Table of contents

Use custom images

You can use custom images in Watson Studio Local in the following ways.

Use a custom image for a notebook on Watson Studio Local

  1. In your project Environments page, click the environment name to edit.

    Runtime environments

  2. For Image, select the custom image and click the Save and restart button.

    Runtime environment

  3. Go to your project Assets page, click Notebooks, and click Add Notebook button.

    Notebooks page

  4. For Environment, select the environment that uses the custom image and click Create to create the notebook.

Create a worker environment for the customized image

To edit a worker environment to use the customized image:

  1. Go to your project Jobs page and click Workers.

    Workers page

  2. Edit the worker environment and select the customized image.


  3. Save the worker.

As a result, whenever Watson Studio Local users run a batch score or evaluate job, they can select this modified worker in the Advanced settings.

Manage visibility of images

Data scientists can customize the Jupyter Python images based on the additional packages they need. By default the images are visible to all data scientists. For enterprises that want to follow an approval process before making the images visible to all data scientists, the Watson Studio Local administrator can set a configuration property that will allow users to see only approved images or images created by them.
Important: You must have patch01 installed to take advantage of this enhancement. Go here, and then select wsl-x86-v1231-patch01-TS002078840-TS002078850 to get the enhancement.

To manage the visibility of images:

  1. Run kubectl get pods -n dsx and identify the utils-api pod.
  2. Run kubectl exec pod -n dsx to get into the pod.
  3. Create or edit the /user-home/_global_/config/ file and set CustomImagesVisibility=ApprovedAndSelf. The default value for CustomImagesVisibility is All.