# Jupyterhub for Kubernes: How to avoid Image Prepull on specific nodes

**URL:** <https://discourse.jupyter.org/t/jupyterhub-for-kubernes-how-to-avoid-image-prepull-on-specific-nodes/5537>\
**Category:** General\
**Created:** [August 7, 2020, 11:48am UTC](https://discourse.jupyter.org/t/jupyterhub-for-kubernes-how-to-avoid-image-prepull-on-specific-nodes/5537 "2020-08-07T11:48:45Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![adr-arroyo](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.jupyter.org/adr-arroyo/32/2285_2.png) [@adr-arroyo](https://discourse.jupyter.org/u/adr-arroyo)\
**Post date:** [August 7, 2020, 11:48am UTC](https://discourse.jupyter.org/t/jupyterhub-for-kubernes-how-to-avoid-image-prepull-on-specific-nodes/5537/1 "2020-08-07T11:48:45Z")

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Hello everyone,

I have a custom Kubernetes cluster with several nodes, each node has assigned some tags to differenciate between them with a Kubernetes label selector (nodeSelector), being one of them PU = “gpu” || “cpu”. I have also deployed a Jupyterhub environment on my cluster. However I am having trouble with the deployments of different profiles (singleUser.profileList) on different nodes.

More specifically, I would like to have a profile assigned to nodes with PU: “cpu” and other profile assigned to nodes with PU: “gpu”, in order to take advantage of the GPU when deploying notebooks. In the “cpu” profile I have to select the intel version of the docker image, whereas in the “gpu” profile I need a specific ARM (+ NVIDIA) docker image since the node is deployed on an arm-based machine. I believe I have configured properly each profile so that the user server is deployed in the proper nodes, with the nodeSelector tag (see below).

The problem comes with the Jupyterhub image prepuller. I have seen that it tries to pull **all the images of every profile in every node** , even though the nodeSelector tag is configured. Then, in the arm/gpu node it tries to prepull the image of the intel/cpu node , and on this node it tries to pull the arm/gpu image, ending up in an unsuccessfull installation and jupyterhub not being deployed on the cluster.

Is this a bug or a misconfiguration?

My configuration:

```auto
...

singleuser:
  nodeSelector:
    ARCH: "intel"
    LAYER: "cloud"
  defaultUrl: "/lab"
  image:
    name: easierai/jupyterhub-core
    tag: latest
  profileList:
    - display_name: "environment"
      description: "Environment preconfigured ."
      default: true
      nodeSelector:
        PU: "cpu"
    - display_name: "Minimal Environment"
      description: "Only default libraries installed."
      kubespawner_override:
        image: jupyter/minimal-notebook
        nodeSelector:
          PU: "cpu"
    - display_name: "one NVIDIA"
      description: "ARM, GPU"
      kubespawner_override:
        image: step21/jupyter-minimal-notebook:arm64
        nodeSelector:
          PU: "gpu"
        extraResource:
          limits:
            nvidia.com/gpu: 1
...

```

See issue: [https://github.com/jupyterhub/zero-to-jupyterhub-k8s/issues/1739](https://github.com/jupyterhub/zero-to-jupyterhub-k8s/issues/1739)