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• affinity-options | string

  Default: []

  The Affinity configurations that are selectable by users in the New Notebook UI when creating a Notebook. Input is in JSON/YAML in the format defined by Kubeflow in: https://github.com/kubeflow/kubeflow/blob/master/components/crud-web-apps/jupyter/manifests/base/configs/spawner_ui_config.yaml Each item in the list should have keys: - configKey: an arbitrary key for the configuration - displayName: the name to be shown in the UI - affinity: the affinity configuration, as defined by Kubernetes: https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/

• affinity-options-default | string

  The Affinity options that is selected by default in the New Notebook UI when creating a Notebook. This must be one of the configKey values from the affinity-options config. Leave as an empty string to select no affinity by default

• backend-mode | string

  Default: production

  Which mode out of `development` or `production` the backend server should use

• default-poddefaults | string

  Default: ["access-ml-pipeline"]

  The PodDefaults that are selected by default in the New Notebook UI when creating a new Notebook. Inputs is a JSON/YAML list of the names of the PodDefaults. The New Notebook UI will always show all PodDefaults available to the user - this only defines which PodDefaults are selected by default.

• gpu-number-default | int

  The number of GPUs that are selected by default in the New Notebook UI when creating a Notebook.

• gpu-vendors | string

  Default: [{"limitsKey": "nvidia.com/gpu", "uiName": "NVIDIA"}, {"limitsKey": "amd.com/gpu", "uiName": "AMD"}]

  The GPU vendors that are selectable by users in the New Notebook UI when creating a Notebook. Input is in JSON/YAML in the format defined by Kubeflow in: https://github.com/kubeflow/kubeflow/blob/master/components/crud-web-apps/jupyter/manifests/base/configs/spawner_ui_config.yaml Each item in the list should have keys: - limitsKey: the key that corresponds to the GPU vendor resource in Kubernetes - uiName: the name to be shown in the UI

• gpu-vendors-default | string

  The GPU vendor that is selected by default in the New Notebook UI when creating a Notebook. This must be one of the limitsKey values from the gpu-vendors config. Leave as an empty string to select no GPU vendor by default

• jupyter-images | string

  Default: - kubeflownotebookswg/jupyter-scipy:v1.9.0 - kubeflownotebookswg/jupyter-pytorch-full:v1.9.0 - kubeflownotebookswg/jupyter-pytorch-cuda-full:v1.9.0 - kubeflownotebookswg/jupyter-tensorflow-full:v1.9.0 - kubeflownotebookswg/jupyter-tensorflow-cuda-full:v1.9.0

  list of image options for Jupyter Notebook

• port | int

  Default: 5000

  HTTP port

• rstudio-images | string

  Default: - kubeflownotebookswg/rstudio-tidyverse:v1.9.0

  list of image options for RStudio

• secure-cookies | boolean

  Whether cookies should require HTTPS

• tolerations-options | string

  Default: []

  The Toleration configurations that are selectable by users in the New Notebook UI when creating a Notebook. Input is in JSON/YAML in the format defined by Kubeflow in: https://github.com/kubeflow/kubeflow/blob/master/components/crud-web-apps/jupyter/manifests/base/configs/spawner_ui_config.yaml Each item in the list should have keys: - groupKey: an arbitrary key for the configuration - displayName: the name to be shown in the UI - tolerations: a list of Kubernetes tolerations, as defined in: https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/

• tolerations-options-default | string

  The Tolerations configuration that is selected by default in the New Notebook UI when creating a Notebook. This must be one of the groupKey values from the tolerations-options config. Leave as an empty string to select no tolerations configuration by default

• ui | string

  Default: default

  Which UI to use

• url-prefix | string

  Default: /jupyter

  URL prefix for HTTP requests

• vscode-images | string

  Default: - kubeflownotebookswg/codeserver-python:v1.9.0

  list of image options for VSCode