403 Forbidden Error when using Azure AD for authentication

[npc2kor]

We are hosting Jupyterhub on an AKS instance, using helm charts and using Azure AD to authenticate the org users. The App Registration on Azure is properly configured, with API Permissions - user.read, openid, profile, email granted.

However, I am getting a 403: Forbidden error, when signing in using Azure AD credentials. Herewith attaching the error as well as the values.yaml configuration for your reference.

Error from the pod logs:

[I 2022-11-14 10:49:46.106 JupyterHub log:186] 302 GET / -> /hub/ (@::ffff:10.0.96.4) 0.84ms
[I 2022-11-14 10:49:46.330 JupyterHub log:186] 302 GET /hub/ -> /hub/login?next=%2Fhub%2F (@::ffff:10.0.96.4) 0.85ms
[I 2022-11-14 10:49:58.758 JupyterHub log:186] 302 GET / -> /hub/ (@::ffff:10.0.96.7) 0.70ms
[I 2022-11-14 10:49:58.980 JupyterHub log:186] 302 GET /hub/ -> /hub/login?next=%2Fhub%2F (@::ffff:10.0.96.7) 0.72ms
[I 2022-11-14 10:50:01.681 JupyterHub oauth2:102] OAuth redirect: 'https://<REDACTED HOSTNAME>/hub/oauth_callback'
[I 2022-11-14 10:50:01.682 JupyterHub log:186] 302 GET /hub/oauth_login?next=%2Fhub%2F -> https://login.microsoftonline.com/<REDACTED>/oauth2/authorize?response_type=code&redirect_uri=https%3A%2F%2Fnotebook-dev.datadriven-sensors.bosch.tech%2Fhub%2Foauth_callback&client_id=<REDACTED>&state=[secret]&scope=openid+profile+email (@::ffff:10.0.96.4) 1.34ms
[W 2022-11-14 10:50:03.850 JupyterHub auth:508] Disallowing invalid username 'redacted-username'.
[W 2022-11-14 10:50:03.850 JupyterHub base:816] Failed login for unknown user
[W 2022-11-14 10:50:03.850 JupyterHub web:1796] 403 GET /hub/oauth_callback?code=0.ASEAGR7lCsgHS067bWSO5YQQ9JqjaZdVgf1ItZysGztzbqIhAOQ.AgABAAIAAAD--DLA3VO7QrddgJg7WevrAgDs_wQA9P-DehWwRzPOvcYaBoxK-OmYG6crauECr5TnaZwZhyx1F6PMYWPfroW_-gK_xMrUZ0SHh9zyhznfrdcf8Lcybxj9kXddd9FwO-v_NtdGM6e-2xc2Gf2FOg68Y75ngRwHB0RSZgvZSSsbXaofPsnNSpQFDfX4LL-otcU6SxrAGOZ3FCvpx2EY2s7ApCHAKE6HV3YBgRQJSMnc5R6WR0o7PDQ4VilYJ7-NMmTZZyJQQuoqYWSqy1fplry4XK2WP35TJSWME6T3cugXz7-x6KVFj3cQaLClUDUkAD9QZiD-FH1Qg0rU1Hp8oMskMesRqmSCQUmsSLe-h6UeB9fGA6qcXUfyCuSESbTbofHX4vsRoN2_FfA99JBg0rdpnqOQ8fzhqUolE-B7Lo7cWnbjgj1Mt82N-ueq2vRuw4T3B4N9yS6i2Gz2GhUiCkVK2FmnRyR-tRsCR6TG9zAnZPthTTkwE-O68KggEl46XnVviXNm-zSoXgwDKocnvJLtl25mntVW758CZV4C2U9jrqXkEQoj_mSDmy8gCbeWdipiWgY17IL6Bb_TG-8eoPfrY5EyGJxBK8YOVP2uXVsAOzbOiEv-xdnuHdD7KHLCGI1IXINtc5Rul3e0VN-zd6hWUvhuFAp5LSPT56DjNVS1H9hX2KK79tmrBDBjljxfKlvhQ4-Ei6SQca6-x2gLxkRbDP8-53FWb4N4Gwb8C-8jRXGqUF_4rWvsKI7e0o3in7yBxxEsHJ-HUg5Lo_BP5KSYJyMzo4oJyiLQUXrwE7HWiEZjrKddpDGZ7boAJUG3vkRcY3O5rehylcDdt4igMG14AwziRBS5dIS3-36XwN952uawSFFC717SCEMe7_yvEMhwvGSdVmLb1WmIS_5j8UQYp6ImE1pZpkM_MC6QO91zkVbvNh6haC8mrFAUxZ7l5p80&state=eyJzdGF0ZV9pZCI6ICIwYjBkMjljNjM4Zjc0ZTI5YTFlODQ1NDA2YWFlMDFjNyIsICJuZXh0X3VybCI6ICIvaHViLyJ9&session_state=8f2e33a3-37e4-4fee-b1df-3c7874fb94ab (::ffff:10.0.96.4): Sorry, you are not currently authorized to use this hub. Please contact the hub administrator.
[W 2022-11-14 10:50:03.852 JupyterHub log:186] 403 GET /hub/oauth_callback?code=[secret]&state=[secret]&session_state=[secret] (@::ffff:10.0.96.4) 216.30ms

Values.yaml configuration

# fullnameOverride and nameOverride distinguishes blank strings, null values,
# and non-blank strings. For more details, see the configuration reference.
fullnameOverride: ""
nameOverride:

# custom can contain anything you want to pass to the hub pod, as all passed
# Helm template values will be made available there.
custom: {}

# imagePullSecret is configuration to create a k8s Secret that Helm chart's pods
# can get credentials from to pull their images.
imagePullSecret:
  create: false
  automaticReferenceInjection: true
  registry:
  username:
  password:
  email:
# imagePullSecrets is configuration to reference the k8s Secret resources the
# Helm chart's pods can get credentials from to pull their images.
imagePullSecrets: 
 - jhub-secret-acr-sp

# hub relates to the hub pod, responsible for running JupyterHub, its configured
# Authenticator class KubeSpawner, and its configured Proxy class
# ConfigurableHTTPProxy. KubeSpawner creates the user pods, and
# ConfigurableHTTPProxy speaks with the actual ConfigurableHTTPProxy server in
# the proxy pod.
hub:
  revisionHistoryLimit:
  config:
    AzureAdOAuthenticator:
      enable_auth_state: true
      client_id: <REDACTED>
      client_secret: <REDACTED>
      oauth_callback_url: https://hostname/hub/oauth_callback
      tenant_id: <REDACTED>
      scope:
        - openid
        - profile
        - email
    JupyterHub:
      admin_access: false
      authenticator_class: azuread  
  service:
    type: ClusterIP
    annotations: {}
    ports:
      nodePort:
    extraPorts: []
    loadBalancerIP:
  baseUrl: /
  cookieSecret:
  initContainers: []
  nodeSelector: 
    kubernetes.azure.com/agentpool: defaultpool
  tolerations: []
  concurrentSpawnLimit: 64
  consecutiveFailureLimit: 5
  activeServerLimit:
  deploymentStrategy:
    ## type: Recreate
    ## - sqlite-pvc backed hubs require the Recreate deployment strategy as a
    ##   typical PVC storage can only be bound to one pod at the time.
    ## - JupyterHub isn't designed to support being run in parallell. More work
    ##   needs to be done in JupyterHub itself for a fully highly available (HA)
    ##   deployment of JupyterHub on k8s is to be possible.
    type: Recreate
  db:
    type: sqlite-pvc
    upgrade:
    pvc:
      annotations: {}
      selector: {}
      accessModes:
        - ReadWriteOnce
      storage: 1Gi
      subPath:
      storageClassName:
    url:
    password:
  labels: {}
  annotations: {}
  command: []
  args: []
  extraConfig: 
    auth.py: |
      import os
      from oauthenticator.azuread import AzureAdOAuthenticator
      c.JupyterHub.authenticator_class = AzureAdOAuthenticator

      c.Application.log_level = 'DEBUG'

      c.AzureAdOAuthenticator.tenant_id = '<REDACTED>'

      c.AzureAdOAuthenticator.oauth_callback_url = 'https://hostname/hub/oauth_callback'
      c.AzureAdOAuthenticator.client_id = '<REDACTED>'
      c.AzureAdOAuthenticator.client_secret = '<REDACTED>'
      c.AzureAdOAuthenticator.scope = ['openid', 'profile', 'email']
  extraFiles: {}
  extraEnv: {}
  extraContainers: []
  extraVolumes: []
  extraVolumeMounts: []
  image:
    name: jupyterhub/k8s-hub
    tag: "2.0.0"
    pullPolicy:
    pullSecrets: []
  resources: {}
  podSecurityContext:
    fsGroup: 1000
  containerSecurityContext:
    runAsUser: 1000
    runAsGroup: 1000
    allowPrivilegeEscalation: false
  lifecycle: {}
  loadRoles: {}
  services: {}
  pdb:
    enabled: false
    maxUnavailable:
    minAvailable: 1
  networkPolicy:
    enabled: true
    ingress: []
    egress: []
    egressAllowRules:
      cloudMetadataServer: true
      dnsPortsPrivateIPs: true
      nonPrivateIPs: true
      privateIPs: true
    interNamespaceAccessLabels: ignore
    allowedIngressPorts: []
  allowNamedServers: false
  namedServerLimitPerUser:
  authenticatePrometheus:
  redirectToServer:
  shutdownOnLogout:
  templatePaths: []
  templateVars: {}
  livenessProbe:
    # The livenessProbe's aim to give JupyterHub sufficient time to startup but
    # be able to restart if it becomes unresponsive for ~5 min.
    enabled: true
    initialDelaySeconds: 300
    periodSeconds: 10
    failureThreshold: 30
    timeoutSeconds: 3
  readinessProbe:
    # The readinessProbe's aim is to provide a successful startup indication,
    # but following that never become unready before its livenessProbe fail and
    # restarts it if needed. To become unready following startup serves no
    # purpose as there are no other pod to fallback to in our non-HA deployment.
    enabled: true
    initialDelaySeconds: 0
    periodSeconds: 2
    failureThreshold: 1000
    timeoutSeconds: 1
  existingSecret:
  serviceAccount:
    create: true
    name:
    annotations: {}
  extraPodSpec: {}

rbac:
  create: true

# proxy relates to the proxy pod, the proxy-public service, and the autohttps
# pod and proxy-http service.
proxy:
  secretToken:
  annotations: {}
  deploymentStrategy:
    ## type: Recreate
    ## - JupyterHub's interaction with the CHP proxy becomes a lot more robust
    ##   with this configuration. To understand this, consider that JupyterHub
    ##   during startup will interact a lot with the k8s service to reach a
    ##   ready proxy pod. If the hub pod during a helm upgrade is restarting
    ##   directly while the proxy pod is making a rolling upgrade, the hub pod
    ##   could end up running a sequence of interactions with the old proxy pod
    ##   and finishing up the sequence of interactions with the new proxy pod.
    ##   As CHP proxy pods carry individual state this is very error prone. One
    ##   outcome when not using Recreate as a strategy has been that user pods
    ##   have been deleted by the hub pod because it considered them unreachable
    ##   as it only configured the old proxy pod but not the new before trying
    ##   to reach them.
    type: Recreate
    ## rollingUpdate:
    ## - WARNING:
    ##   This is required to be set explicitly blank! Without it being
    ##   explicitly blank, k8s will let eventual old values under rollingUpdate
    ##   remain and then the Deployment becomes invalid and a helm upgrade would
    ##   fail with an error like this:
    ##
    ##     UPGRADE FAILED
    ##     Error: Deployment.apps "proxy" is invalid: spec.strategy.rollingUpdate: Forbidden: may not be specified when strategy `type` is 'Recreate'
    ##     Error: UPGRADE FAILED: Deployment.apps "proxy" is invalid: spec.strategy.rollingUpdate: Forbidden: may not be specified when strategy `type` is 'Recreate'
    rollingUpdate:
  # service relates to the proxy-public service
  service:
    type: ClusterIP
    labels: {}
    annotations: {}
    nodePorts:
      http:
      https:
    disableHttpPort: false
    extraPorts: []
    loadBalancerIP:
    loadBalancerSourceRanges: []
  # chp relates to the proxy pod, which is responsible for routing traffic based
  # on dynamic configuration sent from JupyterHub to CHP's REST API.
  chp:
    revisionHistoryLimit:
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    image:
      name: jupyterhub/configurable-http-proxy
      # tag is automatically bumped to new patch versions by the
      # watch-dependencies.yaml workflow.
      #
      tag: "4.5.3" # https://github.com/jupyterhub/configurable-http-proxy/releases
      pullPolicy:
      pullSecrets: []
    extraCommandLineFlags: []
    livenessProbe:
      enabled: true
      initialDelaySeconds: 60
      periodSeconds: 10
      failureThreshold: 30
      timeoutSeconds: 3
    readinessProbe:
      enabled: true
      initialDelaySeconds: 0
      periodSeconds: 2
      failureThreshold: 1000
      timeoutSeconds: 1
    resources: {}
    defaultTarget:
    errorTarget:
    extraEnv: {}
    nodeSelector: 
      kubernetes.azure.com/agentpool: defaultpool
    tolerations: []
    networkPolicy:
      enabled: true
      ingress: []
      egress: []
      egressAllowRules:
        cloudMetadataServer: true
        dnsPortsPrivateIPs: true
        nonPrivateIPs: true
        privateIPs: true
      interNamespaceAccessLabels: ignore
      allowedIngressPorts: [http, https]
    pdb:
      enabled: false
      maxUnavailable:
      minAvailable: 1
    extraPodSpec: {}
  # traefik relates to the autohttps pod, which is responsible for TLS
  # termination when proxy.https.type=letsencrypt.
  traefik:
    revisionHistoryLimit:
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    image:
      name: traefik
      # tag is automatically bumped to new patch versions by the
      # watch-dependencies.yaml workflow.
      #
      tag: "v2.8.4" # ref: https://hub.docker.com/_/traefik?tab=tags
      pullPolicy:
      pullSecrets: []
    hsts:
      includeSubdomains: false
      preload: false
      maxAge: 15724800 # About 6 months
    resources: {}
    labels: {}
    extraInitContainers: []
    extraEnv: {}
    extraVolumes: []
    extraVolumeMounts: []
    extraStaticConfig: {}
    extraDynamicConfig: {}
    nodeSelector: 
      kubernetes.azure.com/agentpool: defaultpool
    tolerations: []
    extraPorts: []
    networkPolicy:
      enabled: true
      ingress: []
      egress: []
      egressAllowRules:
        cloudMetadataServer: true
        dnsPortsPrivateIPs: true
        nonPrivateIPs: true
        privateIPs: true
      interNamespaceAccessLabels: ignore
      allowedIngressPorts: [http, https]
    pdb:
      enabled: false
      maxUnavailable:
      minAvailable: 1
    serviceAccount:
      create: true
      name:
      annotations: {}
    extraPodSpec: {}
  secretSync:
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    image:
      name: jupyterhub/k8s-secret-sync
      tag: "2.0.0"
      pullPolicy:
      pullSecrets: []
    resources: {}
  labels: {}
  https:
    enabled: false
    type: letsencrypt
    #type: letsencrypt, manual, offload, secret
    letsencrypt:
      contactEmail:
      # Specify custom server here (https://acme-staging-v02.api.letsencrypt.org/directory) to hit staging LE
      acmeServer: https://acme-v02.api.letsencrypt.org/directory
    manual:
      key:
      cert:
    secret:
      name:
      key: tls.key
      crt: tls.crt
    hosts: []

# singleuser relates to the configuration of KubeSpawner which runs in the hub
# pod, and its spawning of user pods such as jupyter-myusername.
singleuser:
  podNameTemplate:
  extraTolerations: []
  nodeSelector: 
    kubernetes.azure.com/agentpool: defaultpool
  extraNodeAffinity:
    required: []
    preferred: []
  extraPodAffinity:
    required: []
    preferred: []
  extraPodAntiAffinity:
    required: []
    preferred: []
  networkTools:
    image:
      name: jupyterhub/k8s-network-tools
      tag: "2.0.0"
      pullPolicy:
      pullSecrets: []
    resources: {}
  cloudMetadata:
    # block set to true will append a privileged initContainer using the
    # iptables to block the sensitive metadata server at the provided ip.
    blockWithIptables: true
    ip: 169.254.169.254
  networkPolicy:
    enabled: true
    ingress: []
    egress: []
    egressAllowRules:
      cloudMetadataServer: false
      dnsPortsPrivateIPs: true
      nonPrivateIPs: true
      privateIPs: false
    interNamespaceAccessLabels: ignore
    allowedIngressPorts: []
  events: true
  extraAnnotations: {}
  extraLabels:
    hub.jupyter.org/network-access-hub: "true"
  extraFiles: {}
  extraEnv: {}
  lifecycleHooks: {}
  initContainers: []
  extraContainers: []
  allowPrivilegeEscalation: false
  uid: 1000
  fsGid: 100
  serviceAccountName:
  storage:
    type: dynamic
    extraLabels: {}
    extraVolumes: []
    extraVolumeMounts: []
    static:
      pvcName:
      subPath: "{username}"
    capacity: 10Gi
    homeMountPath: /home/jovyan
    dynamic:
      storageClass:
      pvcNameTemplate: claim-{username}{servername}
      volumeNameTemplate: volume-{username}{servername}
      storageAccessModes: [ReadWriteOnce]
  image:
    name: privatecr.io/jupyter/minimal-notebook
    tag: "latest"
    pullPolicy:
    pullSecrets: 
     - jhub-secret-acr-sp
  startTimeout: 300
  cpu:
    limit:
    guarantee:
  memory:
    limit:
    guarantee: 1G
  extraResource:
    limits: {}
    guarantees: {}
  cmd: jupyterhub-singleuser
  defaultUrl:
  extraPodConfig: {}
  profileList: []

# scheduling relates to the user-scheduler pods and user-placeholder pods.
scheduling:
  userScheduler:
    enabled: true
    revisionHistoryLimit:
    replicas: 2
    logLevel: 4
    # plugins are configured on the user-scheduler to make us score how we
    # schedule user pods in a way to help us schedule on the most busy node. By
    # doing this, we help scale down more effectively. It isn't obvious how to
    # enable/disable scoring plugins, and configure them, to accomplish this.
    #
    # plugins ref: https://kubernetes.io/docs/reference/scheduling/config/#scheduling-plugins-1
    # migration ref: https://kubernetes.io/docs/reference/scheduling/config/#scheduler-configuration-migrations
    #
    plugins:
      score:
        # These scoring plugins are enabled by default according to
        # https://kubernetes.io/docs/reference/scheduling/config/#scheduling-plugins
        # 2022-02-22.
        #
        # Enabled with high priority:
        # - NodeAffinity
        # - InterPodAffinity
        # - NodeResourcesFit
        # - ImageLocality
        # Remains enabled with low default priority:
        # - TaintToleration
        # - PodTopologySpread
        # - VolumeBinding
        # Disabled for scoring:
        # - NodeResourcesBalancedAllocation
        #
        disabled:
          # We disable these plugins (with regards to scoring) to not interfere
          # or complicate our use of NodeResourcesFit.
          - name: NodeResourcesBalancedAllocation
          # Disable plugins to be allowed to enable them again with a different
          # weight and avoid an error.
          - name: NodeAffinity
          - name: InterPodAffinity
          - name: NodeResourcesFit
          - name: ImageLocality
        enabled:
          - name: NodeAffinity
            weight: 14631
          - name: InterPodAffinity
            weight: 1331
          - name: NodeResourcesFit
            weight: 121
          - name: ImageLocality
            weight: 11
    pluginConfig:
      # Here we declare that we should optimize pods to fit based on a
      # MostAllocated strategy instead of the default LeastAllocated.
      - name: NodeResourcesFit
        args:
          scoringStrategy:
            resources:
              - name: cpu
                weight: 1
              - name: memory
                weight: 1
            type: MostAllocated
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    image:
      # IMPORTANT: Bumping the minor version of this binary should go hand in
      #            hand with an inspection of the user-scheduelrs RBAC resources
      #            that we have forked in
      #            templates/scheduling/user-scheduler/rbac.yaml.
      #
      #            Debugging advice:
      #
      #            - Is configuration of kube-scheduler broken in
      #              templates/scheduling/user-scheduler/configmap.yaml?
      #
      #            - Is the kube-scheduler binary's compatibility to work
      #              against a k8s api-server that is too new or too old?
      #
      #            - You can update the GitHub workflow that runs tests to
      #              include "deploy/user-scheduler" in the k8s namespace report
      #              and reduce the user-scheduler deployments replicas to 1 in
      #              dev-config.yaml to get relevant logs from the user-scheduler
      #              pods. Inspect the "Kubernetes namespace report" action!
      #
      #            - Typical failures are that kube-scheduler fails to search for
      #              resources via its "informers", and won't start trying to
      #              schedule pods before they succeed which may require
      #              additional RBAC permissions or that the k8s api-server is
      #              aware of the resources.
      #
      #            - If "successfully acquired lease" can be seen in the logs, it
      #              is a good sign kube-scheduler is ready to schedule pods.
      #
      name: k8s.gcr.io/kube-scheduler
      # tag is automatically bumped to new patch versions by the
      # watch-dependencies.yaml workflow. The minor version is pinned in the
      # workflow, and should be updated there if a minor version bump is done
      # here.
      #
      tag: "v1.23.10" # ref: https://github.com/kubernetes/website/blob/eb2694c8bcc62a1337d8a02f449d38f4947a3ea9/content/en/releases/patch-releases.md
      pullPolicy:
      pullSecrets: []
    nodeSelector: 
      kubernetes.azure.com/agentpool: defaultpool
    tolerations: []
    labels: {}
    annotations: {}
    pdb:
      enabled: true
      maxUnavailable: 1
      minAvailable:
    resources: {}
    serviceAccount:
      create: true
      name:
      annotations: {}
    extraPodSpec: {}
  podPriority:
    enabled: false
    globalDefault: false
    defaultPriority: 0
    imagePullerPriority: -5
    userPlaceholderPriority: -10
  userPlaceholder:
    enabled: true
    image:
      name: k8s.gcr.io/pause
      # tag is automatically bumped to new patch versions by the
      # watch-dependencies.yaml workflow.
      #
      # If you update this, also update prePuller.pause.image.tag
      #
      tag: "3.8"
      pullPolicy:
      pullSecrets: []
    revisionHistoryLimit:
    replicas: 0
    labels: {}
    annotations: {}
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    resources: {}
  corePods:
    tolerations:
      - key: hub.jupyter.org/dedicated
        operator: Equal
        value: core
        effect: NoSchedule
      - key: hub.jupyter.org_dedicated
        operator: Equal
        value: core
        effect: NoSchedule
    nodeAffinity:
      matchNodePurpose: prefer
  userPods:
    tolerations:
      - key: hub.jupyter.org/dedicated
        operator: Equal
        value: user
        effect: NoSchedule
      - key: hub.jupyter.org_dedicated
        operator: Equal
        value: user
        effect: NoSchedule
    nodeAffinity:
      matchNodePurpose: prefer

# prePuller relates to the hook|continuous-image-puller DaemonsSets
prePuller:
  revisionHistoryLimit:
  labels: {}
  annotations: {}
  resources: {}
  containerSecurityContext:
    runAsUser: 65534 # nobody user
    runAsGroup: 65534 # nobody group
    allowPrivilegeEscalation: false
  extraTolerations: []
  # hook relates to the hook-image-awaiter Job and hook-image-puller DaemonSet
  hook:
    enabled: true
    pullOnlyOnChanges: true
    # image and the configuration below relates to the hook-image-awaiter Job
    image:
      name: jupyterhub/k8s-image-awaiter
      tag: "2.0.0"
      pullPolicy:
      pullSecrets: []
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    podSchedulingWaitDuration: 10
    nodeSelector: 
      kubernetes.azure.com/agentpool: defaultpool
    tolerations: []
    resources: {}
    serviceAccount:
      create: true
      name:
      annotations: {}
  continuous:
    enabled: true
  pullProfileListImages: true
  extraImages: {}
  pause:
    containerSecurityContext:
      runAsUser: 65534 # nobody user
      runAsGroup: 65534 # nobody group
      allowPrivilegeEscalation: false
    image:
      name: k8s.gcr.io/pause
      # tag is automatically bumped to new patch versions by the
      # watch-dependencies.yaml workflow.
      #
      # If you update this, also update scheduling.userPlaceholder.image.tag
      #
      tag: "3.8"
      pullPolicy:
      pullSecrets: []

ingress:
  enabled: false
  annotations: {}
  ingressClassName:
  hosts: []
  pathSuffix:
  pathType: Prefix
  tls: []

# cull relates to the jupyterhub-idle-culler service, responsible for evicting
# inactive singleuser pods.
#
# The configuration below, except for enabled, corresponds to command-line flags
# for jupyterhub-idle-culler as documented here:
# https://github.com/jupyterhub/jupyterhub-idle-culler#as-a-standalone-script
#
cull:
  enabled: true
  users: false # --cull-users
  adminUsers: true # --cull-admin-users
  removeNamedServers: false # --remove-named-servers
  timeout: 3600 # --timeout
  every: 600 # --cull-every
  concurrency: 10 # --concurrency
  maxAge: 0 # --max-age

debug:
  enabled: true

global:
  safeToShowValues: false

[welcome[bot]]

Thank you for opening your first issue in this project! Engagement like this is essential for open source projects! :hugs:
If you haven't done so already, check out Jupyter's Code of Conduct. Also, please try to follow the issue template as it helps other other community members to contribute more effectively. You can meet the other Jovyans by joining our Discourse forum. There is also an intro thread there where you can stop by and say Hi! :wave:
Welcome to the Jupyter community! :tada:

[manics]

Please don't open duplicate issues (https://discourse.jupyter.org/t/403-forbidden-error-when-using-azure-ad-for-authentication/16737) as it ends up wasting maintainer's time.