Installing Additional Packages

Cloudera Data Science Workbench engines are preloaded with a few common packages and libraries for R, Python, and Scala. However, a key feature of Cloudera Data Science Workbench is the ability of different projects to install and use libraries pinned to specific versions, just as you would on your local computer.

Generally, Cloudera recommends you install all required packages locally into your project. This will ensure you have the exact versions you want and that these libraries will not be upgraded when Cloudera upgrades the base engine image. You only need to install libraries and packages once per project. From then on, they are available to any new engine you spawn throughout the lifetime of the project.

You can install additional libraries and packages from the workbench, using either the command prompt or the terminal. Alternatively, you might choose to use a package manager such as Conda to install and maintain packages and their dependencies. For some basic usage guidelines for Conda, see Using Conda with Cloudera Data Science Workbench.

Navigate to your project's Overview page. Click New Session and start a session.

At the command prompt in the bottom right, enter the command to install the package. Some examples using Python and R have been provided.

# Install from CRAN 
install.packages("ggplot2") 

# Install using devtools 
install.packages('devtools') 
library(devtools) 
install_github("hadley/ggplot2")

Python 2

# Installing from console using ! shell operator and pip:
!pip install beautifulsoup

# Installing from terminal
pip install beautifulsoup

Python 3

# Installing from console using ! shell operator and pip3:
!pip3 install beautifulsoup4

# Installing from terminal
pip3 install beautifulsoup4