20200220 MLHub runs on the Ubuntu platform and is implemented in Python3. All of the curated models that are registered with MLHub are tested against Ubuntu LTS (Long Term Support). MLHub can also be installed on MacOS and Windows though this is in development and you may need to manually install some dependencies, and many users have reported success on these platforms.
Ubuntu can be installed on almost anything from a Raspberry Pi to a desktop or laptop running Ubuntu directly or through a virtual machine, or via the Windows Subsystem for Linux (WSL). Ubuntu is the most widely deployed operating system on cloud servers, on smart devices (as Android), and is even the operating system of choice for the helicopter on Mars. The various options for installing Ubuntu are covered in the GNU/Linux Desktop Survival Guide. Once you have Ubuntu installed MLHub is easy.
sudo apt update sudo apt upgrade sudo apt install wajig wajig update wajig upgrade wajig install python3-pip pip install wajig
Be sure to log out and log back in after the pip
install so that the system will notice your local installations. This
will refresh the PATH that is used to find applications. On Ubuntu
Pip installs the wajig
~/.local/bin. If all else fails then the following could
be useful (but not usually required):
echo 'PATH=~/.local/bin:$PATH' >> ~/.bashrc && source ~/.bashrc
pip install mlhub
After installation the system can be configured:
This may take 5 to 10 minutes, depending on what other dependencies are already installed.
Also install the mlhub R package:
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 network models 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.
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