Managing Engine Dependencies

This page describes the options available to you for mounting a project's dependencies into its engine environment:

important

Even though experiments and models are created within the scope of a project, the engines they use are completely isolated from those used by sessions or jobs launched within the same project.

Installing Packages Directly Within Projects
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 allows you to install any other packages or libraries required by your projects just as you would on your local computer.
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 /home/cdsw (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.
Mounting Additional Dependencies from the Host
As described previously, all Cloudera Data Science Workbench projects run within an engine.
Managing Dependencies for Spark 2 Projects
With Spark projects, you can add external packages to Spark executors on startup.