bitnamicharts/jupyterhub repository overview

⁠Bitnami Secure Images Helm chart for JupyterHub

JupyterHub brings the power of notebooks to groups of users. It gives users access to computational environments and resources without burdening the users with installation and maintenance tasks.

Overview of JupyterHub⁠

Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

⁠ TL;DR

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/jupyterhub

⁠ Introduction

Bitnami charts for Helm are carefully engineered, actively maintained and are the quickest and easiest way to deploy containers on a Kubernetes cluster that are ready to handle production workloads.

This chart bootstraps a JupyterHub⁠ Deployment in a Kubernetes⁠ cluster using the Helm⁠ package manager.

⁠ Architecture

The JupyterHub chart deploys three basic elements:

• JupyterHub: Central element of the chart. Manages authentication and is responsible for creating the Jupyter Notebook instances (called Single User instances). As a consequence, the Hub requires special RBAC privileges in order to access the Kubernetes API to create and manage Deployments.
• Proxy: This is the external endpoint for users. It manages the communication with the Hub and the Single User instances.
• Image Puller: In order to improve the Single User instance boot time, a DaemonSet object is deployed that pre-pulls all the necessary images to run the Single User Notebooks.

The following diagram shows a deployed release of the chart:

                                                         |
                                                         |
                                                         |
                                                         |
             ------------------                          |
             |                |                          |
             |  Image Puller  |<------Pull images to------
             |                |         all nodes
             ------------------

    -------------           ---------------
    |           |           |             |
    |   Proxy   |---------->|     Hub     |
    |           |           |             |
    -------------           ---------------

After accessing the hub and creating a Single User instance, the deployment looks as follows:

                                                         |
                                                         |
                                                         |
                                                         |
              ----------------                           |
             |                |                          |
             |  Image Puller  |<------Pull images to-----
             |                |         all nodes
              ----------------
    -----------             -------------
   |           |           |             |
   |   Proxy   |---------->|     Hub     |
   |           |           |             |
    -----------             -------------
        |                          |
        |                          |
        |                          |
        |     ---------------      |
        |     | Single User |      |
         ---->|  Instance   |<-----
              ---------------

For more information, check the official JupyterHub documentation⁠.

⁠ Before you begin

• Kubernetes 1.23+
• Helm 3.8.0+
• PV provisioner support in the underlying infrastructure

⁠ Installing the chart

To install the chart with the release name my-release:

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/jupyterhub

These commands deploy JupyterHub on the Kubernetes cluster in the default configuration. The Parameters⁠ section lists the parameters that can be configured during installation.

⁠ Configuration and installation details

This section covers resource requests, configuration, authentication, backup, and other options.

⁠ Resource requests and limits

Bitnami charts allow setting resource requests and limits for all containers inside the chart deployment. These are inside the resources value (check parameter table). Setting requests is essential for production workloads and these should be adapted to your specific use case.

To make this process easier, the chart contains the resourcesPreset values, which automatically sets the resources section according to different presets. Check these presets in the bitnami/common chart⁠. However, in production workloads using resourcesPreset is discouraged as it may not fully adapt to your specific needs. Find more information on container resource management in the official Kubernetes documentation⁠.

⁠ Understand the default configuration

This chart deploys three basic elements:

• JupyterHub: Central element of the chart. Manages authentication and is responsible for creating the Jupyter Notebook instances (called Single User instances). As a consequence, the Hub requires special RBAC privileges in order to access the Kubernetes API to create and manage Deployments.
• Proxy: This is the external endpoint for users. It manages the communication with the Hub and the Single User instances.
• Image Puller: In order to improve the Single User instance boot time, a DaemonSet object is deployed that pre-pulls all the necessary images to run the Single User Notebooks.

The following diagram shows a deployed release of the chart:

                                                         |
                                                         |
                                                         |
                                                         |
             ------------------                          |
             |                |                          |
             |  Image Puller  |<------Pull images to------
             |                |         all nodes
             ------------------

    -------------           ---------------
    |           |           |             |
    |   Proxy   |---------->|     Hub     |
    |           |           |             |
    -------------           ---------------

After accessing the hub and creating a Single User instance, the deployment looks as follows:

                                                         |
                                                         |
                                                         |
                                                         |
              ----------------                           |
             |                |                          |
             |  Image Puller  |<------Pull images to-----
             |                |         all nodes
              ----------------
    -----------             -------------
   |           |           |             |
   |   Proxy   |---------->|     Hub     |
   |           |           |             |
    -----------             -------------
        |                          |
        |                          |
        |                          |
        |     ---------------      |
        |     | Single User |      |
         ---->|  Instance   |<-----
              ---------------

For more information, check the official JupyterHub documentation⁠.

⁠ Prometheus metrics

This chart can be integrated with Prometheus by setting *.metrics.enabled (under the hub and proxy sections) to true. This will expose the JupyterHub native Prometheus ports in the containers, as well as a metrics service. This metrics service will have the necessary annotations to be automatically scraped by Prometheus.

⁠Prometheus requirements

It is necessary to have a working installation of Prometheus or Prometheus Operator for the integration to work. Install the Bitnami Prometheus helm chart⁠ or the Bitnami Kube Prometheus helm chart⁠ to easily have a working Prometheus in your cluster.

⁠Integration with Prometheus Operator

The chart can deploy ServiceMonitor objects for integration with Prometheus Operator installations. To do so, set the value *.metrics.serviceMonitor.enabled=true (under the hub and proxy sections). Ensure that the Prometheus Operator CustomResourceDefinitions are installed in the cluster or it will fail with the following error:

no matches for kind "ServiceMonitor" in version "monitoring.coreos.com/v1"

Install the Bitnami Kube Prometheus helm chart⁠ for having the necessary CRDs and the Prometheus Operator.

⁠ Rolling vs immutable tags⁠

It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image.

Bitnami will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist.

⁠ Configure authentication

The chart configures the Hub DummyAuthenticator⁠ by default, with the password set in the hub.password (auto-generated if not set) chart parameter and user as the administrator user. In order to change the authentication mechanism, change the hub.config.JupyterHub section inside the hub.configuration value.

The following example sets the NativeAuthenticator⁠ authenticator, and configures an admin user called test.

hub:
  configuration: |
    ...
    hub:
      config:
        JupyterHub:
          admin_access: true
          authenticator_class: nativeauthenticator.NativeAuthenticator
          Authenticator:
            admin_users:
              - test
    ...

When deploying, you will need to sign up to set the password for the test user.

For more information on Authenticators, check the official JupyterHub documentation⁠.

⁠ Update credentials

The Bitnami JupyterHub chart, when upgrading, reuses the secret previously rendered by the chart or the one specified in hub.existingSecret. To update credentials, use one of the following:

• Run helm upgrade specifying a new password in hub.configuration in the proper authentication section⁠
• Run helm upgrade specifying a new secret in hub.existingSecret

⁠ Configure the Single User instances

As explained in this section⁠, the Hub is responsible for deploying the Single User instances. The configuration of these instances is passed to the Hub instance via the hub.configuration chart parameter.

In order to make the chart follow standards and to ease the generation of this configuration file, the chart has a singleuser section, which is then used for generating the hub.configuration value. This value can be easily overridden by modifying its default value or by providing a secret using the hub.existingSecret value. In this case, all the settings in the singleuser section will be ignored.

All the settings specified in the hub.configuration value are consumed by the jupyter_config.py script available in the templates/hub/configmap.yaml⁠ file. This script can be changed by providing a custom ConfigMap using the hub.existingConfigmap value. The official JupyterHub documentation⁠ has more examples of the jupyter_config.py script.

⁠ Backup and restore

To back up and restore Helm chart deployments on Kubernetes, you need to back up the persistent volumes from the source deployment and attach them to a new deployment using Velero⁠, a Kubernetes backup/restore tool. Find the instructions for using Velero in this guide⁠.

⁠ Restrict traffic using NetworkPolicies

The Bitnami JupyterHub chart enables NetworkPolicies by default. This restricts the communication between the three main components: the Proxy, the Hub and the Single User instances. There are two elements that were left open on purpose:

• Ingress access to the Proxy instance HTTP port: by default, it is open to any IP, as it is the entry point to the JupyterHub instance. This behavior can be changed by tweaking the proxy.networkPolicy.extraIngress value.
• Hub egress access: As the Hub requires access to the Kubernetes API, the Hub can access to any IP by default (depending on the Kubernetes platform, the Service IP ranges can vary and so there is no easy way to detect the Kubernetes API internal IP). This behavior can be changed by tweaking the hub.networkPolicy.extraEgress value.

⁠ Use sidecars and init containers

If additional containers are needed in the same pod (such as additional metrics or logging exporters), they can be defined using the proxy.sidecars, hub.sidecars or singleuser.sidecars config parameters.

sidecars:
- name: your-image-name
  image: your-image
  imagePullPolicy: Always
  ports:
  - name: portname
    containerPort: 1234

If these sidecars export extra ports, extra port definitions can be added using the service.extraPorts parameter (where available), as shown in the following example:

service:
  extraPorts:
  - name: extraPort
    port: 11311
    targetPort: 11311

Similarly, extra init containers can be added using the hub.initContainers, proxy.initContainers and singleuser.initContainers parameters.

initContainers:
  - name: your-image-name
    image: your-image
    imagePullPolicy: Always
    ports:
      - name: portname
        containerPort: 1234

Learn more about sidecar containers⁠ and init containers⁠.

⁠ Gateway API

This chart provides support for exposing JupyterHub using the Gateway API⁠ and its HTTPRoute resource. If you have a Gateway controller installed on your cluster, such as APISIX, Contour, Envoy Gateway, NGINX Gateway Fabric or Kong Ingress Controller you can utilize the Gateway controller to serve your application. To enable Gateway API integration, set proxy.httpRoute.enabled to true. The Gateway to be used can be customized by setting the proxy.httpRoute.parentRefs parameter. By default, it will reference a Gateway named gateway in the same namespace as the release.

You can specify the list of hostnames to be mapped to the deployment using the proxy.httpRoute.hostnames parameter. Additionally, you can customize the rules used to route the traffic to the service by modifying the proxy.httpRoute.matches and proxy.httpRoute.filters parameters or adding new rules using the proxy.httpRoute.extraRules parameter.

This chart also supports creating a BackendTLSPolicy to define the SNI the Gateway should use to connect to the JupyterHub backend pods and how the certificate served by these pods should be verified. To do so, set the proxy.backendTLSPolicy.enabled parameter to true. Please note it's required to secure traffic using HTTPS between the Gateway and the backend pods by setting proxy.tls.enabled to true.

⁠ Ingress

This chart provides support for Ingress resources. If you have an ingress controller installed on your cluster, such as nginx-ingress-controller⁠ or contour⁠ you can utilize the ingress controller to serve your application. To enable Ingress integration, set proxy.ingress.enabled to true.

The most common scenario is to have one host name mapped to the deployment. In this case, the proxy.ingress.hostname property can be used to set the host name. The proxy.ingress.tls parameter can be used to add the TLS configuration for this host.

However, it is also possible to have more than one host. To facilitate this, the proxy.ingress.extraHosts parameter (if available) can be set with the host names specified as an array. The proxy.ingress.extraTLS parameter (if available) can also be used to add the TLS configuration for extra hosts.

Adding the TLS parameter (where available) will cause the chart to generate HTTPS URLs, and the application will be available on port 443. The actual TLS secrets do not have to be generated by this chart. However, if TLS is enabled, the Ingress record will not work until the TLS secret exists.

Learn more about Ingress controllers⁠.

⁠ Configure TLS secrets

This chart facilitates the creation of TLS secrets for use with the Ingress controller (although this is not mandatory). There are several common use cases:

• Generate certificate secrets based on chart parameters.
• Enable externally generated certificates.
• Manage application certificates using an external service (like cert-manager⁠).
• Create self-signed certificates within the chart (if supported).

In the first two cases, a certificate and a key are needed. Files are expected in .pem format.

Here is an example of a certificate file:

-----BEGIN CERTIFICATE-----
MIID6TCCAtGgAwIBAgIJAIaCwivkeB5EMA0GCSqGSIb3DQEBCwUAMFYxCzAJBgNV
...
jScrvkiBO65F46KioCL9h5tDvomdU1aqpI/CBzhvZn1c0ZTf87tGQR8NK7v7
-----END CERTIFICATE-----

Here is an example of a certificate key:

-----BEGIN RSA PRIVATE KEY-----
MIIEogIBAAKCAQEAvLYcyu8f3skuRyUgeeNpeDvYBCDcgq+LsWap6zbX5f8oLqp4
...
wrj2wDbCDCFmfqnSJ+dKI3vFLlEz44sAV8jX/kd4Y6ZTQhlLbYc=
-----END RSA PRIVATE KEY-----

• If using Helm to manage the certificates based on the parameters, copy these values into the certificate and key values for a given *.ingress.secrets entry.
• If managing TLS secrets separately, it is necessary to create a TLS secret with name INGRESS_HOSTNAME-tls (where INGRESS_HOSTNAME is a placeholder to be replaced with the host name you set using the *.ingress.hostname parameter).
• If your cluster has a cert-manager⁠ add-on to automate the management and issuance of TLS certificates, add to *.ingress.annotations the corresponding ones⁠ for cert-manager.
• If using self-signed certificates created by Helm, set both *.ingress.tls and *.ingress.selfSigned to true.

⁠ Set pod affinity

This chart allows you to set your custom affinity using the *.affinity parameter(s).

As an alternative, you can use the preset configurations for pod affinity, pod anti-affinity, and node affinity available in the bitnami/common chart. To do so, set the *.podAffinityPreset, *.podAntiAffinityPreset, or *.nodeAffinityPreset parameters.

Learn more about pod affinity⁠.

⁠ Deploy extra resources

There are cases where you may want to deploy extra objects, such a ConfigMap containing your app's configuration or some extra deployment with a micro service used by your app. For covering this case, the chart allows adding the full specification of other objects using the extraDeploy parameter.

⁠ FIPS parameters

The FIPS parameters only have effect if you are using images from the Bitnami Secure Images catalog⁠.

For more information on this new support, see the FIPS Compliance section⁠.

⁠ Parameters

The following subsections list global, common, and component-specific parameters.

⁠ Global parameters

Note: the README for this chart is longer than the DockerHub length limit of 25000, so it has been trimmed. The full README can be found at https://techdocs.broadcom.com/us/en/vmware-tanzu/bitnami-secure-images/bitnami-secure-images/services/bsi-app-doc/apps-charts-jupyterhub-index.html⁠