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Preparing for a Run:ai OpenShift Installation

The following section provides IT with the information needed to prepare for a Run:ai installation. This includes Third-party dependencies which must be met as well as access control that must be granted for Run:ai components.

Create OpenShift Projects

Run:ai uses three projects. One for the control plane (runai-backend) and two for the cluster itself (runai, runai-reservation). The project gpu-operator is used by the GPU Opeator dependency described above.

oc new-project runai
oc new-project runai-reservation
oc new-project runai-backend
oc new-project gpu-operator-resources

Prepare Run:ai Installation Artifacts

Run:ai Software Files

SSH into a node with oc access (oc is the OpenShift command-line) to the cluster and Docker installed.

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

oc apply -f runai-gcr-secret.yaml -n runai-backend
oc apply -f runai-gcr-secret.yaml -n gpu-operator-resources

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

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

Upload images to 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 have 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).

Run:ai Administration CLI

Install the Run:ai Administrator Command-line Interface by following the steps here.

Install the Run:ai Administrator Command-line Interface by following the steps here. Use the image under deploy/runai-admin-cli-<version>-linux-amd64.tar.gz

Install Helm

If helm v3 does not yet exist on the machine, install it now:

See on how to install Helm. Run:ai works with Helm version 3 only (not helm 2).

tar xvf helm-<version>-linux-amd64.tar.gz
sudo mv linux-amd64/helm /usr/local/bin/

Mark Run:ai System Workers

The Run:ai Control plane should be installed on a set of dedicated Run:ai system worker nodes rather than GPU worker nodes. To set system worker nodes run:

oc label node <NODE-NAME>

To avoid single-point-of-failure issues, we recommend assigning more than one node in production environments.

Install NVIDIA Dependencies


You must have Cluster Administrator rights to install these dependencies.

Before installing Run:ai, you must install NVIDIA software on your OpenShift cluster to enable GPUs. NVIDIA has provided detailed documentation{target=blank}. Follow the instructions to install the two operators _Node Feature Discovery and NVIDIA GPU Operator from the OpenShift web console.

When done, verify that the GPU Operator is installed by running:

oc get pods -n gpu-operator
Run:ai 2.3 or earlier

Disable the NVIDIA Device Plugin and DCGM Exporter

After successful verification,

(1) Disable the GPU Operator by running:

oc scale --replicas=0 -n openshift-operators deployment gpu-operator

(1) Disable the NVIDIA DCGM exporter by running:

oc -n gpu-operator-resources patch daemonset nvidia-dcgm-exporter \
  -p '{"spec": {"template": {"spec": {"nodeSelector": {"non-existing": "true"}}}}}'

(2) Replace the NVIDIA Device Plug-in with the Run:ai version:

oc patch daemonsets.apps -n gpu-operator-resources nvidia-device-plugin-daemonset \
  -p '{"spec":{"template":{"spec":{"containers":[{"name":"nvidia-device-plugin-ctr","image":""}]}}}}'
oc create clusterrolebinding --clusterrole=admin \
  --serviceaccount=gpu-operator-resources:nvidia-device-plugin nvidia-device-plugin-crb

Additional Permissions

As part of the installation, you will be required to install the 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: June 19, 2022