Create an Autopilot cluster

Autopilot

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This page explains how to create a Google Kubernetes Engine (GKE) cluster in Autopilot mode. Autopilot is a GKE mode of operation that lets you focus on your services and applications, while Google manages your nodes and infrastructure. When you deploy your workloads, GKE provisions, configures, and manages the resources and hardware, including when you scale.

Before you begin

Before you start, make sure you have performed the following tasks:

• Enable the Google Kubernetes Engine API.
Enable Google Kubernetes Engine API
• If you want to use the Google Cloud CLI for this task, install and then initialize the gcloud CLI.
  Note: For existing gcloud CLI installations, make sure to set the compute/region and compute/zone properties. By setting default locations, you can avoid errors in gcloud CLI like the following: One of [--zone, --region] must be supplied: Please specify location.

Caution: If you have the organization policy constraint constraints/compute.vmExternalIpAccess configured to Deny All or to restrict external IPs to specific VM instances at the organization, folder, or project level where you are trying to create a public GKE Autopilot cluster, then your cluster creation operations will fail. See the troubleshooting information for details.

Create an Autopilot cluster

You can create an Autopilot cluster by using the Google Cloud CLI or the Google Cloud console.

gcloud

Run the following command:

gcloud container clusters create-auto CLUSTER_NAME \
    --region REGION \
    --project=PROJECT_ID 

Replace the following:

• CLUSTER_NAME: the name of your new Autopilot cluster.
• REGION: the region for your cluster, such as us-central1.
• PROJECT_ID: your project ID.

You can optionally use the --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com flag to specify a different IAM service account that nodes use instead of the Compute Engine default service account. We strongly recommend that you create and use a service account with minimal privileges so that your nodes don't have more privileges than they require.

Caution: You cannot modify the IAM service account on an existing Autopilot cluster.

For a list of other options that you can specify, see the gcloud container clusters create-auto reference documentation.

Console

Perform the following tasks:

1. Go to the Google Kubernetes Engine page in the Google Cloud console.

   Go to Google Kubernetes Engine

2. Click Create.

3. For Autopilot, click Configure.

4. Enter the Name for your cluster.

5. Select a region for your cluster.

6. Choose Public cluster. If your organization has specific network isolation requirements, consider creating a private Autopilot cluster instead. For instructions, refer to Create a private cluster.

7. Optionally, change networking settings and advanced settings.

8. Click Create.

Set the version and release channel of a new Autopilot cluster

By default, GKE enrolls new Autopilot clusters in the Regular release channel, with the default GKE version in the channel. You can change the release channel when you create an Autopilot cluster using the gcloud CLI or the Google Cloud console. You can also set a specific GKE version when you create a cluster using the gcloud CLI.

gcloud

To set the release channel and the cluster version when you create an Autopilot cluster, run the following command. Setting the cluster version is only useful if you have a specific version requirement. In most situations, we recommend that you only set the release channel.

gcloud container clusters create-auto CLUSTER_NAME \
    --region=COMPUTE_REGION \
    --release-channel=RELEASE_CHANNEL \
    --cluster-version=VERSION

Replace the following:

• RELEASE_CHANNEL: the name of the release channel for the cluster. The default is Regular.
• CLUSTER_VERSION: the GKE version for the cluster, such as 1.22.11-gke.400. If omitted, the default value is the release channel's default version. To check the available versions, refer to What versions are available in a channel?. You can run any minor version in your selected channel or choose a subset of patch versions from other channels.

These flags are optional. If you set a specific GKE version, that version applies until a new version becomes the default version in your cluster's release channel. GKE then automatically upgrades your cluster to the new default version.

Console

To set the release channel when you create an Autopilot cluster, do the following:

1. Go to the Google Kubernetes Engine page in the Google Cloud console.

   Go to Google Kubernetes Engine

2. Click Create.

3. For Autopilot, click Configure.

4. Specify a Name.

5. Select a Region.

6. Click Advanced options.

7. Choose a Release channel.

8. Change other settings as needed, and then click Create.

You can also change the release channel and GKE version for existing clusters. For instructions, refer to Manually upgrading a control plane and to Selecting a new release channel.

Connect to the cluster

gcloud

gcloud container clusters get-credentials CLUSTER_NAME \
    --region REGION \
    --project=PROJECT_ID

This command configures kubectl to use the cluster you created.

Console

1. In the cluster list, beside the cluster that you want to connect to, click Actions, and then click Connect.

2. Click Run in Cloud Shell when prompted. The generated command is copied into your Cloud Shell, for example:

   gcloud container clusters get-credentials autopilot-cluster --region us-east1 --project autopilot-test
   

3. Press Enter to run the command.

Verify the cluster mode

You can verify that your cluster is an Autopilot cluster by using the gcloud CLI or the Google Cloud console.

gcloud

To verify that your cluster is created in Autopilot mode, run the following command:

gcloud container clusters describe CLUSTER_NAME \
    --region REGION

The output contains the following:

autopilot:
  enabled: true

Console

To verify that your cluster is created in Autopilot mode, in the cluster list, check the Mode column. The status shows Autopilot.

What's next

• Deploying a workload.
• Scaling an application.