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Creating a custom image

Create custom images for Python-based models and functions or create a custom SPSS image.

The samples provided here use Podman, but you can use any other Docker-compatible image builder.

Creating a custom Python image

After you download the image for the runtime that you want to customize, you can create a custom image by adding your customizations to the downloaded image. Start with the base image and install the libraries and packages that you need. Then, add your customizations to the Dockerfile.

Refer to the sample Dockerfile for the Watson Machine Learning Python runtime.

Required role: You must have root privileges on your machine to execute the commands that build a custom image.

  1. Prepare the Dockerfile.

    • Do not change the contents in the USER directives. They impact how the container runs in the cluster.
    • You must have root privileges to install OS packages and to modify kernel specifications.
    • Use the microdnf tool to install the OS libraries.

  2. Create a custom Watson Machine Learning image.

    If you are updating an existing CPD 3.5 image or an image that was created before 4.0.6 release of CPD 4.0.x, refer to the sample Dockerfile in Updating a custom image.

Adding customizations for SPSS models

Refer to the sample Dockerfile for the Watson Machine Learning SPSS Deployment images.

To add customizations for SPSS, run the Dockerfile for installing the Exasol driver in SPSS.

For <SPSS_RUNTIME_IMAGE>, use the image name from Downloading a custom image configuration file for SPSS.

Next steps

Pushing the custom image to the image registry.

Parent topic: Customizing deployment runtime images