Comment by momojo

21 hours ago

This is awesome! For those here not familiar with Numba, it helps bridge that performance vs ergonomics tradeoff that's always existed when you reach for python over a lower level but faster lang like C or C++.

Sure you could write Cython but then you have to have a build step and make wheels for every platform you and python version. Sure numpy has gotten faster over the years but you're still hampered by the GIL.

Numba is a little magic because you get to write stuff that feels like numpy, but get literal bytecode perf.

BUT there's a cost to this, which u learned the hard way when I imported a color map extension for matplotlib recently.

I thought i was going to be importing a couple megabytes at most. But Numba+llvmlite alone is almost 100MB!

This might be a drop in the bucket in some applications but for a color map library that has only two hot paths that need to be JITed, it's excessive.

Overall though, love this achievement, and i love what's being done for in-browser (aka local-first) scientific computing!

I run some Jupyter notebooks against a local Postgres realestate db to do statistical analysis. Could I use this to just run it on a compute host without having to manually set up and activate a venv?

  • Good question.

    I'd stick with the venv if it's heavy duty crunching and you do it often.

    However, if this is a one off or doesn't need heavy compute, and you don't mind waiting a little longer, use the notebook.link.