Creating Customized ML Runtimes

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

note

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

Create a Dockerfile for the Custom Runtime Image
This topic shows you how to create a Dockerfile for a custom image.
Metadata for Custom ML Runtimes
This topic addresses the metadata for custom Runtimes.
Editor Customization
This topic addresses customizing a third-party editor to work with ML Runtimes.
Build the New Docker Image
This topic shows you how to use Docker to build a custom image.
Distribute the Image
This topic explains the different methods that can be used to distribute a custom ML Runtime to all the hosts.
Add the new ML Runtime
Cloudera Machine Learning enables you to add customized ML Runtimes from the Runtime Catalog window.

Parent topic: Customized Runtimes