A bunch of admin notes for HPC admin.
quotatools: Obtained via
apt-get. Not set up yet, only downloaded. Danger: may involve editing
/etc/fstabwhich is easily damaged, and may render the system un-bootable! PROCEED WITH EXTREME CAUTION. See a rough walkthrough here
- Nice askubuntu post about copying global bashrc's for everyone.
- BEAST: installed in
/usr/local/BEASTv1.8.4, as recommended by Tim Vaughan.
- BEAST2: local install in ~/dten0001/Downloads/beast2/, with a PATH added. Will think about global installation later.
- BEAGLE: I frankly have no idea. It's not in
- RAXML: ???
- Figtree: Installed via
- fasttree: Installed via
R: Installing shared libraries
Not sure if how to organize it such that users can install their own insulated packages if required (or maybe I'll just install packages on request to prevent compatibility debt from building up). Anyway, from within the R environment, use
.libPaths() to see which paths are accessible to the R executable. "/>
Global libraries are much better setup (compared to Python). Global libraries should be installed in
/usr/lib/R/site-libraries. source. Use
sudo R to start up R with admin privileges, and run:
Check out this University cluster webpage for how they did their setup: https://www.chpc.utah.ed u/documentation/software/r-language.php.
adephylo refuses to be installed, for some strange reason.
IMPT: do NOT use
sudo pip install my_package! Reason.
Frankly, I didn't initially track whether libraries should be globally installed (because I didn't think to do so), so much of what's on there now is quite messy; lost track of what site-packages are on the PATH. Users are recommended to
conda install whatever they need to their local conda
env. There's a global install of Anaconda in
/opt/anaconda, but this can't be conda-updated easily, so the current workaround is just containerise everything, with no global libraries, for each user.
Try this: Install a multi-user env in a global location. https://conda.io/docs/user-guide/configuration/admin-multi-user-install.html
Containerization done using
conda. See this page for conda vs. pip vs. virtualenv info. Some exploration commands:
conda info --envs- to see what environments have been set up
conda list- from an activated environment, to see what packages have been conda or pip-installed
pip list- to see what packages have been pip-installed.
pip show <my_package>- to see where
my_packagewas pip-installed. This should ideally be a global location.
Shared Anaconda installation: installed in
/opt/anaconda3. See this SO post.
- Read up about conda: myths and misconceptions