILD_ID-jupyter service/proxy-public 9442:80 &
+  #       # Wait port-forwarding to take its place
+  #       sleep 5s
+
+  #       cd /workspace/modules/jupyter/tests
+  #       python3 test_hub.py "127.0.0.1:9442" $_AUTOPILOT_CLUSTER
+  #       echo "pass" > /workspace/jupyterhub_test_result.txt
+  #   allowFailure: true
+  #   waitFor: ['create gke cluster']
+
+  # - id: 'cleanup jupyterhub'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'bash'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       cd /workspace/applications/jupyter/
+  #       terraform destroy \
+  #       -var-file=workloads-without-iap.example.tfvars \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=cluster_location=$_REGION \
+  #       -var=kubernetes_namespace=ml-$SHORT_SHA-$_BUILD_ID-jupyter \
+  #       -var=workload_identity_service_account=jupyter-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=gcs_bucket=gke-aieco-jupyter-$SHORT_SHA-$_BUILD_ID \
+  #       -auto-approve -no-color
+  #   allowFailure: true
+  #   waitFor: ['test jupyterhub']
+
+  # - id: 'test rag'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'sh'
+  #   secretEnv: ['KAGGLE_USERNAME', 'KAGGLE_KEY']
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       # Get kube config
+  #       gcloud container clusters get-credentials \
+  #       ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       --location $_REGION \
+  #       --project $PROJECT_ID
+
+  #       cd /workspace/modules/jupyter/tests
+  #       python3 change_jupyter_config.py $_AUTOPILOT_CLUSTER
+
+  #       cd /workspace/applications/rag/
+  #       terraform apply \
+  #       -var-file=workloads.tfvars \
+  #       -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
+  #       -var=create_cluster=false \
+  #       -var=jupyter_add_auth=false \
+  #       -var=frontend_add_auth=false \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=cluster_location=$_REGION \
+  #       -var=kubernetes_namespace=rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=gcs_bucket=gke-aieco-rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=ray_service_account=ray-sa-4-rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=rag_service_account=rag-sa-4-rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=jupyter_service_account=jupyter-sa-4-rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=cloudsql_instance=pgvector-instance-$SHORT_SHA-$_BUILD_ID \
+  #       -auto-approve -no-color
+  #       echo "pass" > /workspace/rag_tf_result.txt
+
+  #       # Validate Ray: Make sure pods are running
+  #       kubectl wait --for=condition=Ready pods -n rag-$SHORT_SHA-$_BUILD_ID -l 'component!=continuous-image-puller' --timeout=1200s
+  #       kubectl port-forward -n rag-$SHORT_SHA-$_BUILD_ID service/ray-cluster-kuberay-head-svc 8262:8265 &
+  #       # Wait port-forwarding to take its place
+  #       sleep 5s
+
+  #       # Validate Ray: Check dashboard
+  #       ray job submit --working-dir ./tests \
+  #       --address=http://127.0.0.1:8262 -- python -c "import ray; ray.init(); print(ray.cluster_resources())"
+  #       echo "pass" > /workspace/rag_ray_dashboard_result.txt
+
+  #       # Validate JupyterHub: Get hub url
+  #       kubectl get services -n rag-$SHORT_SHA-$_BUILD_ID
+  #       kubectl port-forward -n rag-$SHORT_SHA-$_BUILD_ID service/proxy-public 9443:80 &
+  #       # Wait port-forwarding to take its place
+  #       sleep 5s
+
+  #       # Validate JupyterHub: Test Hub
+  #       cd /workspace/modules/jupyter/tests
+  #       python3 test_hub.py "127.0.0.1:9443" $_AUTOPILOT_CLUSTER
+  #       echo "pass" > /workspace/rag_jupyterhub_test_result.txt
+
+  #       # Validate RAG: Test rag frontend
+  #       kubectl port-forward -n rag-$SHORT_SHA-$_BUILD_ID service/rag-frontend 8081:8080 &
+  #       # Wait port-forwarding to take its place
+  #       sleep 5s
+
+  #       cd /workspace/applications/rag/tests
+  #       python3 test_frontend.py "127.0.0.1:8081"
+  #       echo "pass" > /workspace/rag_frontend_result.txt
+
+  #       cd /workspace/
+  #       sed -i "s/<username>/$$KAGGLE_USERNAME/g" ./applications/rag/example_notebooks/rag-kaggle-ray-sql-interactive.ipynb
+  #       sed -i "s/<token>/$$KAGGLE_KEY/g" ./applications/rag/example_notebooks/rag-kaggle-ray-sql-interactive.ipynb
+  #       gsutil cp ./applications/rag/example_notebooks/rag-kaggle-ray-sql-interactive.ipynb gs://gke-aieco-rag-$SHORT_SHA-$_BUILD_ID/
+  #       kubectl exec -it -n rag-$SHORT_SHA-$_BUILD_ID $(kubectl get pod -l app=jupyterhub,component=hub -n rag-$SHORT_SHA-$_BUILD_ID -o jsonpath="{.items[0].metadata.name}") -- jupyterhub token admin --log-level=CRITICAL | xargs python3 ./applications/rag/notebook_starter.py
+  #       # Wait for jupyterhub to trigger notebook pod startup
+  #       sleep 5s
+  #       kubectl wait --for=condition=Ready pod/jupyter-admin -n rag-$SHORT_SHA-$_BUILD_ID --timeout=500s
+  #       kubectl exec -it -n rag-$SHORT_SHA-$_BUILD_ID jupyter-admin -c notebook -- jupyter nbconvert --to script /data/rag-kaggle-ray-sql-interactive.ipynb
+  #       kubectl exec -it -n rag-$SHORT_SHA-$_BUILD_ID jupyter-admin -c notebook -- ipython /data/rag-kaggle-ray-sql-interactive.py
+
+  #       python3 ./applications/rag/tests/test_rag.py "http://127.0.0.1:8081/prompt"
+  #       echo "pass" > /workspace/rag_prompt_result.txt
+
+  #   allowFailure: true
+  #   waitFor: ['create gke cluster']
+
+  # - id: 'cleanup rag'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'bash'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       cd /workspace/applications/rag/
+  #       terraform destroy \
+  #       -var-file=workloads.tfvars \
+  #       -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
+  #       -var=create_cluster=false \
+  #       -var=jupyter_add_auth=false \
+  #       -var=frontend_add_auth=false \
+  #       -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=cluster_location=$_REGION \
+  #       -var=kubernetes_namespace=rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=gcs_bucket=gke-aieco-rag-$SHORT_SHA-$_BUILD_ID \
+  #       -var=ray_service_account=ray-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=rag_service_account=rag-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=jupyter_service_account=jupyter-sa-$SHORT_SHA-$_BUILD_ID \
+  #       -var=cloudsql_instance=pgvector-instance-$SHORT_SHA-$_BUILD_ID \
+  #       -auto-approve -no-color
+  #   allowFailure: true
+  #   waitFor: ['test rag']
+
+  # - id: 'cleanup gke cluster'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'bash'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       set -e
+
+  #       cd /workspace/infrastructure
+  #       terraform destroy -var-file=tfvars_tests/standard-gke-public.platform.tfvars -var=project_id=$PROJECT_ID \
+  #       -var=cluster_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-cluster \
+  #       -var=network_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
+  #       -var=subnetwork_name=ml-$SHORT_SHA-$_PR_NUMBER-$_BUILD_ID-$_AUTOPILOT_CLUSTER  \
+  #       -var=autopilot_cluster=$_AUTOPILOT_CLUSTER \
+  #       -var=cluster_location=$_REGION -auto-approve -no-color
+
+  #   allowFailure: true
+  #   waitFor: ['cleanup rag', 'cleanup jupyterhub', 'cleanup ray cluster']
+
+  # - id: 'check result'
+  #   name: 'gcr.io/$PROJECT_ID/terraform'
+  #   entrypoint: 'bash'
+  #   args:
+  #     - '-c'
+  #     - |
+  #       if [[ $(cat /workspace/gke_cluster_result.txt) != "pass" ]]; then
+  #         echo "gke cluster creation failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/ray_result.txt) != "pass" ]]; then
+  #         echo "ray API run failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/user_result.txt) != "pass" ]]; then
+  #         echo "ray cluster failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/jupyterhub_tf_result.txt) != "pass" ]]; then
+  #         echo "jupyterhub tf failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/jupyterhub_test_result.txt) != "pass" ]]; then
+  #         echo "jupyterhub test failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/rag_tf_result.txt) != "pass" ]]; then
+  #         echo "rag tf failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/rag_ray_dashboard_result.txt) != "pass" ]]; then
+  #         echo "rag ray dashboard test failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/rag_jupyterhub_test_result.txt) != "pass" ]]; then
+  #         echo "rag jupyterhub test failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/rag_frontend_result.txt) != "pass" ]]; then
+  #         echo "rag frontend test failed"
+  #         exit 1
+  #       fi
+
+  #       if [[ $(cat /workspace/rag_prompt_result.txt) != "pass" ]]; then
+  #         echo "rag prompt test failed"
+  #         exit 1
+  #       fi
+  #   waitFor: ['cleanup gke cluster']
+
 substitutions:
   _REGION: us-east4
   _USER_NAME: github
@@ -390,13 +390,13 @@ substitutions:
   _BUILD_ID: ${BUILD_ID:0:8}
 logsBucket: gs://ai-on-gke-build-logs
 options:
-  substitutionOption: 'ALLOW_LOOSE'
-  machineType: 'E2_HIGHCPU_8'
+  substitutionOption: "ALLOW_LOOSE"
+  machineType: "E2_HIGHCPU_8"
 timeout: 5400s
 
 availableSecrets:
   secretManager:
-  - versionName: projects/gke-ai-eco-dev/secrets/cloudbuild-kaggle-username/versions/latest
-    env: 'KAGGLE_USERNAME'
-  - versionName: projects/gke-ai-eco-dev/secrets/cloudbuild-kaggle-key/versions/latest
-    env: 'KAGGLE_KEY'
+    - versionName: projects/gke-ai-eco-dev/secrets/cloudbuild-kaggle-username/versions/latest
+      env: "KAGGLE_USERNAME"
+    - versionName: projects/gke-ai-eco-dev/secrets/cloudbuild-kaggle-key/versions/latest
+      env: "KAGGLE_KEY"