Contact support

Clusters

This section explains the procedure to view and manage Clusters.

The Cluster table provides a quick and easy way to see the status of your cluster.

Clusters table

The Clusters table can be found under Resources in the NVIDIA Run:ai platform.

The clusters table provides a list of the clusters added to NVIDIA Run:ai platform, along with their status.

The clusters table consists of the following columns:

Column
Description

Cluster

The name of the cluster

Status

The status of the cluster. For more information see the table below. Hover over the information icon for a short description and links to troubleshooting

Creation time

The timestamp when the cluster was created

URL

The URL that was given to the cluster

NVIDIA Run:ai cluster version

The NVIDIA Run:ai version installed on the cluster

Kubernetes distribution

The flavor of Kubernetes distribution

Kubernetes version

The version of Kubernetes installed

NVIDIA Run:ai cluster UUID

The unique ID of the cluster

Customizing the table view

  • Filter - Click ADD FILTER, select the column to filter by, and enter the filter values

  • Search - Click SEARCH and type the value to search by

  • Sort - Click each column header to sort by

  • Column selection - Click COLUMNS and select the columns to display in the table

  • Download table - Click MORE and then Click Download as CSV. Export to CSV is limited to 20,000 rows.

Cluster status

Status
Description

Waiting to connect

The cluster has never been connected.

Disconnected

There is no communication from the cluster to the Control plane. This may be due to a network issue. See troubleshooting scenarios.

Missing prerequisites

Some prerequisites are missing from the cluster. As a result, some features may be impacted. See troubleshooting scenarios.

Service issues

At least one of the services is not working properly. You can view the list of nonfunctioning services for more information. See troubleshooting scenarios.

Connected

The NVIDIA Run:ai cluster is connected, and all NVIDIA Run:ai services are running.

Adding a new cluster

To add a new cluster, see the installation guide.

Removing a cluster

  1. Select the cluster you want to remove

  2. Click REMOVE

  3. A dialog appears: Make sure to carefully read the message before removing

  4. Click REMOVE to confirm the removal.

Using the API

Go to the Clusters API reference to view the available actions

Troubleshooting

Before starting, make sure you have access to the Kubernetes cluster where NVIDIA Run:ai is deployed with the necessary permissions

Troubleshooting scenarios

Cluster disconnected

Description: When the cluster's status is ‘disconnected’, there is no communication from the cluster services reaching the NVIDIA Run:ai Platform. This may be due to networking issues or issues with NVIDIA Run:ai services.

Mitigation:

  1. Check NVIDIA Run:ai’s services status:

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to view pods

    3. Copy and paste the following command to verify that NVIDIA Run:ai’s services are running:

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

    4. If any of the services are not running, see the ‘cluster has service issues’ scenario.

  2. Check the network connection

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to create pods

    3. Copy and paste the following command to create a connectivity check pod:

      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"'

    4. Replace <control-plane-endpoint> with the URL of the Control Plane in your environment. If the pod fails to connect to the Control Plane, check for potential network policies

  3. Check and modify the network policies

    1. Open your terminal

    2. Copy and paste the following command to check the existence of network policies:

      kubectl get networkpolicies -n runai

    3. Review the policies to ensure that they allow traffic from the NVIDIA Run:ai namespace to the Control Plane. If necessary, update the policies to allow the required traffic

      Example of allowing traffic:

      apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-control-plane-traffic
  namespace: runai
spec:
  podSelector:
    matchLabels:
      app: runai
  policyTypes:
    - Ingress
    - Egress
  egress:
    - to:
        - ipBlock:
            cidr: <control-plane-ip-range>
      ports:
        - protocol: TCP
          port: <control-plane-port>
  ingress:
    - from:
        - ipBlock:
            cidr: <control-plane-ip-range>
      ports:
        - protocol: TCP
          port: <control-plane-port>

    4. Check infrastructure-level configurations:

      • Ensure that firewall rules and security groups allow traffic between your Kubernetes cluster and the Control Plane

      • Verify required ports and protocols:

        • Ensure that the necessary ports and protocols for NVIDIA Run:ai’s services are not blocked by any firewalls or security groups

  4. Check NVIDIA Run:ai services logs

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to view logs

    3. Copy and paste the following commands to view the logs of the NVIDIA Run:ai services:

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

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

  5. Diagnosing internal network issues: NVIDIA Run:ai operates on Kubernetes, which uses its internal subnet and DNS services for communication between pods and services. If you find connectivity issues in the logs, the problem might be related to Kubernetes' internal networking.

    To diagnose DNS or connectivity issues, you can start a debugging {{glossary.Pod}} with networking utilities:

    1. Copy the following command to your terminal, to start a pod with networking tools:

      kubectl run -i --tty netutils --image=dersimn/netutils -- bash

      This command creates an interactive pod (netutils) where you can use networking commands like ping, curl, nslookup, etc., to troubleshoot network issues.

    2. Use this pod to perform network resolution tests and other diagnostics to identify any DNS or connectivity problems within your Kubernetes {{glossary.Cluster}}.

  6. Contact NVIDIA Run:ai’s support

Cluster has service issues

Description: When a cluster's status is ‘Has service issues`, it means that one or more NVIDIA Run:ai services running in the cluster are not available.

Mitigation:

  1. Verify non-functioning services

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to view the runaiconfig resource

    3. Copy and paste the following command to determine which services are not functioning:

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

  2. Check for Kubernetes events

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to view events

    3. Copy and paste the following command to get all Kubernetes events:

      kubectl get events -A

  3. Inspect resource details

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to describe resources

    3. Copy and paste the following command to check the details of the required resource:

      kubectl describe <resource_type> <name>

  4. Contact NVIDIA Run:ai’s Support

Cluster is waiting to connect

Description: When the cluster's status is ‘waiting to connect’, it means that no communication from the cluster services reaches the NVIDIA Run:ai Platform. This may be due to networking issues or issues with NVIDIA Run:ai services.

Mitigation:

  1. Check NVIDIA Run:ai’s services status

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to view pods

    3. Copy and paste the following command to verify that NVIDIA Run:ai’s services are running:

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

    4. If any of the services are not running, see the ‘cluster has service issues’ scenario.

  2. Check the network connection

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to create pods

    3. Copy and paste the following command to create a connectivity check pod:

      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"'

    4. Replace <control-plane-endpoint> with the URL of the Control Plane in your environment. If the pod fails to connect to the Control Plane, check for potential network policies:

  3. Check and modify the network policies

    1. Open your terminal

    2. Copy and paste the following command to check the existence of network policies:

      kubectl get networkpolicies -n runai

    3. Review the policies to ensure that they allow traffic from the NVIDIA Run:ai namespace to the Control Plane. If necessary, update the policies to allow the required traffic

    4. Example of allowing traffic:

      apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-control-plane-traffic
  namespace: runai
spec:
  podSelector:
    matchLabels:
      app: runai
  policyTypes:
    - Ingress
    - Egress
  egress:
    - to:
        - ipBlock:
            cidr: <control-plane-ip-range>
      ports:
        - protocol: TCP
          port: <control-plane-port>
  ingress:
    - from:
        - ipBlock:
            cidr: <control-plane-ip-range>
      ports:
        - protocol: TCP
          port: <control-plane-port>

    5. Check infrastructure-level configurations:

    6. Ensure that firewall rules and security groups allow traffic between your Kubernetes cluster and the Control Plane

    7. Verify required ports and protocols:

      • Ensure that the necessary ports and protocols for NVIDIA Run:ai’s services are not blocked by any firewalls or security groups

  4. Check NVIDIA Run:ai services logs

    1. Open your terminal

    2. Make sure you have access to the Kubernetes cluster with permissions to view logs

    3. Copy and paste the following commands to view the logs of the NVIDIA Run:ai services:

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

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

  5. Contact NVIDIA Run:ai’s support

Cluster is missing prerequisites

Description: 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, NVIDIA Run:ai services may not function properly.

Mitigation:

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 NVIDIA Run:ai platform for further details regarding the missing prerequisites.

  2. Inspect the runai-public ConfigMap:

    1. Open your terminal. In the terminal, type the following command to list all ConfigMaps in the runai-public namespace:

      kubectl get configmap -n runai-public

  3. Describe the ConfigMap

    1. Locate the ConfigMap named runai-public from the list

    2. To view the detailed contents of this ConfigMap, type the following command:

      kubectl describe configmap runai-public -n runai-public

  4. Find Missing Prerequisites

    1. In the output displayed, look for a section labeled dependencies.required

    2. This section provides detailed information about any missing resources or prerequisites. Review this information to identify what is needed

  5. Contact NVIDIA Run:ai’s support

PreviousNVIDIA Run:ai system monitoringNextShared storage

Last updated