[Numpy-discussion] distributing wheels & SSE/superpack options
cgohlke at uci.edu
Fri Dec 6 16:01:32 EST 2013
On 12/6/2013 12:40 PM, David Cournapeau wrote:
> On Fri, Dec 6, 2013 at 8:38 PM, Christoph Gohlke <cgohlke at uci.edu
> <mailto:cgohlke at uci.edu>> wrote:
> On 12/6/2013 10:06 AM, Ralf Gommers wrote:
> > Hi all,
> > There are a few discussions on packaging for the scientific Python stack
> > ongoing, on the NumFOCUS and distutils lists:
> > <https://groups.google.com/forum/#%21topic/numfocus/mVNakFqfpZg>
> > <https://groups.google.com/forum/#%21topic/numfocus/HUcwXTM_jNY>
> > One of the things that we should start doing for numpy is distribute
> > releases as wheels. On OS X at least this is quite simple, so I propose
> > to just experiment with it. I can create some to try out and put them on
> > a separate folder on SourceForge. If that works they can be put on PyPi.
> > For Windows things are less simple, because the wheel format doesn't
> > handle the multiple builds (no SSE, SSE2, SSE3) that are in the
> > superpack installers. A problem is that we don't really know how many
> > users still have old CPUs that don't support SSE3. The impact for those
> > users is high, numpy will install but crash (see
> >https://github.com/scipy/scipy/issues/1697). Questions:
> > 1. does anyone have a good idea to obtain statistics?
> > 2. in the absence of statistics, can we do an experiment by putting one
> > wheel up on PyPi which contains SSE3 instructions, for python 3.3 I
> > propose, and seeing for how many (if any) users this goes wrong?
> > Ralf
> > P.S. related question: did anyone check whether the recently merged
> > NPY_HAVE_SSE2_INTRINSIC puts SSE2 instructions into the no-SSE binary?
> Has anyone succeeded building wheels for numpy, scipy, and matplotlib?
> I did for numpy and scipy. You had to hack a bit numpy.distutils to make
> it work for scipy,but nothing that would be too complicated to really fix.
> In your case, the trick is to use the setupegg file: python setupegg.py
Thank you. The setupegg.py trick worked. Could the numpy.distutils hack
be applied to the numpy 1.8.x and master branches? I'll try to fix the
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