Installing Packages and Libraries

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.

You can install additional libraries and packages from the workbench, either using the command prompt or 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, see Using Conda with Cloudera Data Science Workbench.

To install a package from the command prompt:

R

# 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

(Python Only) Using a Requirements File

For a Python project, you can specify a list of the packages you want in a requirements.txt file that lives in your project. The packages can be installed all at once using pip/pip3.

1. Create a new file called requirements.txt file within your project:

   beautifulsoup4==4.6.0
   seaborn==0.7.1

   To install the packages, just run:

   !pip3 install -r requirements.txt

Cloudera Data Science Workbench does not currently support customization of system packages that require root access. However, Cloudera Data Science Workbench site administrators and project administrators can add libraries and other dependencies to the Docker image in which their engines run. See Creating Extended Engine Images.