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Troubleshooting

Determining the Health of a Run:AI Cluster

To understand whether your Run:AI cluster is healthy you need perform the following verification tests:

  1. All Run:AI services are running.
  2. Data is sent to the cloud.
  3. A job can be submitted.

1. Run:AI services are running

Run:

  kubectl get pods -n runai

Verify that all pods are in Running status.

Run:

  kubectl get deployments -n runai
  kubectl get sts -n runai

Verify that all items (deployments and statefulsets alike) are in a ready state (1/1)

Run:

  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 which contain GPUs. Verify that for all daemon-sets the desired number is equal to current and to ready.

2. Data is sent to the cloud

Log in to https://app.run.ai/dashboards/now

  • Verify that all metrics in the overview dashboard are showing. Specifically the list of nodes and the numeric indicators
  • Go to Projects and create a new project. Find the new project using the CLI command:
     runai list projects
    

3. Submit a job

Submitting a job will allow you to verify that Run:AI scheduling service are in order.

  • Make sure that the project you have created has a quota of at least 1 GPU
  • Run:

     runai config project <project-name>
     runai submit job1 -i gcr.io/run-ai-demo/quickstart -g 1
    
  • Verify that the job is a Running state when running:

     runai list jobs
    
  • Verify that the job is showing in the Jobs area in app.run.ai/jobs

Symptom: Metrics are not showing on Overview Dashboard

Some or all metrics are not showing in https://app.run.ai/dashboards/now

Typical root causes:

  • NVIDIA prerequisites have not been met.
  • Firewall related issues.
  • Internal clock is not synced.

NVIDIA prerequisites have not been met

Run:

  runai pods -n runai | grep nvidia

Select one of the nvidia pods and run:

  kubectl logs -n runai nvidia-device-plugin-daemonset-<id>

If the log contains an error, it means that NVIDIA related prerequisites have not been met. Review step 1 in NVIDIA prerequisites. Verify that:

  • Step 1.1: NVIDIA drivers are installed
  • Step 1.2: NVIDIA Docker is installed. A typical issue here is the installation of the NVIDIA Container Toolkit instead of NVIDIA Docker 2.
  • Step 1.3: Verify that NVIDIA Docker is the default docker runtime
  • If the system has recently been installed, verify that docker has restarted by running the aforementioned pkill command
  • Check the status of Docker by running:
     sudo systemctl status docker
    

Firewall issues

Add verbosity to Prometheus by editing RunaiConfig:

kubectl edit runaiconfig runai -n runai

Add a debug log level:

prometheus-operator:
  prometheus:
    prometheusSpec:
      logLevel: debug

Run:

kubectl logs  prometheus-runai-prometheus-operator-prometheus-0 prometheus \
      -n runai -f --tail 100

Verify that there are no errors. If there are connectivity related errors you may need to:

Clock is not synced

Run: date on cluster nodes and verify that date/time is correct. If not,

  • Set the Linux time service (NTP).
  • Restart Run:AI services. Depending on the previous time gap between servers, you may need to reinstall the Run:AI cluster

Symptom: Projects are not syncing

Create a project on the Admin UI, then run: runai list projects. The new project does not appear.

Typical root cause: The Run:AI agent is not syncing properly. This may be due to:

  • A dependency on the internal Run:AI database. See separate symptom below
  • Firewall issues

Run:

  runai pods -n runai | grep agent

See if the agent is in Running state. Select the agent's full name and run:

  kubectl logs -n runai runai-agent-<id>

Verify that there are no errors. If there are connectivity related errors you may need to:

Symptom: Internal Database has not started

Run:

runai pods -n runai | grep runai-db-0

The status of the Run:AI database is not Running

Typical root causes:

  • More than one default storage class is installed
  • Incompatible NFS version

More than one default storage class is installed

The Run:AI Cluster installation includes, by default, a storage class named local path provisioner which is installed as a default storage class. In some cases, your k8s cluster may already have a default storage class installed. In such cases you should disable the local path provisioner. Having two default storage classes will disable both the internal database and some of the metrics.

Run:

  kubectl get storageclass

And look for default storage classes.

Run:

  kubectl describe pod -n runai runai-db-0

See that there is indeed a storage class error appearing

To disable local path provisioner, run:

  kubectl edit runaiconfig -n runai

Add the following lines under spec:

local-path-provisioner:
      enabled: false

Incompatible NFS version

Default NFS Protocol level is currently 4. If your NFS requires an older version, you may need to add the option as follows. Run:

kubectl edit runaiconfig runai -n runai

Add mountOptions as follows:

nfs-client-provisioner:
  nfs: 
    mountOptions: ["nfsvers=3"]

Adding Verbosity to Database container

Run:

kubectl edit runaiconfig runai -n runai

Under spec, add:

spec:
  postgresql:
    image:
      debug: true

Then view the log by running:

kubectl logs -n runai runa-db-0 

Internal Networking Issues

Run:AI is based on Kubernetes. Kubernetes runs its own internal subnet with a separate DNS service. If you see in the logs that services have trouble connecting, the problem may reside there. You can find further information on how to debug Kubernetes DNS here. Specifically, it is useful to start a Pod with networking utilities and use it for network resolution:

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

Last update: November 29, 2020