<div dir="ltr">On Fri, Dec 6, 2013 at 10:06 AM, Ralf Gommers <span dir="ltr"><<a href="mailto:ralf.gommers@gmail.com" target="_blank">ralf.gommers@gmail.com</a>></span> wrote:<br><div class="gmail_extra"><div class="gmail_quote">
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div>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. </div>
</div></div></blockquote><div><br></div><div>OK -- maybe on the wrong list, but an itch of mine is OSX binaries of IPython (and the dependencies required for the notebook, too. There is right no way for an OS_X user without the compiler setup to get iPython without going to Anaconda or Canopy, <br>
</div><div><br></div><div>Yet it's a really great tool for newbies....<br><br></div><div>So I just sat down and did a simple:<br><br>pip wheel --wheel-dir=wheelhouse2 ipython[all]<br><br></div><div>Wow -- took a little while, but presto! A pile of wheels, ready to go:<br>
<br>$ ls wheelhouse/<br>Jinja2-2.7.1-py27-none-any.whl<br>pyzmq-14.0.1-cp27-none-macosx_10_6_intel.whl<br>MarkupSafe-0.18-cp27-none-macosx_10_6_intel.whl<br>readline-6.2.4.1-cp27-none-macosx_10_6_intel.whl<br>Pygments-1.6-py27-none-any.whl<br>
tornado-3.1.1-py27-none-any.whl<br>ipython-1.1.0-py27-none-any.whl<br></div><div><br></div><div><br></div><div>Now, do they work? They do on my machine. Is there somewhere I could put them up so folks could test?<br><br>
</div>
<div>-Chris<br><br></div><div><br><br></div><div> <br><br><br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div>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.<br>
<br></div>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 <a href="https://github.com/scipy/scipy/issues/1697" target="_blank">https://github.com/scipy/scipy/issues/1697</a>). Questions:<br>
1. does anyone have a good idea to obtain statistics?<br></div><div>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?<span class=""><font color="#888888"><br>
</font></span></div><span class=""><font color="#888888"><div><br></div><div>Ralf<br></div></font></span><div><br></div>P.S. related question: did anyone check whether the recently merged NPY_HAVE_SSE2_INTRINSIC puts SSE2 instructions into the no-SSE binary?<br>
<br></div>
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