On Wed, Jan 29, 2014 at 10:27 PM, Chris Barker <chris.bar...@noaa.gov>wrote:
> On Wed, Jan 29, 2014 at 2:04 PM, David Cournapeau <courn...@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.orgbinary > 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
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