The article was specific to AMD's BLIS fork, but just as an FYI, using standard BLIS w/ numpy (which I believe would still outperform OpenBLAS) is pretty straightforward:
Are there any reads you can recommend on how to recover from (possibly) failed experiments like these under pip / conda / linux in general? I've been doing R and Linux for two decades, but still am perfectly capable of totally breaking my python/jupyter-workflow by messing up dependencies without knowing how to recover. Would love to learn to remedy that gap and reducing the risk.
You can actually see in my pasted command that I created a new venv for the blis install. It's as simple as "activate" or "deactivate" to switch around.
That's basically all there is to it, but here are some docs for you to reference from the top page of search:
When dealing with purely python packages: always always work in python virtual envs. Conduct these kinds of experiments in separate venvs. If worse comes to worse, a reset consists simply in deleting the venv directory and installing from scratch.
If an experiment involves system packages it's a little trickier. You can learn about Docker (which does for the whole system what venv does for python packages), but in most cases you may get away with just taking note of what packages you have installed/uninstalled, so you can revert the process later if needed.
