On Sat, Jan 24, 2015 at 5:29 PM, Carl Kleffner <email@example.com> wrote:
> 2015-01-23 0:23 GMT+01:00 Nathaniel Smith <firstname.lastname@example.org>:
>> On Thu, Jan 22, 2015 at 9:29 PM, Carl Kleffner <email@example.com>
>> > OpenBLAS is deployed as part of the numpy wheel. That said, the scipy
>> > wheels
>> > mentioned above are dependant on the installation of the OpenBLAS based
>> > numpy and won't work i.e. with an installed numpy-MKL.
>> This sounds like it probably needs to be fixed before we can recommend
>> the scipy wheels for anyone? OTOH it might be fine to start
>> distributing numpy wheels first.
> I very much prefer dynamic linking to numpy\core\libopenblas.dll instead of
> static linking to avoid bloat. This matters, because libopenblas.dll is a
> heavy library (around 30Mb for amd64). As a consequence all packages with
> dynamic linkage to OpenBLAS depend on numpy-openblas. This is not different
> to scipy-MKL that has a dependancy to numpy-MKL - see C. Gohlke's site.
The difference is that if we upload this as the standard scipy wheel,
and then someone goes "hey, look, a new scipy release just got
announced, 'pip upgrade scipy'", then the result will often be that
they just get random unexplained crashes. I think we should try to
avoid that kind of outcome, even if it means making some technical
compromises. The whole idea of having the wheels is to make fetching
particular versions seamless and robust, and the other kinds of builds
will still be available for those willing to invest more effort.
One solution would be for the scipy wheel to explicitly depend on a
numpy+openblas wheel, so that someone doing 'pip install scipy' also
forced a numpy upgrade. But I think we should forget about trying this
given the current state of python packaging tools: pip/setuptools/etc.
are not really sophisticated enough to let us do this without a lot of
kluges and compromises, and anyway it is nicer to allow scipy and
numpy to be upgraded separately.
Another solution would be to just include openblas in both. This
bloats downloads, but I'd rather waste 30 MiB then waste users' time
fighting with random library incompatibility nonsense that they don't
Another solution would be to split the openblas library off into its
own "python package", that just dropped the binary somewhere where it
could be found later, and then have both the numpy and scipy wheels
depend on this package.
We could start with the brute force solution (just including openblas
in both) for the first release, and then upgrade to the fancier
solution (both depend on a separate package) later.
>> > For the numpy 32bit builds there are 3 failures for special FP value
>> > tests,
>> > due to a bug in mingw-w64 that is still present. All scipy versions show
>> > up
>> > 7 failures with some numerical noise, that could be ignored (or
>> > corrected
>> > with relaxed asserts in the test code).
>> > PR's for numpy and scipy are in preparation. The mingw-w64 compiler used
>> > for
>> > building can be found at
>> > https://bitbucket.org/carlkl/mingw-w64-for-python/downloads.
>> Correct me if I'm wrong, but it looks like there isn't any details on
>> how exactly the compiler was set up? Which is fine, I know you've been
>> doing a ton of work on this and it's much appreciated :-). But
>> eventually I do think a prerequisite for us adopting these as official
>> builds is that we'll need a text document (or an executable script!)
>> that walks through all the steps in setting up the toolchain etc., so
>> that someone starting from scratch could get it all up and running.
>> Otherwise we run the risk of eventually ending up back where we are
>> today, with a creaky old mingw binary snapshot that no-one knows how
>> it works or how to reproduce...
> This has to be done and is in preperation, but not ready for consumption
> right now. Some preliminary information is given here:
Right, I read that :-). There's no way that I could sit down with that
document and a clean windows install and replicate your mingw-w64
toolchain, though :-). Which, like I said, is totally fine at this
stage in the process, I just wanted to make sure that this step is on
the radar, b/c it will eventually become crucial.
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
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