Migrate x86 application on GKE to multi-arch with Arm

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This tutorial describes how to migrate an application built for nodes using an x86 (Intel or AMD) processor in a Google Kubernetes Engine (GKE) cluster to a multi-architecture (multi-arch) application that runs on either x86 or Arm nodes. The intended audience for this tutorial is Platform Admins, App Operators, and App Developers who want to run their existing x86-compatible workloads on Arm.

With GKE clusters, you can run workloads on Arm nodes using the Tau T2A Arm machine series. T2A nodes can run in your GKE cluster just like any other node using x86 (Intel or AMD) processors. They are a good choice for scale-out and compute-intensive workloads.

To learn more, see Arm workloads on GKE.

This tutorial assumes that you are familiar with Kubernetes and Docker. The tutorial uses Google Kubernetes Engine and Artifact Registry.

Objectives

In this tutorial, you will complete the following tasks:

  • Store container images with Docker in Artifact Registry.
  • Deploy an x86-compatible workload to a GKE cluster.
  • Rebuild an x86-compatible workload to run on Arm.
  • Add an Arm node pool to an existing cluster.
  • Deploy an Arm-compatible workload to run on an Arm node.
  • Build a multi-arch image to run a workload across multiple architectures.
  • Run workloads across multiple architectures in one GKE cluster.

Costs

This tutorial uses the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish this tutorial, you can avoid continued billing by deleting the resources you created. For more information, see Clean up.

Before you begin

Take the following steps to enable the Kubernetes Engine API:
  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  4. Enable the Artifact Registry and Google Kubernetes Engine APIs.

    Enable the APIs

  5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  6. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  7. Enable the Artifact Registry and Google Kubernetes Engine APIs.

    Enable the APIs

When you finish this tutorial, you can avoid continued billing by deleting the resources you created. See Clean up for more details.

Launch Cloud Shell

In this tutorial you will use Cloud Shell, which is a shell environment for managing resources hosted on Google Cloud.

Cloud Shell comes preinstalled with the Google Cloud CLI and kubectl command-line tool. The gcloud CLI provides the primary command-line interface for Google Cloud, and kubectl provides the primary command-line interface for running commands against Kubernetes clusters.

Launch Cloud Shell:

  1. Go to the Google Cloud console.

    Google Cloud console

  2. From the upper-right corner of the console, click the Activate Cloud Shell button:

A Cloud Shell session appears inside the console. You use this shell to run gcloud and kubectl commands.

Prepare your environment

In this section, you prepare your environment to follow the tutorial.

Set the default settings for the gcloud CLI

Set environment variables for your project ID, the zone, and the name of your new cluster.

export PROJECT_ID=PROJECT_ID
export ZONE=us-central1-a
export CLUSTER_NAME=my-cluster

Replace PROJECT_ID with the project ID you chose for this tutorial in the Before you begin section.

In this tutorial, you create resources in us-central1-a. To see a complete list of where the Tau T2A machine series is available, see Available regions and zones.

Clone the git repository

This tutorial uses resources from the Arm on GKE GitHub repository.

  1. Clone the repository:

    git clone https://github.com/GoogleCloudPlatform/gke-arm
    
  2. Change your current working directory to the gke-arm/migrate-x86-app-to-multi-arch/ from the repository cloned in the previous step:

    cd gke-arm/migrate-x86-app-to-multi-arch/
    

Create a GKE cluster and deploy the x86 application

In the first part of this tutorial, you create a cluster with x86 nodes and deploy an x86 application. The example application is a service which responds to HTTP requests. It is built with the Golang programming language.

This setup represents what a typical cluster environment might look like, using x86-compatible applications and x86 nodes.

Create a GKE cluster

First, create a GKE using nodes with x86 processors. With this configuration, you create a typical cluster environment to run x86 applications.

Create the cluster:

gcloud container clusters create $CLUSTER_NAME \
    --release-channel=rapid \
    --zone=$ZONE \
    --machine-type=e2-standard-2 \
    --num-nodes=1 \
    --enable-gvnic \
    --async

This cluster has autoscaling disabled in order to demonstrate specific functionality in later steps.

It might take several minutes to finish creating the cluster. The --async flag lets this operation run in the background while you complete the next steps.

You can create clusters with only Arm nodes, however for this tutorial you will create a cluster with only x86 nodes first to learn about the process of making x86-only applications compatible with Arm.

Create the Artifact Registry Docker repository

  1. Create a repository in Artifact Registry to store Docker images:

    gcloud artifacts repositories create docker-repo \
          --repository-format=docker \
          --location=us-central1 \
          --description="Docker repository"
    
  2. Configure the Docker command-line tool to authenticate to this repository in Artifact Registry:

    gcloud auth configure-docker us-central1-docker.pkg.dev
    

Build the x86 image and push it to Artifact Registry

  1. Build the x86-compatible version of the application:

    docker build -t us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/x86-hello:v0.0.1 . 
    
  2. Push the image to Artifact Registry:

    docker push us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/x86-hello:v0.0.1
    

Deploy the x86 application

  1. Check that the cluster is ready by running the following script:

    echo
    echo -ne "Waiting for GKE cluster to finish provisioning"
    gke_status=""
    while [ -z $gke_status ]; do
       sleep 2
       echo -ne '.'   
       gke_status=$(gcloud container clusters list --format="value(STATUS)" --filter="NAME=$CLUSTER_NAME AND STATUS=RUNNING")
    done
    echo
    echo "GKE Cluster '$CLUSTER_NAME' is $gke_status" 
    echo
    

    When the cluster is ready, the output should be similar to the following:

    GKE Cluster 'my-cluster' is RUNNING
    
  2. Retrieve the cluster credentials so that kubectl can connect to the Kubernetes API for the cluster:

    gcloud container clusters get-credentials $CLUSTER_NAME --zone $ZONE --project $PROJECT_ID
    
  3. Update the image using kustomize and deploy the x86 application:

    $(cd k8s/overlays/x86 && kustomize edit set image hello=us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/x86-hello:v0.0.1) 
    kubectl apply -k k8s/overlays/x86
    
  4. Deploy a Service to expose the application to the Internet:

    kubectl apply -f k8s/hello-service.yaml
    
  5. Check that the external IP address for the Service, hello-service, is finished provisioning:

    echo
    echo -ne "Waiting for External IP to be provisioned"
    external_ip=""
    while [ -z $external_ip ]; do
       sleep 2
       echo -ne '.'
       external_ip=$(kubectl get svc hello-service --template="{{range .status.loadBalancer.ingress}}{{.ip}}{{end}}")
    done
    echo
    echo "External IP: $external_ip"
    echo
    

    After the external IP address is provisioned, the output should be similar to the following:

    External IP: 203.0.113.0
    
  6. Make an HTTP request to test that the deployment works as expected:

    curl -w '\n' http://$external_ip
    

    The output is similar to the following:

    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-mwfkd, CPU PLATFORM:linux/amd64
    

    The output shows that this x86-compatible deployment is running on a node in the default node pool on the amd64 architecture. The nodes in the default node pool of your cluster have x86 (either Intel or AMD) processors.

Add Arm nodes to the cluster

In the next part of this tutorial, add Arm nodes to your existing cluster. These nodes are where the Arm-compatible version of your application is deployed when it's rebuilt to run on Arm.

Checkpoint

So far you've accomplished the following objectives:

  • create a GKE cluster using x86 nodes.
  • store an x86-compatible container image with Docker in Artifact Registry.
  • deploy an x86-compatible workload to a GKE cluster.

You've configured a cluster environment with x86 nodes and an x86-compatible workload. This configuration is similar to your existing cluster environments if you don't currently use Arm nodes and Arm-compatible workloads.

Add an Arm node pool to your cluster

Add an Arm node pool to your existing cluster:

gcloud container node-pools create arm-pool \
    --cluster $CLUSTER_NAME \
    --zone $ZONE \
    --machine-type=t2a-standard-2 \
    --num-nodes=1 \
    --enable-gvnic

The t2a-standard-2 machine type is an Arm VM from the Tau T2A machine series (Preview).

You create a node pool with Arm nodes in the same way as creating a node pool with x86 nodes. After this node pool is created, you will have both x86 nodes and Arm nodes running in this cluster.

To learn more about adding Arm node pools to existing clusters, see Add an Arm node pool to a GKE cluster.

Scale up the existing application running on x86-based nodes

Nodes of multiple architecture types can work seamlessly together in one cluster. GKE doesn't schedule existing workloads running on x86 nodes to Arm nodes in the cluster because a taint is automatically placed on Arm nodes. You can see this by scaling up your existing application.

  1. Update the workload, scaling it up to 6 replicas:

    $(cd k8s/overlays/x86_increase_replicas && kustomize edit set image hello=us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/x86-hello:v0.0.1) 
    kubectl apply -k k8s/overlays/x86_increase_replicas/
    
  2. Wait 30 seconds, then run the following command to check the status of the deployment:

    kubectl get pods -l="app=hello" --field-selector="status.phase=Pending"
    

    The output should look similar to the following:

    NAME                                    READY   STATUS    RESTARTS   AGE
    x86-hello-deployment-6b7b456dd5-6tkxd   0/1     Pending   0          40s
    x86-hello-deployment-6b7b456dd5-k95b7   0/1     Pending   0          40s
    x86-hello-deployment-6b7b456dd5-kc876   0/1     Pending   0          40s
    

    This output shows Pods with a Pending status as there is no room left on the x86-based nodes. Since Cluster Autoscaler is disabled and the Arm nodes are tainted, the workloads will not be deployed on any of the available Arm nodes. This taint prevents GKE from scheduling x86 workloads on Arm nodes. To deploy to Arm nodes, you must indicate that the deployment is compatible with Arm nodes.

  3. Check the Pods that are in the Running state:

    kubectl get pods -l="app=hello" --field-selector="status.phase=Running" -o wide
    

    The output should look similar to the following:

    NAME                                    READY   STATUS    RESTARTS   AGE   IP            NODE                                        NOMINATED NODE   READINESS GATES
    x86-hello-deployment-6b7b456dd5-cjclz   1/1     Running   0          62s   10.100.0.17   gke-my-cluster-default-pool-32019863-b41t   <none>           <none>
    x86-hello-deployment-6b7b456dd5-mwfkd   1/1     Running   0          34m   10.100.0.11   gke-my-cluster-default-pool-32019863-b41t   <none>           <none>
    x86-hello-deployment-6b7b456dd5-n56rg   1/1     Running   0          62s   10.100.0.16   gke-my-cluster-default-pool-32019863-b41t   <none>           <none>
    

    In this output, the NODE column indicates that all Pods from the deployment are running only in the default-pool, meaning that the x86-compatible Pods are only scheduled to the x86 nodes. The original Pod that was already scheduled before the creation of the Arm node pool is still running on the same node.

  4. Run the following command to access the service and see the output:

    for i in $(seq 1 6); do curl -w '\n' http://$external_ip; done
    

    The output is similar to the following:

    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-cjclz, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-cjclz, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-n56rg, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-n56rg, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-cjclz, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-cjclz, CPU PLATFORM:linux/amd64
    

    This output shows that all Pods serving requests are running on x86 nodes. Some Pods cannot respond because they are still in the Pending state as there is no space on the existing x86 nodes and they will not be scheduled to Arm nodes.

Rebuild your application to run on Arm

In the previous section, you added an Arm node pool to your existing cluster. However, when you scaled up the existing x86 application, it did not schedule any of the workloads to the Arm nodes. In this section, you rebuild your application to be Arm-compatible, so that this application can run on the Arm nodes in the cluster.

For this example, accomplish these steps by using docker build. This two-step approach includes:

  • First stage: Build the code to Arm.
  • Second stage: Copy the executable to a lean container.

After following these steps, you will have an Arm-compatible image in addition to the x86-compatible image.

The second step of copying the executable to another container follows one of the best practices for building a container, which is to build the smallest image possible.

This tutorial uses an example application built with the Golang programming language. With Golang, you can cross-compile an application to different operating systems and CPU platforms by providing environment variables, GOOS and GOARCH, respectively.

  1. Run cat Dockerfile_arm to see the Dockerfile written for Arm:

    #
    # Build: 1st stage
    #
    FROM golang:1.18-alpine as builder 
    WORKDIR /app
    COPY go.mod .
    COPY hello.go .
    RUN GOARCH=arm64 go build -o /hello && \
       apk add --update --no-cache file && \
       file /hello
    

    The snippet shown here shows just the first stage. In the file, both stages are included.

    In this file, setting GOARCH=arm64 instructs the Go compiler to build the application for the Arm instruction set. You do not need to set GOOS because the base image in the first stage is a Linux Alpine image.

  2. Build the code for Arm, and push it to Artifact Registry:

    docker build -t us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/arm-hello:v0.0.1 -f Dockerfile_arm .
    docker push us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/arm-hello:v0.0.1
    

Deploy the Arm version of your application

Now that the application is built to run on Arm nodes, you can deploy it to the Arm nodes in your cluster.

  1. Inspect the add_arm_support.yaml by running cat k8s/overlays/arm/add_arm_support.yaml:

    The output is similar to the following:

       nodeSelector:
          kubernetes.io/arch: arm64
       tolerations:
        - key: kubernetes.io/arch
          operator: Equal
          value: arm64
          effect: NoSchedule
    

    This nodeSelector specifies that the workload should run only on the Arm nodes. The toleration matches the taint set on all Arm nodes so that GKE can schedule the workload there. To learn more about setting these fields, see Prepare an Arm workload for deployment.

  2. Deploy one replica of the Arm-compatible version of the application:

    $(cd k8s/overlays/arm && kustomize edit set image hello=us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/arm-hello:v0.0.1) 
    kubectl apply -k k8s/overlays/arm
    
  3. Wait 5 seconds, then check that the Arm deployment is answering curl requests:

    for i in $(seq 1 6); do curl -w '\n' http://$external_ip; done
    

    The output is similar to the following:

    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-n56rg, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-n56rg, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-mwfkd, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-mwfkd, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-arm-pool-e172cff7-shwc, POD:arm-hello-deployment-69b4b6bdcc-n5l28, CPU PLATFORM:linux/arm64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:x86-hello-deployment-6b7b456dd5-n56rg, CPU PLATFORM:linux/amd64
    

    This output should include responses from both the x86-compatible and Arm-compatible applications responding to the curl request.

Build a multi-architecture image to run a workload across architectures

While you can use the strategy described in the previous section and deploy separate workloads for x86 and Arm, this would require you to maintain and keep organized two build processes and two container images.

Ideally, you want to build and run your application seamlessly across both x86 and Arm platforms. We recommend this approach. To run your application with one manifest across multiple architecture platforms, you need to use multi-architecture (multi-arch) images. To learn more about multi-architecture images, see Build multi-arch images for Arm workloads.

To use multi-architecture images, you must ensure that your application meets the following prerequisites:

  • Your application does not have any architecture platform-specific dependencies.
  • All dependencies must be built for multi-architecture or, at minimum, the targeted platforms.

The example application used in this tutorial meets both of these prerequisites. However, we recommend testing your own applications when building their multi-arch images before deploying them to production.

Build and push multi-architecture images

You can build multi-arch images with Docker Buildx if your workload fulfills the following prerequisites:

  • The base image supports multiple architectures. Check this by running docker manifest inspect on the base image and checking the list of architecture platforms. See an example of how to inspect an image at the end of this section.
  • The application does not require special build steps for each architecture platform. If special steps were required, Buildx might not be sufficient. You would need to have a separate Dockerfile for each platform and create the manifest manually with docker manifest create.

The example application's base image is Alpine, which supports multiple architectures. There are also no architecture platform-specific steps, so you can build the multi-arch image with Buildx.

  1. Inspect the Dockerfile by running cat Dockerfile:

    # This is a multi-stage Dockerfile. 
    # 1st stage builds the app in the target platform
    # 2nd stage create a lean image coping the binary from the 1st stage
    
    #
    # Build: 1st stage
    #
    FROM golang:1.18-alpine as builder 
    ARG BUILDPLATFORM    
    ARG TARGETPLATFORM
    RUN echo "I am running on $BUILDPLATFORM, building for $TARGETPLATFORM"  
    WORKDIR /app
    COPY go.mod .
    COPY hello.go .
    RUN go build -o /hello && \
       apk add --update --no-cache file && \
       file /hello   
    
    #
    # Release: 2nd stage
    #
    FROM alpine
    WORKDIR /
    COPY --from=builder /hello /hello
    CMD [ "/hello" ]
    

    This Dockerfile defines two stages: the build stage and release stage. You use the same Dockerfile used for building the x86 application. If you follow best practices for building containers, you might be able to rebuild your own container images without changing anything.

  2. Run the following command to create and use a new docker buildx builder:

    docker buildx create --name multiarch --use --bootstrap
    

    Now that you have created this new builder, you can build and push an image that is compatible with both linux/amd64 and linux/arm64 by using the --platform flag. For each platform provided with the flag, Buildx builds an image in the target platform. When Buildx builds the linux/arm64 image, it downloads arm64 base images. In the first stage, it builds the binary on the arm64 golang:1.18-alpine image for arm64. In the second stage, the arm64 Alpine Linux image is downloaded and the binary is copied to a layer of that image.

  3. Build and push the image:

    docker buildx build -t us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/multiarch-hello:v0.0.1 -f Dockerfile --platform linux/amd64,linux/arm64 --push .
    

    The output is similar to the following:

    => [linux/arm64 builder x/x] ..
    => [linux/amd64 builder x/x] ..
    

    This output shows that two images are generated, one for linux/arm64 and one for linux/amd64.

  4. Inspect the manifest of your new multi-arch image:

    docker manifest inspect us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/multiarch-hello:v0.0.1
    

    The output is similar to the following:

    {
       "schemaVersion": 2,
       "mediaType": "application/vnd.docker.distribution.manifest.list.v2+json",
       "manifests": [
          {
             "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
             "size": 739,
             "digest": "sha256:dfcf8febd94d61809bca8313850a5af9113ad7d4741edec1362099c9b7d423fc",
             "platform": {
                "architecture": "amd64",
                "os": "linux"
             }
          },
          {
             "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
             "size": 739,
             "digest": "sha256:90b637d85a93c3dc03fc7a97d1fd640013c3f98c7c362d1156560bbd01f6a419",
             "platform": {
                "architecture": "arm64",
                "os": "linux"
             }
          }
       ]
    

    In this output, the manifests section includes two manifests, one with the amd64 platform architecture, and the other with the arm64 platform architecture.

    When you deploy this container image to your cluster, GKE automatically downloads only the image that matches the node's architecture.

Deploy the multi-arch version of your application

  1. Before you deploy the multi-arch image, delete the original workloads:

    kubectl delete deploy x86-hello-deployment arm-hello-deployment
    
  2. Inspect the add_multiarch_support.yaml kustomize overlay by running cat k8s/overlays/multiarch/add_multiarch_support.yaml:

    The output includes the following toleration set:

       tolerations:
          - key: kubernetes.io/arch
             operator: Equal
             value: arm64
             effect: NoSchedule
    

    This toleration allows the workload to run on the Arm nodes in your cluster, since the toleration matches the taint set on all Arm nodes. As this workload can now run on any node in the cluster, only the toleration is needed. With just the toleration, GKE can schedule the workload to both x86 and Arm nodes. If you want to specify where GKE can schedule workloads, use node selectors and node affinity rules. To learn more about setting these fields, see Prepare an Arm workload for deployment.

  3. Deploy the multi-arch container image with 6 replicas:

    $(cd k8s/overlays/multiarch && kustomize edit set image hello=us-central1-docker.pkg.dev/$PROJECT_ID/docker-repo/multiarch-hello:v0.0.1) 
    kubectl apply -k k8s/overlays/multiarch
    
  4. Wait 10 seconds, then confirm that all of the replicas of the application are running:

    kubectl get pods -l="app=hello" -o wide
    

    The output is similar to the following:

    NAME                                         READY   STATUS    RESTARTS   AGE   IP            NODE                                        NOMINATED NODE   READINESS GATES
    multiarch-hello-deployment-65bfd784d-5xrrr   1/1     Running   0          95s   10.100.1.5    gke-my-cluster-arm-pool-e172cff7-shwc       <none>           <none>
    multiarch-hello-deployment-65bfd784d-7h94b   1/1     Running   0          95s   10.100.1.4    gke-my-cluster-arm-pool-e172cff7-shwc       <none>           <none>
    multiarch-hello-deployment-65bfd784d-7qbkz   1/1     Running   0          95s   10.100.1.7    gke-my-cluster-arm-pool-e172cff7-shwc       <none>           <none>
    multiarch-hello-deployment-65bfd784d-7wqb6   1/1     Running   0          95s   10.100.1.6    gke-my-cluster-arm-pool-e172cff7-shwc       <none>           <none>
    multiarch-hello-deployment-65bfd784d-h2g2k   1/1     Running   0          95s   10.100.0.19   gke-my-cluster-default-pool-32019863-b41t   <none>           <none>
    multiarch-hello-deployment-65bfd784d-lc9dc   1/1     Running   0          95s   10.100.0.18   gke-my-cluster-default-pool-32019863-b41t   <none>           <none>
    

    This output includes a NODE column that indicates the Pods are running on both nodes in the Arm node pool and others in the default (x86) node pool.

  5. Run the following command to access the service and see the output:

    for i in $(seq 1 6); do curl -w '\n' http://$external_ip; done
    

    The output is similar to the following:

    Hello from NODE:gke-my-cluster-arm-pool-e172cff7-shwc, POD:multiarch-hello-deployment-65bfd784d-7qbkz, CPU PLATFORM:linux/arm64
    Hello from NODE:gke-my-cluster-default-pool-32019863-b41t, POD:multiarch-hello-deployment-65bfd784d-lc9dc, CPU PLATFORM:linux/amd64
    Hello from NODE:gke-my-cluster-arm-pool-e172cff7-shwc, POD:multiarch-hello-deployment-65bfd784d-5xrrr, CPU PLATFORM:linux/arm64
    Hello from NODE:gke-my-cluster-arm-pool-e172cff7-shwc, POD:multiarch-hello-deployment-65bfd784d-7wqb6, CPU PLATFORM:linux/arm64
    Hello from NODE:gke-my-cluster-arm-pool-e172cff7-shwc, POD:multiarch-hello-deployment-65bfd784d-7h94b, CPU PLATFORM:linux/arm64
    Hello from NODE:gke-my-cluster-arm-pool-e172cff7-shwc, POD:multiarch-hello-deployment-65bfd784d-7wqb6, CPU PLATFORM:linux/arm64
    

    You should see that Pods running across architecture platforms are answering the requests.

You built and deployed a multi-arch image to seamlessly run a workload across multiple architectures.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

After you finish the tutorial, you can clean up the resources that you created to reduce quota usage and stop billing charges. The following sections describe how to delete or turn off these resources.

Delete the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

  1. In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then click Delete.
  3. In the dialog, type the project ID, and then click Shut down to delete the project.

Delete the service, cluster, and repository

If you don't want to delete the entire project, delete the cluster and repository that you created for the tutorial:

  1. Delete the application's Service by running kubectl delete:

    kubectl delete service hello-service
    

    This command deletes the Compute Engine load balancer that you created when you exposed the Deployment.

  2. Delete your cluster by running gcloud container clusters delete:

    gcloud container clusters delete $CLUSTER_NAME --zone $ZONE
    
  3. Delete the repository:

    gcloud artifacts repositories delete docker-repo —location=us-central1 --async
    

What's next