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Creating a cluster via the CLI on Google Cloud Platform.

Creating a Cluster via the CLI

In this guide, we will create an HA Kubernetes cluster in GCP with 1 worker node. We will assume an existing Cloud Storage bucket, and some familiarity with Google Cloud. If you need more information on Google Cloud specifics, please see the official Google documentation.

jq and talosctl also needs to be installed

Manual Setup

Environment Setup

We’ll make use of the following environment variables throughout the setup. Edit the variables below with your correct information.

# Storage account to use
export STORAGE_BUCKET="StorageBucketName"
# Region
export REGION="us-central1"

Create the Image

First, download the Google Cloud image from a Talos release. These images are called gcp-$ARCH.tar.gz.

Upload the Image

Once you have downloaded the image, you can upload it to your storage bucket with:

gsutil cp /path/to/gcp-amd64.tar.gz gs://$STORAGE_BUCKET

Register the image

Now that the image is present in our bucket, we’ll register it.

gcloud compute images create talos \
 --source-uri=gs://$STORAGE_BUCKET/gcp-amd64.tar.gz \

Network Infrastructure

Load Balancers and Firewalls

Once the image is prepared, we’ll want to work through setting up the network. Issue the following to create a firewall, load balancer, and their required components. and are the GCP Load Balancer IP ranges

# Create Instance Group
gcloud compute instance-groups unmanaged create talos-ig \
  --zone $REGION-b

# Create port for IG
gcloud compute instance-groups set-named-ports talos-ig \
    --named-ports tcp6443:6443 \
    --zone $REGION-b

# Create health check
gcloud compute health-checks create tcp talos-health-check --port 6443

# Create backend
gcloud compute backend-services create talos-be \
    --global \
    --protocol TCP \
    --health-checks talos-health-check \
    --timeout 5m \
    --port-name tcp6443

# Add instance group to backend
gcloud compute backend-services add-backend talos-be \
    --global \
    --instance-group talos-ig \
    --instance-group-zone $REGION-b

# Create tcp proxy
gcloud compute target-tcp-proxies create talos-tcp-proxy \
    --backend-service talos-be \
    --proxy-header NONE

# Create LB IP
gcloud compute addresses create talos-lb-ip --global

# Forward 443 from LB IP to tcp proxy
gcloud compute forwarding-rules create talos-fwd-rule \
    --global \
    --ports 443 \
    --address talos-lb-ip \
    --target-tcp-proxy talos-tcp-proxy

# Create firewall rule for health checks
gcloud compute firewall-rules create talos-controlplane-firewall \
     --source-ranges, \
     --target-tags talos-controlplane \
     --allow tcp:6443

# Create firewall rule to allow talosctl access
gcloud compute firewall-rules create talos-controlplane-talosctl \
  --source-ranges \
  --target-tags talos-controlplane \
  --allow tcp:50000

Cluster Configuration

With our networking bits setup, we’ll fetch the IP for our load balancer and create our configuration files.

LB_PUBLIC_IP=$(gcloud compute forwarding-rules describe talos-fwd-rule \
               --global \
               --format json \
               | jq -r .IPAddress)

talosctl gen config talos-k8s-gcp-tutorial https://${LB_PUBLIC_IP}:443

Additionally, you can specify --config-patch with RFC6902 jsonpatch which will be applied during the config generation.

Compute Creation

We are now ready to create our GCP nodes.

# Create the control plane nodes.
for i in $( seq 1 3 ); do
  gcloud compute instances create talos-controlplane-$i \
    --image talos \
    --zone $REGION-b \
    --tags talos-controlplane \
    --boot-disk-size 20GB \
    --tags talos-controlplane-$i

# Add control plane nodes to instance group
for i in $( seq 1 3 ); do
  gcloud compute instance-groups unmanaged add-instances talos-ig \
      --zone $REGION-b \
      --instances talos-controlplane-$i

# Create worker
gcloud compute instances create talos-worker-0 \
  --image talos \
  --zone $REGION-b \
  --boot-disk-size 20GB \
  --tags talos-worker-$i

Bootstrap Etcd

You should now be able to interact with your cluster with talosctl. We will need to discover the public IP for our first control plane node first.

CONTROL_PLANE_0_IP=$(gcloud compute instances describe talos-controlplane-0 \
                     --zone $REGION-b \
                     --format json \
                     | jq -r '.networkInterfaces[0].accessConfigs[0].natIP')

Set the endpoints and nodes:

talosctl --talosconfig talosconfig config endpoint $CONTROL_PLANE_0_IP
talosctl --talosconfig talosconfig config node $CONTROL_PLANE_0_IP

Bootstrap etcd:

talosctl --talosconfig talosconfig bootstrap

Retrieve the kubeconfig

At this point we can retrieve the admin kubeconfig by running:

talosctl --talosconfig talosconfig kubeconfig .


# cleanup VM's
gcloud compute instances delete \
  talos-worker-0 \
  talos-controlplane-0 \
  talos-controlplane-1 \

# cleanup firewall rules
gcloud compute firewall-rules delete \
  talos-controlplane-talosctl \

# cleanup forwarding rules
gcloud compute forwarding-rules delete \

# cleanup addresses
gcloud compute addresses delete \

# cleanup proxies
gcloud compute target-tcp-proxies delete \

# cleanup backend services
gcloud compute backend-services delete \

# cleanup health checks
gcloud compute health-checks delete \

# cleanup unmanaged instance groups
gcloud compute instance-groups unmanaged delete \

# cleanup Talos image
gcloud compute images delete \

Using GCP Deployment manager

Using GCP deployment manager automatically creates a Google Storage bucket and uploads the Talos image to it. Once the deployment is complete the generated talosconfig and kubeconfig files are uploaded to the bucket.

By default this setup creates a three node control plane and a single worker in us-west1-b

First we need to create a folder to store our deployment manifests and perform all subsequent operations from that folder.

mkdir -p talos-gcp-deployment
cd talos-gcp-deployment

Getting the deployment manifests

We need to download two deployment manifests for the deployment from the Talos github repository.

curl -fsSLO ""
curl -fsSLO ""
# if using ccm
curl -fsSLO ""

Updating the config

Now we need to update the local config.yaml file with any required changes such as changing the default zone, Talos version, machine sizes, nodes count etc.

An example config.yaml file is shown below:

  - path: talos-ha.jinja

  - name: talos-ha
    type: talos-ha.jinja
      zone: us-west1-b
      talosVersion: v1.4.8
      externalCloudProvider: false
      controlPlaneNodeCount: 5
      controlPlaneNodeType: n1-standard-1
      workerNodeCount: 3
      workerNodeType: n1-standard-1
  - name: bucketName
    value: $(ref.talos-ha.bucketName)

Enabling external cloud provider

Note: The externalCloudProvider property is set to false by default. The manifest used for deploying the ccm (cloud controller manager) is currently using the GCP ccm provided by openshift since there are no public images for the ccm yet.

Since the routes controller is disabled while deploying the CCM, the CNI pods needs to be restarted after the CCM deployment is complete to remove the taint. See Nodes network-unavailable taint not removed after installing ccm for more information

Use a custom built image for the ccm deployment if required.

Creating the deployment

Now we are ready to create the deployment. Confirm with y for any prompts. Run the following command to create the deployment:

# use a unique name for the deployment, resources are prefixed with the deployment name
export DEPLOYMENT_NAME="<deployment name>"
gcloud deployment-manager deployments create "${DEPLOYMENT_NAME}" --config config.yaml

Retrieving the outputs

First we need to get the deployment outputs.

# first get the outputs
OUTPUTS=$(gcloud deployment-manager deployments describe "${DEPLOYMENT_NAME}" --format json | jq '.outputs[]')

BUCKET_NAME=$(jq -r '. | select(.name == "bucketName").finalValue' <<< "${OUTPUTS}")
# used when cloud controller is enabled
SERVICE_ACCOUNT=$(jq -r '. | select(.name == "serviceAccount").finalValue' <<< "${OUTPUTS}")
PROJECT=$(jq -r '. | select(.name == "project").finalValue' <<< "${OUTPUTS}")

Note: If cloud controller manager is enabled, the below command needs to be run to allow the controller custom role to access cloud resources

gcloud projects add-iam-policy-binding \
    "${PROJECT}" \
    --member "serviceAccount:${SERVICE_ACCOUNT}" \
    --role roles/iam.serviceAccountUser

gcloud projects add-iam-policy-binding \
    "${PROJECT}" \
    --member serviceAccount:"${SERVICE_ACCOUNT}" \
    --role roles/compute.admin

gcloud projects add-iam-policy-binding \
    "${PROJECT}" \
    --member serviceAccount:"${SERVICE_ACCOUNT}" \
    --role roles/compute.loadBalancerAdmin

Downloading talos and kube config

In addition to the talosconfig and kubeconfig files, the storage bucket contains the controlplane.yaml and worker.yaml files used to join additional nodes to the cluster.

gsutil cp "gs://${BUCKET_NAME}/generated/talosconfig" .
gsutil cp "gs://${BUCKET_NAME}/generated/kubeconfig" .

Deploying the cloud controller manager

kubectl \
  --kubeconfig kubeconfig \
  --namespace kube-system \
  apply \
  --filename gcp-ccm.yaml
#  wait for the ccm to be up
kubectl \
  --kubeconfig kubeconfig \
  --namespace kube-system \
  rollout status \
  daemonset cloud-controller-manager

If the cloud controller manager is enabled, we need to restart the CNI pods to remove the taint.

# restart the CNI pods, in this case flannel
kubectl \
  --kubeconfig kubeconfig \
  --namespace kube-system \
  rollout restart \
  daemonset kube-flannel
# wait for the pods to be restarted
kubectl \
  --kubeconfig kubeconfig \
  --namespace kube-system \
  rollout status \
  daemonset kube-flannel

Check cluster status

kubectl \
  --kubeconfig kubeconfig \
  get nodes

Cleanup deployment

Warning: This will delete the deployment and all resources associated with it.

Run below if cloud controller manager is enabled

gcloud projects remove-iam-policy-binding \
    "${PROJECT}" \
    --member "serviceAccount:${SERVICE_ACCOUNT}" \
    --role roles/iam.serviceAccountUser

gcloud projects remove-iam-policy-binding \
    "${PROJECT}" \
    --member serviceAccount:"${SERVICE_ACCOUNT}" \
    --role roles/compute.admin

gcloud projects remove-iam-policy-binding \
    "${PROJECT}" \
    --member serviceAccount:"${SERVICE_ACCOUNT}" \
    --role roles/compute.loadBalancerAdmin

Now we can finally remove the deployment

# delete the objects in the bucket first
gsutil -m rm -r "gs://${BUCKET_NAME}"
gcloud deployment-manager deployments delete "${DEPLOYMENT_NAME}" --quiet