Cluster Install

Below are instructions on how to install a Run:ai cluster. Before installing, please review the installation prerequisites here: Run:ai GPU Cluster Prerequisites.

Install Run:ai¶

Log in to Run:ai user interface at <company-name>.run.ai. Use credentials provided by Run:ai Customer Support:

• If no clusters are currently configured, you will see a Cluster installation wizard
• If a cluster has already been configured, use the menu on the top left and select "Clusters". On the top right, click "Add New Cluster".

Using the Wizard:

1. Choose a target Kubernetes platform (see table above)
2. (SaaS and remote self-hosted cluster only) Provide a domain name for your cluster as described here.
3. (SaaS and remote self-hosted cluster only) Install a trusted certificate to the domain within Kubernetes.
4. Download a Helm values YAML file runai-<cluster-name>.yaml
5. (Optional) customize the values file. See Customize Cluster Installation
6. Install Helm
7. For RKE and EKS, perform the steps here
8. Run the helm commands as provided in the wizard.

Verify your Installation¶

• Go to <company-name>.run.ai/dashboards/now.
• Verify that the number of GPUs on the top right reflects your GPU resources on your cluster and the list of machines with GPU resources appears on the bottom line.

Version 2.9 and up

Run: kubectl get cm runai-public -n runai -o jsonpath='{.data}' | yq -P

(assumes the yq is instaled)

Example output:

cluster-version: 2.9.0
runai-public: 
  version: 2.9.0
  runaiConfigStatus: # (1)
    conditions:
    - type: DependenciesFulfilled
      status: "True"
      reason: dependencies_fulfilled
      message: Dependencies are fulfilled
    - type: Deployed
      status: "True"
      reason: deployed
      message: Resources Deployed
    - type: Available
      status: "True"
      reason: available
      message: System Available
    - type: Reconciled
      status: "True"
      reason: reconciled
      message: Reconciliation completed successfully
  optional:  # (2)
    knative:    # (3)  
      components:
        hpa:
          available: true
        knative:
          available: true
        kourier:
          available: true
    mpi:        # (4) 
      available: true

1. Verifies that all mandatory dependencies are met: NVIDIA GPU Operator, Prometheus and NGINX controller.
2. Checks whether optional product dependencies have been met.
3. See Inference prerequisites.
4. See distributed training prerequisites.

For a more extensive verification of cluster health, see Determining the health of a cluster.

Researcher Authentication¶

You must now set up Researcher Access Control.

(Optional) Set Node Roles¶

When installing a production cluster you may want to:

• Set one or more Run:ai system nodes. These are nodes dedicated to Run:ai software.
• Machine learning frequently requires jobs that require CPU but not GPU. You may want to direct these jobs to dedicated nodes that do not have GPUs, so as not to overload these machines.
• Limit Run:ai to specific nodes in the cluster.

To perform these tasks. See Set Node Roles.

Next Steps¶

• Set up Run:ai Users Working with Users.
• Set up Projects for Researchers Working with Projects.
• Set up Researchers to work with the Run:ai Command-line interface (CLI). See Installing the Run:ai Command-line Interface on how to install the CLI for users.
• Review advanced setup and maintenance scenarios.