How do I Deploy Riva at Scale on Google Cloud with GKE?#

This is an example of deploying and scaling Riva Speech Skills on Google Cloud (GCP) Google Kuberenetes Engine (GKE) with Traefik-based load balancing. It includes the following steps:

  1. Creating the GKE cluster

  2. Deploying the Riva API service

  3. Deploying the Traefik edge router

  4. Creating the IngressRoute to handle incoming requests

  5. Deploying a sample client

  6. Scaling the cluster


Before continuing, ensure you have:

  • An Google account with the appropriate user/role privileges to manage GKE

  • The gcloud command-line tool, Configured for your account

  • Access to NGC and the associated command-line interface

  • Cluster management tools gcloud, helm and kubectl

Creating the GKE Cluster#

The cluster contains three separate node pools:

  • gpu-linux-workers: A GPU-equipped node where the main Riva service runs. n1-standard-16 instances, each using a Tesla T4 GPU, which provides good value and sufficient capacity for many applications.

  • cpu-linux-lb: A general-purpose compute node for the Traefik load balancer, using an n1-standard-4 instance.

  • cpu-linux-clients: A general-purpose node with an n1-standard-8 instance for client applications accessing the Riva service.

  1. Create the GKE cluster. This will take some time as it will spin nodes and setup kubernetes control plane in backend.

    gcloud container clusters create riva-gke --machine-type n1-standard-2 --num-nodes 1 --zone us-central1-c
  2. After the cluster creation is complete, install the plugin for kubectl for gcloud:

    gcloud components install kubectl
  3. Verify if you are able to connect to the cluster using kubectl. You should see nodes and pods running.

    kubectl get nodes
    kubectl get po -A
  4. Create the three node pools for GPU workers, load balancers, and clients:


      gcloud container node-pools create gpu-linux-workers --cluster=riva-gke --node-labels=role=workers --machine-type=n1-standard-16 --accelerator=count=1,type=nvidia-tesla-t4 --num-nodes=1 --disk-size=100 --zone us-central1-c

      gcloud container node-pools create cpu-linux-lb --cluster=riva-gke --node-labels=role=loadbalancers --machine-type=n1-standard-4  --num-nodes=1 --disk-size=100 --zone us-central1-c

      gcloud container node-pools create cpu-linux-clients --cluster=riva-gke --node-labels=role=clients --machine-type=n1-standard-8  --num-nodes=1 --disk-size=100 --zone us-central1-c
  5. Verify that the newly added nodes now appear in the Kubernetes cluster.

    kubectl get nodes --show-labels
    kubectl get nodes --selector role=workers
    kubectl get nodes --selector role=clients
    kubectl get nodes --selector role=loadbalancers

Deploying the Riva API#

The Riva Speech Skills Helm chart is designed to automate deployment to a Kubernetes cluster. After downloading the Helm chart, minor adjustments will adapt the chart to the way Riva will be used in the remainder of this tutorial.

  1. Download and untar the Riva API Helm chart. Replace VERSION_TAG with the specific version needed.

    export NGC_CLI_API_KEY=<your NGC API key>
    export VERSION_TAG="2.16.0"
    helm fetch${VERSION_TAG}.tgz --username='$oauthtoken' --password=$NGC_CLI_API_KEY
    tar -xvzf riva-api-${VERSION_TAG}.tgz
  2. In the riva-api folder, modify the following files:

    1. values.yaml

      • In modelRepoGenerator.ngcModelConfigs, comment or uncomment specific models or languages, as needed.

      • Change service.type from LoadBalancer to ClusterIP. This directly exposes the service only to other services within the cluster, such as the proxy service to be installed below.

      • Set persistentVolumeClaim.usePVC to true , persistentVolumeClaim.storageClassName to standard , and persistentVolumeClaim.storageAccessMode to ReadWriteOnce. This will store the Riva models in Created Persistent Volume.

    2. templates/deployment.yaml

      • Add a node selector constraint to ensure that Riva is only deployed on the correct GPU resources. In spec.template.spec, add:

  3. If you see that the GPU driver plugin is not enabled by GCP, then deploy it using the following command:

    kubectl apply -f
  4. Verify the GPU plugin installation with either of the following commands:

    kubectl get pod -A | grep nvidia
    kubectl get nodes ",GPU:.status.allocatable.nvidia\.com/gpu"
  5. Ensure you are in a working directory with riva-api as a subdirectory, then install the Riva Helm chart. You can explicitly override variables from the values.yaml file, such as the modelRepoGenerator.modelDeployKey settings.

    helm install riva-api riva-api/ \
        --set ngcCredentials.password=`echo -n $NGC_CLI_API_KEY | base64 -w0` \
        --set modelRepoGenerator.modelDeployKey=`echo -n tlt_encode | base64 -w0`
  6. The Helm chart runs two containers in order: a riva-model-init container that downloads and deploys the models, followed by a riva-speech-api container to start the speech service API. Depending on the number of models, the initial model deployment could take an hour or more. To monitor the deployment, use kubectl to describe the riva-api pod and watch the container logs.

    export pod=`kubectl get pods | cut -d " " -f 1 | grep riva-api`
    kubectl describe pod $pod
    kubectl logs -f $pod -c riva-model-init
    kubectl logs -f $pod -c riva-speech-api

Deploying the Traefik Edge Router#

Now that the Riva service is running, the cluster needs a mechanism to route requests into Riva.

In the default values.yaml of the riva-api Helm chart, service.type was set to LoadBalancer, which would have automatically created a Google Load Balancer to direct traffic into the Riva service. Instead, the open-source Traefik edge router will serve this purpose.

  1. Download and untar the Traefik Helm chart.

    helm repo add traefik
    helm repo update
    helm fetch traefik/traefik
    tar -zxvf traefik-*.tgz
  2. Modify the traefik/values.yaml file.

    1. Change service.type from LoadBalancer to ClusterIP. This exposes the service on a cluster-internal IP.

    2. Set nodeSelector to { cpu-linux-lb }. Similar to what you did for the Riva API service, this tells the Traefik service to run on the cpu-linux-lb node pool.

  3. Deploy the modified traefik Helm chart.

    helm install traefik traefik/

Creating the IngressRoute#

An IngressRoute enables the Traefik load balancer to recognize incoming requests and distribute them across multiple riva-api services.

When you deployed the traefik Helm chart above, Kubernetes automatically created a local DNS entry for that service: traefik.default.svc.cluster.local. The IngressRoute definition below matches these DNS entries and directs requests to the riva-api service. You can modify the entries to support a different DNS arrangement, depending on your requirements.

  1. Create the following riva-ingress.yaml file:

    kind: IngressRoute
      name: riva-ingressroute
        - web
        - match: "Host(`traefik.default.svc.cluster.local`)"
          kind: Rule
            - name: riva-api
              port: 50051
              scheme: h2c
  2. Deploy the IngressRoute.

    kubectl apply -f riva-ingress.yaml

The Riva service is now able to serve gRPC requests from within the cluster at the address traefik.default.svc.cluster.local. If you are planning to deploy your own client application in the cluster to communicate with Riva, you can send requests to that address. In the next section, you will deploy a Riva sample client and use it to test the deployment.

Deploying a Sample Client#

Riva provides a container with a set of pre-built sample clients to test the Riva services. The clients are also available on GitHub for those interested in adapting them.

  1. Create the client-deployment.yaml file that defines the deployment and contains the following:

    apiVersion: apps/v1
    kind: Deployment
      name: riva-client
        app: "rivaasrclient"
      replicas: 1
          app: "rivaasrclient"
            app: "rivaasrclient"
          - name: imagepullsecret
          - name: riva-client
            image: "{NgcOrg}/{NgcTeam}/riva-speech:2.16.0"
            command: ["/bin/bash"]
            args: ["-c", "while true; do sleep 5; done"]
  2. Deploy the client service.

    kubectl apply -f client-deployment.yaml
  3. Connect to the client pod.

    export cpod=`kubectl get pods | cut -d " " -f 1 | grep riva-client`
    kubectl exec --stdin --tty $cpod /bin/bash
  4. From inside the shell of the client pod, run the sample ASR client on an example .wav file. Specify the traefik.default.svc.cluster.local endpoint, with port 80, as the service address.

    riva_streaming_asr_client \
       --audio_file=wav/en-US_sample.wav \
       --automatic_punctuation=true \

Scaling the Cluster#

As deployed above, the GKE cluster only provisions a single GPU node, although we can scale the nodes. While a single GPU can handle a large volume of requests, the cluster can easily be scaled with more nodes.

  1. Scale the GPU node pool to the desired number of compute nodes (2 in this case).

    gcloud container clusters resize riva-gke --node-pool gpu-linux-workers --num-nodes 2 --zone us-central1-c
  2. Scale the riva-api deployment to use the additional nodes.

    kubectl scale deployments/riva-api --replicas=2

As with the original riva-api deployment, each replica pod downloads and initializes the necessary models prior to starting the Riva service.