README.md

JupyterHub

JupyterHub comes with 2 components:

1. jupyterhub
2. notebook-images

JupyterHub

Contains deployment manifests for JupyterHub instance.

Parameters

JupyterHub component comes with 2 parameters exposed vie KFDef.

s3_endpoint_url

HTTP endpoint exposed by your S3 object storage solution which will be made available to JH users in S3_ENDPOINT_URL env variable.

storage_class

Name of the storage class to be used for PVCs created by JupyterHub component. This requires storage-class overlay to be enabled as well to work.

jupyterhub_groups_config

A ConfigMap containing comma separated lists of groups which would be used as Admin and User groups for JupyterHub. The default ConfgiMap can be found here.

jupyterhub_secret

A Secret containing configuration values like JupyterHub DB password or COOKIE_SECRET. The default Secret can be found here.

Examples

  - kustomizeConfig:
      overlays:
      - storage-class
      parameters:
        - name: storage_class
          value: standard
        - name: s3_endpoint_url
          value: "s3.odh.com"
      repoRef:
        name: manifests
        path: jupyterhub/jupyterhub
    name: jupyterhub

Overlays

JupyterHub component comes with 3 overlays.

build

Contains build manifests for JupyterHub images.

storage-class

Customizes JupyterHub to use a specific StorageClass for PVCs, see storage_class parameter.

Notebook Images

Contains manifests for Jupyter notebook images compatible with JupyterHub on OpenShift.

Parameters

Notebook Images do not provide any parameters.

Overlays

Notebook Images component comes with 3 overlays.

additional

Contains additional Jupyter notebook images.

build

Contains build manifests for Jupyter notebook images.

cuda

Contains build chain manifest for CUDA enabled ubi 7 based images, provides tensorflow-gpu enabled notebook image.

NOTE: Builds in this overlay require 4 GB of memory and 4 cpus

cuda-11.0.3

Contains build chain manifest for CUDA 11.0.3 enabled ubi 8 based images with python 3.8 support, provides tensorflow-gpu and pytorch-gpu enabled notebook image.

NOTE: Builds in this overlay require 4-6 GB of memory