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
Kubernetes distribution	The flavor of Kubernetes distribution
Kubernetes version	The version of Kubernetes installed
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
Last connected	Indicates the most recent time the cluster successfully connected to the control plane. If the cluster is currently connected, the value is displayed as Now. If the cluster is disconnected or has experienced issues, the exact timestamp of the last successful connection is displayed.
Network topologies	The network topologies associated with the cluster
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
NVIDIA Run:ai cluster UUID	The unique ID of the cluster

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.

Network Topologies Associated with the Cluster

Click one of the values in the Network topologies column to view the list of network topologies and their parameters.

Column	Description
Topology	The name of the topology
Labels	The ordered set of node label keys that define the topology hierarchy
Created by	The user who created the network topology
Creation time	The timestamp of when the network topology was created

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.

Adding a New Cluster

To add a new cluster, see the installation guide.

Managing Network Topologies

Network topologies optimize placement and accelerate distributed workloads by keeping pods on nodes that are as close to each other as possible in the network. For more details, see Accelerating workloads with network topology-aware scheduling.

To add topologies that represent the cluster's network:

1. Select the cluster you want to add a network topology for

2. In the top action bar, click NETWORK TOPOLOGIES

3. In the Network Topologies Associated with <Cluster Name> modal, click + NETWORK TOPOLOGY

4. Enter a unique name for the topology. If the name already exists, you will be requested to enter a different name.

5. Click + LABEL to add the node label keys that represent the network hierarchy

   • Order labels from farthest (first) to closest (last)

   • Ensure the labels match the corresponding keys on the nodes. For example: cloud.provider.com/topology-block, cloud.provider.com/topology-rack, kubernetes.io/hostname

   • Drag labels to adjust their order if needed

6. Click SAVE NETWORK TOPOLOGY

To delete a topology, click the trash icon next to the topology entry in the modal.

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

   • If the issue persists, contact NVIDIA Run:ai’s support for assistance.

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

   • If the issue persists, contact contact NVIDIA Run:ai’s support for assistance.

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

   • If the issue persists, contact NVIDIA Run:ai’s support for assistance

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

   • If the issue persists, contact NVIDIA Run:ai’s support for assistance