cloudbuild.yaml

# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

steps:
  - 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'
    script: |
      terraform init -no-color
      terraform validate -no-color
    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'
    script: |
      terraform init -no-color
      terraform validate -no-color
    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

        # Make sure pods are running
        kubectl wait --all pods -n ml-$SHORT_SHA-$_BUILD_ID-ray --for=condition=Ready --timeout=1200s
        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 5s

        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
  _AUTOPILOT_CLUSTER: "false"
  _BUILD_ID: ${BUILD_ID:0:8}
options:
  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'