2.2 Setup MLHub on Ubuntu


After installation the system can be checked to ensure the required software is installed by running the command:

ml configure

If you are asked for a sudo password then some system packages will need to be installed. If you do not have system privileges then you will need your system administrator to install the required packages, by running the above command for you. As a non-sudo user simply type Ctrl-D to escape from the request for a privileged password and to continue the configuration of mlhub.

This may take 5 to 10 minutes, depending on what other dependencies are already installed.

You can also install the mlhub R package. If you don’t have system administrator privileges simply drop the sudo prefix to these two commands:

sudo Rscript -e 'install.packages("testthat", quiet=TRUE)'
sudo Rscript -e 'devtools::install_github("mlhubber/mlhub@main", quiet=TRUE)'

The ml command should now be ready to use.

Getting started is now simple. Choose from amongst the packages of interest to you from the package catalogue. As a data scientist you may be interested in visualisations (ports), beeswarm, and animations (animate). For traditional machine learning there are models for rain prediction (rain) and movie recommendation (movies). For pre-built deep neural networks you can find models to colorize photos (colorize), identify objects (objects), to make you computer see with computer vision (azcv), or to detect faces (facedetect).

Explore, enjoy, share, and empower. Above all, let’s work toward a collective purpose of ensuring we have a meaningful future for humanity.

The remainder of this chapter introduces the ml command and the sub-commands it supports. The chapter finishes with useful tips for interacting with ml and improving ml performance.

Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0