Creating a Customized Engine with the Required Package(s)
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
the project mount), cannot be installed directly from the workbench. For such circumstances,
Cloudera recommends you extend the base Cloudera Data Science Workbench engine image to build a
customized image with all the required packages installed to it.
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 out of the box or if a package just has a complicated setup, it might be easier to simply provide users with an engine environment that has already been customized for their project(s).
For detailed instructions with an example, see Customized Engine Images.