GNU/Linux Desktop Survival Guide by Graham Williams
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MLHub Tips

20200221 For R based models it is often useful to install some R packages through the operating system, or else locally by a user. For the latter case some useful packages to pre-install are identified below. This can be done at any time, but is useful before installing any of the R based MLHub packages. They will not then individually need to install the packages for themselves.

$ R
> install.packages(c("rpart", "tidyverse"))

Similarly for common Python dependencies. One particular example is tensorflow which does not have a Ubuntu package and thus is installed using pip3. This can be installed any time, and any mlhub package that requires tensorflow will not need to install it separately.

$ pip3 install tensorflow

If a model has installed badly, got corrupted, or not working as expected, sometimes an uninstall followed by install will fix the problem. When uninstalling in these circumstances it is usually a good idea to remove the cache as well:

$ ml uninstall objects
Remove '/home/kayon/.mlhub/objects/' [Y/n]? y
Remove cache '/home/kayon/.mlhub/.cache/objects/' as well [y/N]? y

$ ml install objects