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See the Prerequisites section above.

Prepare Installation Artifacts

Run:ai Software Files

SSH into a node with kubectl access to the cluster and Docker installed.

Run the following to enable image download from the Run:ai Container Registry on Google cloud:

kubectl create namespace runai-backend
kubectl apply -f runai-gcr-secret.yaml

To extract Run:ai files, replace <VERSION> in the command below and run:

tar xvf runai-air-gapped-<version>.tar.gz
cd deploy

kubectl create namespace runai-backend

Upload images

Upload images to a local Docker Registry. Set the Docker Registry address in the form of NAME:PORT (do not add https):

export REGISTRY_URL=<Docker Registry address>

Run the following script (you must dockerd installed and at least 20GB of free disk space to run):

sudo -E ./

If Docker is configured to run as non-root then sudo is not required.

The script should create a file named custom-env.yaml which will be used by the control-plane installation.

(Optional) Mark Run:ai System Workers

You can optionally set the Run:ai control plane to run on specific nodes. Kubernetes will attempt to schedule Run:ai pods to these nodes. If lacking resources, the Run:ai nodes will move to another, non-labeled node.

To set system worker nodes run:

kubectl label node <NODE-NAME>


Do not select the Kubernetes master as a runai-system node. This may cause Kubernetes to stop working (specifically if Kubernetes API Server is configured on 443 instead of the default 6443).

Additional Permissions

As part of the installation, you will be required to install the Run:ai Control Plane and Cluster Helm Charts. The Helm Charts require Kubernetes administrator permissions. You can review the exact permissions provided by using the --dry-run on both helm charts.

Next Steps

Continue with installing the Run:ai Control Plane.

Last update: 2023-05-21
Created: 2021-08-03