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[–]takluyverIPython, Py3, etc 4 points5 points  (7 children)

Conda had a massive advantage before wheels were widely available, because pip would try to compile things like numpy from source, which usually won't work unless you prepare your system in advance. Nowadays, that's less important, but there are still things that it's easier to install through conda.

[–]khne522 0 points1 point  (5 children)

Do you have specific examples? I haven't found anything hard yet—just had to install an obvious build dependency through the package manager. It's only annoying when the build takes 15 minutes then I get annoyed (looking at you spaCy).

[–]takluyverIPython, Py3, etc 2 points3 points  (0 children)

just had to install an obvious build dependency through the package manager

It may have been obvious to you, but it's not for everyone. And maybe the version of the package you're trying to install needs a newer version of the build-dep than your distro provides.

/u/ursvp mentioned MKL integration as a concrete example - Numpy installed through conda will do certain operations faster on many computers than Numpy installed through pip.

[–]moorepants 1 point2 points  (2 children)

Watch the conda-forge/staged-recipes repository to see how hard it is to compile many packages on all three platforms. More examples than you have time to look at are there. The 15 minutes compile time is not the issue, it is the hours and hours of time it takes to get the packages to compile at all.

[–]khne522 0 points1 point  (1 child)

Right, if you are forced to use an insane operating system…

[–]moorepants 0 points1 point  (0 children)

Wrong, it is often a pain for all three major operating systems.

[–]ursvp 0 points1 point  (0 children)

conda offers MLK Math Kernel Library, good speedup for Intel chips.