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RAG: Frontend deployment

Prerequisites

  • This guide was developed to be run on the playground AI/ML platform. If you are using a different environment the scripts and manifest will need to be modified for that environment.

Preparation

  • Clone the repository.

    git clone https://github.com/GoogleCloudPlatform/accelerated-platforms && \
cd accelerated-platforms

  • Change directory to the guide directory.

    cd use-cases/rag-pipeline/frontend

  • Ensure that your MLP_ENVIRONMENT_FILE is configured.

    cat ${MLP_ENVIRONMENT_FILE} && \
set -o allexport && \
source ${MLP_ENVIRONMENT_FILE} && \
set +o allexport

    You should see the various variables populated with the information specific to your environment.

  • Get credentials for the GKE cluster.

    gcloud container clusters get-credentials ${MLP_CLUSTER_NAME} \
--dns-endpoint \
--project=${MLP_PROJECT_ID} \
--region=${MLP_REGION}

Build the container image

  • Build the container image using Cloud Build and push the image to Artifact Registry.

    cd src
git restore cloudbuild.yaml
sed -i -e "s|^serviceAccount:.*|serviceAccount: projects/${MLP_PROJECT_ID}/serviceAccounts/${MLP_BUILD_GSA}|" cloudbuild.yaml
gcloud beta builds submit \
--config cloudbuild.yaml \
--gcs-source-staging-dir gs://${MLP_CLOUDBUILD_BUCKET}/source \
--project ${MLP_PROJECT_ID} \
--substitutions _DESTINATION=${MLP_RAG_FRONTEND_IMAGE}
cd -

Deploy the frontend application

  • Configure the deployment.

    set -o nounset
export BACKEND_SERVICE_ENDPOINT="http://rag-backend.ml-team:8000/generate_product_recommendations/"
export CONTAINER_IMAGE_URL="${MLP_RAG_FRONTEND_IMAGE}"
set -o nounset

    Ensure there are no bash: <ENVIRONMENT_VARIABLE> unbound variable error messages.

    git restore manifests/deployment.yaml
envsubst < manifests/deployment.yaml | sponge manifests/deployment.yaml

  • Create the deployment.

    kubectl --namespace ${MLP_KUBERNETES_NAMESPACE} apply -f manifests/deployment.yaml

  • Watch the deployment until it is ready and available.

    watch --color --interval 5 --no-title \
"kubectl --namespace ${MLP_MODEL_OPS_NAMESPACE} get deployment/rag-frontend | GREP_COLORS='mt=01;92' egrep --color=always -e '^' -e '1/1     1            1'
echo '\nLogs(last 10 lines):'
kubectl --namespace ${MLP_KUBERNETES_NAMESPACE} logs deployment/rag-frontend --tail 10"
    NAME           READY   UP-TO-DATE   AVAILABLE   AGE
rag-frontend   1/1     1            1           XXXXX

Test the frontend application

  • Run the following command to output the URL for the frontend application.

    echo -e "\n${MLP_KUBERNETES_NAMESPACE} RAG frontend URL: ${MLP_RAG_FRONTEND_NAMESPACE_ENDPOINT}\n"

  • Open the frontend application in your browser.

    It can take several minutes for the frontend application to be available via the gateway.

    If you are seeing fault filter abort, wait a moment and retry.