On Wed, Jan 29, 2014 at 10:27 PM, Chris Barker <chris.barker@noaa.gov> wrote:

On Wed, Jan 29, 2014 at 2:04 PM, David Cournapeau <cournape@gmail.com> wrote:

I think the SSE issue is a bit of a side discussion: most people who care about performance already know how to install numpy. What we care about here are people who don't care so much about fast eigenvalue decomposition, but want to use e.g. pandas. Building numpy in a way that supports every architecture is both doable and acceptable IMO.

Exactly -- I'm pretty sure SSE2 is being suggested because that's the lowest common denominator that we expect to see a lot of -- if their really are a lot of non-SSE-2 machines out there we could leave that off, too.

The failure mode is fairly horrible though, and the gain is not that substantial anyway compared to really optimized installation (MKL, etc... as provided by Continuum or us).

Building numpy wheels is not hard, we can do that fairly easily (I have already done so several times, the hard parts have nothing to do with wheel or even python, and are related to mingw issues on win 64 bits).

David,

Where is numpy as with building "out of the box" with the python.org binary for Windows, and the "standard" MS compilers that are used with those builds. That used to be an easy "python setup.py install" away -- has that changed? If so, is this a known bug, or a known we-aren't-supporting-that?

i.e. it would be nice if anyone setup to build C extensions could "just build numpy".

This has always been possible, and if not, that's certainly considered as a bug (I would be eager to fix).

Numpy is actually fairly easy to build if you have a C Compiler (which is the obvious pain point on windows). Scipy, and fortran is where things fall apart.

David