Advanced Topics

This page contains a grab bag of various useful topics that don’t have an easy home elsewhere:

  • Ingress
  • Arbitrary extra code and configuration in

Most people setting up JupyterHubs on popular public clouds should not have to use any of this information, but these topics are essential for more complex installations.


If you are using a Kubernetes Cluster that does not provide public IPs for services directly, you need to use an ingress to get traffic into your JupyterHub. This varies wildly based on how your cluster was set up, which is why this is in the ‘Advanced’ section.

You can enable the required ingress object with the following in your config.yaml

  enabled: true
    - <hostname>

You can specify multiple hosts that should be routed to the hub by listing them under ingress.hosts.

Note that you need to install and configure an Ingress Controller for the ingress object to work.

We recommend the community-maintained nginx ingress controller, kubernetes/ingress-nginx. Note that Nginx maintains two additional ingress controllers. For most use cases, we recommend the community maintained kubernetes/ingress-nginx since that is the ingress controller that the development team has the most experience using.

Ingress and Automatic HTTPS with kube-lego & Let’s Encrypt

When using an ingress object, the default automatic HTTPS support does not work. To have automatic fetch and renewal of HTTPS certificates, you must set it up yourself.

Here’s a method that uses kube-lego to automatically fetch and renew HTTPS certificates from Let’s Encrypt. This approach with kube-lego and Let’s Encrypt currently only works with two ingress controllers: the community-maintained kubernetes/ingress-nginx and google cloud’s ingress controller.

  1. Make sure that DNS is properly set up (configuration depends on the ingress controller you are using and how your cluster was set up). Accessing <hostname> from a browser should route traffic to the hub.

  2. Install & configure kube-lego using the kube-lego helm-chart. Remember to change config.LEGO_EMAIL and config.LEGO_URL at the least.

  3. Add an annotation + TLS config to the ingress so kube-lego knows to get certificates for it:

      annotations: "true"
       - hosts:
          - <hostname>
         secretName: kubelego-tls-jupyterhub

This should provision a certificate, and keep renewing it whenever it gets close to expiry!

Arbitrary extra code and configuration in

Sometimes the various options exposed via the helm-chart’s values.yaml is not enough, and you need to insert arbitrary extra code / config into This is a valuable escape hatch for both prototyping new features that are not yet present in the helm-chart, and also for installation-specific customization that is not suited for upstreaming.

There are four properties you can set in your config.yaml to do this.


The value specified for hub.extraConfig is evaluated as python code at the end of You can do anything here since it is arbitrary Python Code. Some examples of things you can do:

  1. Override various methods in the Spawner / Authenticator by subclassing them. For example, you can use this to pass authentication credentials for the user (such as GitHub OAuth tokens) to the environment. See the JupyterHub docs for an example.
  2. Specify traitlets that take callables as values, allowing dynamic per-user configuration.
  3. Set traitlets for JupyterHub / Spawner / Authenticator that are not currently supported in the helm chart

Unfortunately, you have to write your python in your YAML file. There’s no way to include a file in config.yaml.

You can specify hub.extraConfig as a raw string (remember to use the | for multi-line YAML strings):

  extraConfig: |
    import time
    c.Spawner.environment += {
       "CURRENT_TIME": str(time.time())

You can also specify hub.extraConfig as a dictionary, if you want to logically split your customizations. The code will be evaluated in alphabetical sorted order of the key.

    00-first-config: |
      # some code
    10-second-config: |
      # some other code


This property takes a dictionary of values that are then made available for code in hub.extraConfig to read using a z2jh.get_config function. You can use this to easily separate your code (which goes in hub.extraConfig) from your config (which should go here).

For example, if you use the following snippet in your config.yaml file:

    myString: Hello!
      - Item1
      - Item2
      key: value
    myLongString: |

In your hub.extraConfig,

  1. z2jh.get_config('custom.myString') will return a string "Hello!"
  2. z2jh.get_config('custom.myList') will return a list ["Item1", "Item2"]
  3. z2jh.get_config('custom.myDict') will return a dict {"key": "value"}
  4. z2jh.get_config('custom.myLongString') will return a string "Line1\nLine2"
  5. z2jh.get_config('custom.nonExistent') will return None (since you didn’t specify any value for nonExistent)
  6. z2jh.get_config('custom.myDefault', True) will return True, since that is specified as the second parameter (default)

You need to have a import z2jh at the top of your extraConfig for z2jh.get_config() to work.

Note that the keys in hub.extraConfigMap must be alpha numeric strings starting with a character. Dashes and Underscores are not allowed.


This property takes a dictionary that is set as environment variables in the hub container. You can use this to either pass in additional config to code in your hub.extraConfig or set some hub parameters that are not settable by other means.


A list of extra containers that are bundled alongside the hub container in the same pod. This is a common pattern in kubernetes that as a long list of cool use cases. Some example use cases are:

  1. Database Proxies, which are sometimes required for the hub to talk to its configured database (in Google Cloud) for example
  2. Servers / other daemons that are used by code in your hub.customConfig

The items in this list must be valid kubernetes container specifications.

Picking a Scheduler Strategy

Kubernetes offers very flexible ways to determine how it distributes pods on your nodes. The JupyterHub helm chart supports two common configurations, see below for a brief description of each.


  • Behavior: This spreads user pods across as many nodes as possible.
  • Benefits: A single node going down will not affect too many users. If you do not have explicit memory & cpu limits, this strategy also allows your users the most efficient use of RAM & CPU.
  • Drawbacks: This strategy is less efficient when used with autoscaling.

This is the default strategy. To explicitly specify it, use the following in your config.yaml:

   schedulerStrategy: spread


  • Behavior: This packs user pods into as few nodes as possible.
  • Benefits: This reduces your resource utilization, which is useful in conjunction with autoscalers.
  • Drawbacks: A single node going down might affect more user pods than using a “spread” strategy (depending on the node).

When you use this strategy, you should specify limits and guarantees for memory and cpu. This will make your users’ experience more predictable.

To explicitly specify this strategy, use the following in your config.yaml:

    schedulerStrategy: pack

Pre-pulling Images for Faster Startup

Pulling and building a user’s images forces a user to wait before the user’s server is started. Sometimes, the wait can be 5 to 10 minutes. Pre-pulling the images on all the nodes can cut this wait time to a few seconds. Let’s look at how pre-pulling works.

Pre-pulling basics

With pre-pulling, which is enabled by default, the user’s container image is pulled on all nodes whenever a helm install or helm upgrade is performed. While this causes helm install and helm upgrade to take several minutes, this time makes the user startup experience faster and more pleasant.

With the default pre-pulling setting, a helm install or helm upgrade will cause the system to wait for 5 minutes to begin pulling the images before timing out. This wait time is configurable by passing the --wait <seconds> flag to the helm commands.

We recommend using pre-pulling. For the rare cases where you have a good reason to disable it, pre-pulling can be disabled. To disable the pre-pulling during helm install and helm upgrade, you can use the following snippet in your config.yaml:

     enabled: false

Pre-pulling and changes in cluster size

Cluster size can change through manual addition of nodes or autoscaling. When a new node is added to the cluster, the new node does not yet have the user image. A user using this new node would be forced to wait while the image is pulled from scratch. Ideally, it would be helpful to pre-pull images when the new node is added to the cluster.

By enabling the continuous pre-puller (default state is disabled), the user image will be pre-pulled when adding a new node. When enabled, the continuous pre-puller runs as a daemonset to force Kubernetes to pull the user image on all nodes as soon as a node is present. The continuous pre-puller uses minimal resources on all nodes and greatly speeds up the user pod start time.

The continuous pre-puller is disabled by default. To enable it, use the following snippet in your config.yaml:

    enabled: true

Pre-pulling additional images

By default, the pre-puller only pulls the singleuser image & the networktools image (if access to cloud metadata is disabled). If you have customizations that need additional images present on all nodes, you can ask the pre-puller to also pull an arbitrary number of additional images.

       name: ubuntu
       tag: 16.04
       policy: IfNotPresent

This snippet will pre-pull the ubuntu:16.04 image on all nodes, for example. You can pre-pull any number of images.