Customized Engine Images

By default, Cloudera Data Science Workbench engines are preloaded with a few common packages and libraries for R, Python, and Scala. In addition to these, Cloudera Data Science Workbench 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 Data Science Workbench 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 whitelist this new image for use in projects, and project administrators set the new white-listed image to be used as the default engine image for their projects. For an end-to-end example of this process, see End Example: MeCab.

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 an engine that has already been customized for their project(s).