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/#!topic/numfocus/mVNakFqfpZg https://groups.google.com/forum/#!topic/numfocus/HUcwXTM_jNY http://thread.gmane.org/gmane.comp.python.distutils.devel/20202 http://thread.gmane.org/gmane.comp.python.distutils.devel/20296
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?
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