Alexander Michael wrote:
> On Feb 4, 2008 5:13 AM, David Cournapeau <[EMAIL PROTECTED]> wrote:
>> Hi,
>>
>> While studying a bit nsis (an open source system to build windows
>> installers), I realized that it would be good if we could detect the
>> target CPU and install the right numpy accordingly. I have coded a
>> nsis plugin to detect SSE availability (no SSE vs SSE vs SSE2 vs SS3),
>> and including installers within the nsis installer is easy. What would
>> people think about including the installers generated with the current
>> method (bdist_wininst, I guess ?) for every CPU target, and distribute
>> the bundled installer ? The only drawback I can see is the size of the
>> installer: in this case, we could have a system which download the
>> right installer, but that would be more work, obviously.
>> This seems like an easy, "not too much work required" solution to
>> the recurrent problem we get with atlas on windows.
>
> I like the idea of creating such a "universal" Windows installer for the
> (optional) numpy dependencies (particularly ATLAS) which is
> separate from the numpy distribution. Ultimately, it would be great if
> numpy automatically noticed if ATLAS has been installed this way and
> self-configured itself to use the libraries when available, but I would still
> consider this a better situation if it was easy to build numpy to use
> such an installation with numscons.
Well, this has nothing to do with numscons per se. I indeed started
working on this because of my work on numscons, though (I still need to
support windows platform, which I find extremely frustrating to work
with, and a super pack installer for all numpy/scipy dependencies makes
the pain lower for reproducible builds).
I see two cases, which is why I suggested this as a separate issue of my
recently announced blas/lapack superpack:
- people who just want to install numpy: people want to try numpy,
they don't want to care about sse and co. That's why an installer with
several numpy versions inside would be good: it would work for everybody.
- people who work with SVN: particularly for scipy, that's something
many people want to. Building blas, lapack and atlas is hard. I think I
know the problems pretty well, having build and installed them on so
many compiler/platforms combinations by now, but that's not something
terribly interesting. And it is hard to explain it well, because it is
so easy to make a mistake at some point. So instead of explaining how to
do it, just put something which works out of the box: that's what the
BLAS/LAPACK superpack is for.
cheers,
David
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
