This topic explains how custom Runtimes work and when they should be used.
default, Cloudera Machine Learning Runtimes are preloaded with a few
common packages and libraries for R, Python, and Scala. In addition to these, Cloudera Machine Learning also allows you to install any other packages
or libraries that are required by your projects. However, directly installing a package to
a project as described above might not always be feasible. For example, packages that
require root access to be installed, or that must be installed to a path outside
/home/cdsw (outside the project mount), cannot
be installed directly from the workbench.
For such circumstances, Cloudera Machine Learning allows you to extend the base Docker image and create a new Docker image with all the libraries and packages you require. Site administrators can then add this new image in the allowlist for use in projects.
PBJ Custom Runtimes can be built on top of any Ubuntu base image, and users have to install the kernel themselves. However, non-PBJ Runtime images can only be built on top of Cloudera-released non-PBJ Runtime images, and users cannot change the kernel.
Note that this approach can also be used to accelerate project setup across the deployment. For example, if you want multiple projects on your deployment to have access to some common dependencies (package or software or driver) out of the box, or even if a package just has a complicated setup, it might be easier to simply provide users with a Runtime that has already been customized for their project(s).
- The Cloudera Engineering Blog post on Customizing Docker Images in Cloudera Machine Learning describes an end-to-end example on how to build and publish a customized Docker image and use it as an engine in Cloudera Machine Learning.
- For an example of how to extend the base engine image to include Conda, see Installing Additional Packages.