Installing JupyterHub#

With a Kubernetes cluster available and Helm installed, we can install JupyterHub in the Kubernetes cluster using the JupyterHub Helm chart.

Initialize a Helm chart configuration file#

Helm charts’ contain templates that can be rendered to the Kubernetes resources to be installed. A user of a Helm chart can override the chart’s default values to influence how the templates render.

In this step we will initialize a chart configuration file for you to adjust your installation of JupyterHub. We will name and refer to it as config.yaml going onwards.

Introduction to YAML

If you haven’t worked with YAML before, investing some minutes learning about it will likely be worth your time.

As of version 1.0.0, you don’t need any configuration to get started so you can just create a config.yaml file with some helpful comments.

# This file can update the JupyterHub Helm chart's default configuration values.
# For reference see the configuration reference and default values, but make
# sure to refer to the Helm chart version of interest to you!
# Introduction to YAML:
# Chart config reference:
# Chart default values:
# Available chart versions:

In case you are working from a terminal and are unsure how to create this file, can try with nano config.yaml.

Install JupyterHub#

  1. Make Helm aware of the JupyterHub Helm chart repository so you can install the JupyterHub chart from it without having to use a long URL name.

    helm repo add jupyterhub
    helm repo update

    This should show output like:

    Hang tight while we grab the latest from your chart repositories...
    ...Skip local chart repository
    ...Successfully got an update from the "stable" chart repository
    ...Successfully got an update from the "jupyterhub" chart repository
    Update Complete. ⎈ Happy Helming!⎈
  2. Now install the chart configured by your config.yaml by running this command from the directory that contains your config.yaml:

    helm upgrade --cleanup-on-fail \
      --install <helm-release-name> jupyterhub/jupyterhub \
      --namespace <k8s-namespace> \
      --create-namespace \
      --version=<chart-version> \
      --values config.yaml


    • <helm-release-name> refers to a Helm release name, an identifier used to differentiate chart installations. You need it when you are changing or deleting the configuration of this chart installation. If your Kubernetes cluster will contain multiple JupyterHubs make sure to differentiate them. You can list your Helm releases with helm list.

    • <k8s-namespace> refers to a Kubernetes namespace, an identifier used to group Kubernetes resources, in this case all Kubernetes resources associated with the JupyterHub chart. You’ll need the namespace identifier for performing any commands with kubectl.

    • This step may take a moment, during which time there will be no output to your terminal. JupyterHub is being installed in the background.

    • If you get a release named <helm-release-name> already exists error, then you should delete the release by running helm delete <helm-release-name>. Then reinstall by repeating this step. If it persists, also do kubectl delete namespace <k8s-namespace> and try again.

    • In general, if something goes wrong with the install step, delete the Helm release by running helm delete <helm-release-name> before re-running the install command.

    • If you’re pulling from a large Docker image you may get a Error: timed out waiting for the condition error, add a --timeout=<number-of-minutes>m parameter to the helm command.

    • The --version parameter corresponds to the version of the Helm chart, not the version of JupyterHub. Each version of the JupyterHub Helm chart is paired with a specific version of JupyterHub. E.g., 0.11.1 of the Helm chart runs JupyterHub 1.3.0. For a list of which JupyterHub version is installed in each version of the JupyterHub Helm Chart, see the Helm Chart repository.

  3. While Step 2 is running, you can see the pods being created by entering in a different terminal:

    kubectl get pod --namespace <k8s-namespace>

    To remain sane we recommend that you enable autocompletion for kubectl (follow the kubectl installation instructions for your platform to find the shell autocompletion instructions)

    and set a default value for the --namespace flag:

    kubectl config set-context $(kubectl config current-context) --namespace <k8s-namespace>
  4. Wait for the hub and proxy pod to enter the Running state.

    NAME                    READY     STATUS    RESTARTS   AGE
    hub-5d4ffd57cf-k68z8    1/1       Running   0          37s
    proxy-7cb9bc4cc-9bdlp   1/1       Running   0          37s
  5. Find the IP we can use to access the JupyterHub. Run the following command until the EXTERNAL-IP of the proxy-public service is available like in the example output.

    kubectl --namespace <k8s-namespace> get service proxy-public
    NAME           TYPE           CLUSTER-IP     EXTERNAL-IP     PORT(S)        AGE
    proxy-public   LoadBalancer   80:31916/TCP   1m

    Or, use the short form:

    kubectl --namespace <k8s-namespace> get service proxy-public --output jsonpath='{.status.loadBalancer.ingress[].ip}'
  6. To use JupyterHub, enter the external IP for the proxy-public service in to a browser. JupyterHub is running with a default dummy authenticator so entering any username and password combination will let you enter the hub.

Congratulations! Now that you have basic JupyterHub running, you can extend it and optimize it in many ways to meet your needs.

Some examples of customizations are:

  • Configure the login to use the account that makes sense to you (Google, GitHub, etc.).

  • Use a suitable pre-built image for the user container or build your own.

  • Host it on