Sometimes your JupyterHub deployment doesn’t behave the way you’d expect. This section provides some tips on debugging and fixing some common problems.

Debugging commands

In order to debug your JupyterHub deployment, you need to be able to inspect the state of the resources being used. The following are a few common commands for debugging.

Real world scenario: Let’s say you’ve got a JupyterHub deployed, and a user tells you that they are experiencing strange behavior. Let’s take a look at our deployment to figure out what is going on.


For our real world scenario, we’ll assume that our Kubernetes namespace is called jhub. Your namespace may be called something different

kubectl get pod

To list all pods in your Kubernetes deployment:

kubectl --namespace=jhub get pod

This will output a list of all pods being used in the deployment.

Real world scenario: In our case, we see two pods for the JupyterHub infrastructure (hub and proxy) as well as one user pod that was created when somebody logged in to the JupyterHub.

Here’s an example of the output:

$ kubectl --namespace=jhub get pod
NAME                                READY     STATUS         RESTARTS   AGE
hub-3311438805-xnfvp     1/1       Running        0          2m
jupyter-choldgraf                   0/1       ErrImagePull   0          25s
proxy-1227971824-mn2wd   1/1       Running        0          5h

Here we can see the two JupyterHub pods, as well as a single user pod. Note that all user pods will begin with jupyter-.

In particular, keep an eye on the STATUS column. If a given pod contains something other than Running, then something may be wrong.

In this case, we can see that our user’s pod is in the ErrImagePull state. This generally means that there’s something wrong with the Docker image that is defined in singleuser in our helm chart config. Let’s dig further…

kubectl describe pod

To see more detail about the state of a specific pod, use the following command:

kubectl --namespace=jhub describe pod <POD_NAME>

This will output several pieces of information, including configuration and settings for the pod. The final section you’ll see is a list of recent events. These can be particularly informative, as often an error will show up in this section.

Real world scenario: In our case, one of the lines in the events page displays an error:

$ kubectl --namespace=jhub describe pod jupyter-choldgraf
2m            52s             4       kubelet, gke-jhubtest-default-pool-52c36683-jv6r        spec.containers{notebook}       Warning         Failed           Failed to pull image "jupyter/scipy-notebook:v0.4": rpc error: code = 2 desc = Error response from daemon: {"message":"manifest for jupyter/scipy-notebook:v0.4 not found"}

It seems there is indeed something wrong with the Docker image. Let’s confirm this by getting another view on the events that have transpired in the pod.

kubectl logs

If you only want to see the latest logs for a pod, use the following command:

kubectl --namespace=jhub logs <POD_NAME>

This will show you the logs from the pod, which often contain useful information about what is going wrong. Parse these logs to see if something is generating an error.

Real world scenario: In our case, we get this line back:

$ kubectl --namespace=jhub logs jupyter-choldgraf
Error from server (BadRequest): container "notebook" in pod "jupyter-choldgraf" is waiting to start: trying and failing to pull image

Now we are sure that something is wrong with our Dockerfile. Let’s check our config.yaml file for the section where we specify the user’s Docker image. Here we see our problem:

    name: jupyter/scipy-notebook

We haven’t specified a tag for our Docker image! Not specifying a tag will cause it to default to v0.4, which isn’t what we want and is causing the pod to fail.

To fix this, let’s add a tag to our config.yaml file:

    name: jupyter/scipy-notebook
    tag: ae885c0a6226

Then run a helm upgrade:

helm upgrade --cleanup-on-fail jhub jupyterhub/jupyterhub --version=v0.6 -f config.yaml

where jhub is the helm release name (substitute the release name that you chose during setup).


Depending on the size of the Docker image, this may take a while to complete.

Right after you run this command, let’s once again list the pods in our deployment:

$ kubectl --namespace=jhub get pod
NAME                                READY     STATUS              RESTARTS   AGE
hub-2653507799-r7wf8     0/1       ContainerCreating   0          31s
hub-3311438805-xnfvp     1/1       Terminating         0          14m
jupyter-choldgraf                   0/1       ImagePullBackOff    0          12m
proxy-deployment-1227971824-mn2wd   1/1       Running             0          5h

Here we can see one hub pod being destroyed, and another (based on the upgraded helm chart) being created. We also see our broken user pod, which will not be deleted automatically. Let’s manually delete it so a newer working pod can be started.:

$ kubectl --namespace=jhub delete pod jupyter-choldgraf

Finally, we’ll tell our user to log back in to the JupyterHub. Then let’s list our running pods once again:

$ kubectl --namespace=jhub get pod
NAME                                READY     STATUS    RESTARTS   AGE
hub-2653507799-r7wf8     1/1       Running   0          3m
jupyter-choldgraf                   1/1       Running   0          18s
proxy-deployment-1227971824-mn2wd   1/1       Running   0          5h

And now we see that we have a running user pod!

Note that many debugging situations are not as straightforward as this one. It will take some time before you get a feel for the errors that Kubernetes may throw at you, and how these are tied to your configuration files.

Troubleshooting Examples

The following sections contain some case studies that illustrate some of the more common bugs / gotchas that you may experience using JupyterHub with Kubernetes.

Hub fails to start

Symptom: following kubectl get pod, the hub pod is in Error or CrashLoopBackoff state, or appears to be running but accessing the website for the JupyterHub returns an error message in the browser).

Investigating: the output of kubectl --namespace=jhub logs hub... shows something like:

File "/usr/local/lib/python3.5/dist-packages/jupyterhub/", line 589, in get_all_routes
  resp = yield self.api_request('', client=client)
tornado.httpclient.HTTPError: HTTP 403: Forbidden

Diagnosis: This is likely because the hub pod cannot communicate with the proxy pod API, likely because of a problem in the secretToken that was put in config.yaml.

Fix: Follow these steps:

  1. Create a secret token:

    openssl rand -hex 32
  2. Add the token to config.yaml like so:

       secretToken: '<output of `openssl rand -hex 32`>'
  3. Redeploy the helm chart:

    helm upgrade --cleanup-on-fail jhub jupyterhub/jupyterhub -f config.yaml