Creating customized ML Runtimes

This section walks you through the steps required to create your own custom ML Runtimes based on the ML Runtime images provided by Cloudera.

note

In case of PBJ Runtimes, the Custom runtime does not have to be built on the top of the ML Runtime image provided by Cloudera.

Creating a Dockerfile for the custom Runtime Image
Follow the instructions to create a Dockerfile for a custom image.
Metadata for custom ML Runtimes
This topic addresses the metadata for custom Runtimes.
Customizing the editor
A third-party editor can be customized to work with ML Runtimes.
Building the new Docker Image
Follow the guidelines to use Docker to build a custom image.
Distributing the ML Runtime Image
Select a method to distribute a custom ML Runtime to all the hosts.
Adding Docker registry credentials and certificates
To enable Cloudera Machine Learning to fetch custom ML Runtimes from a secure repository, as an Administrator you need to add Docker registry credentials used to log into the registry. You may also need to add the Docker registry TLS certificate if you are using a custom or self-signed certificate for the registry.
Adding the new ML Runtime
Cloudera Machine Learning enables you to add customized ML Runtimes from the Runtime Catalog window.

Parent topic: Customized Runtimes