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Cluster Health

This troubleshooting guide helps you diagnose and resolve issues you may find in your cluster. The cluster status is displayed in the Run:ai Contol Plane. See Cluster Status a list of possible statuses.

Cluster is disconnected

When a cluster's status shows Disconnected, this means that no communication from the Run:ai cluster services reaches the Run:ai Control Plane.

This may reflect a networking issue from or to your Kubernetes cluster regardless of Run:ai components. In some cases, it may indicate an issue with one or more Run:ai services that communicate with the Control Plane. These are:

  • Cluster sync (cluster-sync)
  • Agent (runai-agent)
  • Assets sync (assets-sync)

Troubleshooting actions

Use the following steps to troubleshoot the issue:

  1. Check that the Run:ai services that communicate with the Control Plane are up and running. Run the following command:

    kubectl get pods -n runai | grep -E 'runai-agent|cluster-sync|assets-sync'

    If one or more of the services are not running, see Cluster has Service issues for futher guidelines.

  2. Check the network connection from the runai namespace in your cluster to the Control Plane.

    You can do that by running a connectivity check pod. This pod can be a simple container with basic network troubleshooting tools, such as curl or get. Use the following command to create the pod and determine if it can establish connections to the necessary Control Plane endpoints:

    kubectl run control-plane-connectivity-check -n runai --image=wbitt/network-multitool --command -- /bin/sh -c 'curl -sSf <control-plane-endpoint> > /dev/null && echo "Connection Successful" || echo "Failed connecting to the Control Plane"'

    Replace control-plane-endpoint with the URL of the Control Plane in your environment.

    If the pod has failed to connect to the Control Plane, check for potential network issues. Use the following guidelines:

    • Ensure that the network policies in your Kubernetes cluster allow communication between the Control Plane and the Run:ai services that communicate with the Control Plane.
    • Check both Kubernetes Network Policies and any network-related configurations at the infrastructure level.
    • Verify that the required ports and protocols are not blocked.
  3. Verify Run:ai services logs. Inspecting the logs of the Run:ai services that communicate with the CP is essential to identify any error messages.

    Run the following command on each one of the services:

    kubectl logs deployment/runai-agent -n runai
    kubectl logs deployment/cluster-sync -n runai
    kubectl logs deployment/assets-sync -n runai

    Try to identify the problem from the logs. If you cannot resolve the issue, continue to the next step.

  4. If the issue persists, contact Run:ai support for assistance.


    The previous steps can be used if you installed the cluster and the status is stuck in Waiting to connect for a long time.

Cluster has service issues

When a cluster's status shows Service issues, this means that one or more Run:ai services that are running in the cluster are not available.

  1. Run the following command to verify which are the non-functioning services, and to get more details about deployment issues and resources required by these services that may not be ready (for example, ingress was not created or is unhealthy):

    kubectl get runaiconfig -n runai runai -ojson | jq -r '.status.conditions | map(select(.type == "Available"))'

    The list of non-functioning services is also available on the UI Clusters page.

  2. After determining the non-functioning services, use the following guidelines to further investigate the issue.

    Run the following to get all the Kubernetes events and look for recent failures:

    Kubectl get events  -A

    If a required resource was created but not available or unhealthy, check the details by running:

    Kubectl describe <resource_type> <name>
  3. If the issue persists, contact Run:ai support for assistance.

Cluster has missing prerequisites

When a cluster's status displays Missing prerequisites, it indicates that at least one of the Mandatory Prerequisites has not been fulfilled. In such cases, Run:ai services may not function properly.

If you have ensured that all prerequisites are installed and the status still shows Missing prerequisites, follow these steps:

  1. Check the message in the Control Plane for further details regarding the missing prerequisites.
  2. Inspect the runai-public ConfigMap and look for the dependencies.required field to obtain detailed information about the missing resources.
  3. If the issue persists, contact Run:ai support for assistance.

General tests to verify the Run:ai cluster health

Use the following tests regularly to determine the health of the Run:ai cluster, regardless of the cluster status and the troubleshooting options previously described.

Verify that data is sent to the cloud

Log in to <company-name>

  • Verify that all metrics in the overview dashboard are showing.
  • Verify that all metrics are showing in the Nodes view.
  • Go to Projects and create a new Project. Find the new Project using the CLI command: runai list projects

Verify that the Run:ai services are running


kubectl get runaiconfig -n runai runai -ojson | jq -r '.status.conditions | map(select(.type == "Available"))'

Verify that all the Run:ai services are available and have all their required resources available.


kubectl get pods -n runai
kubectl get pods -n monitoring

Verify that all pods are in Running status and a ready state (1/1 or similar).


kubectl get deployments -n runai

Check that all deployments are in a ready state (1/1).


kubectl get daemonset -n runai

A Daemonset runs on every node. Some of the Run:ai daemon-sets run on all nodes. Others run only on nodes that contain GPUs. Verify that for all daemonsets the desired number is equal to current and to ready.

Submit a job using the command-line interface

Submitting a Job allows you to verify that the Run:ai scheduling service is running properly.

  1. Make sure that the Project you have created has a quota of at least 1 GPU.
  2. Run:

    runai config project <project-name>
    runai submit -i -g 1
  3. Verify that the Job is in a Running state by running:

    runai list jobs
  4. Verify that the Job is showing in the Jobs area at <company-name>

Submit a job using the user interface

Log into the Run:ai user interface, and verify that you have a Researcher or Research Manager role. Go to the Jobs area. On the top right, press the button to create a Job. Once the form opens, you can submit a Job.

Advanced troubleshooting

Run:ai public ConfigMap

Run:ai services use the runai-public ConfigMap to store information about the cluster status. This ConfigMap can be helpful in troubleshooting issues with Run:ai services. Inspect the ConfigMap by running:

kubectl get cm runai-public -oyaml

Resources not deployed / System unavailable / Reconciliation failed

  1. Run the Preinstall diagnostic script and check for issues.
  2. Run
   kubectl get pods -n runai
   kubectl get pods -n monitoring

Look for any failing pods and check their logs for more information by running kubectl describe pod -n <pod_namespace> <pod_name>.