[Numpy-discussion] Numpy 1.11.0b2 released

[Robert T. McGibbon]

> (we've had a few recent issues with libgfortran accidentally missing as a
requirement of scipy).

On this topic, you may be able to get some milage out of adapting
pypa/auditwheel, which can load
up extension module `.so` files inside a wheel (or conda package) and walk
the shared library dependency
tree like the runtime linker (using pyelftools), and check whether things
are going to resolve properly and
where shared libraries are loaded from.

Something like that should be able to, with minimal adaptation to use the
conda dependency resolver,
check that a conda package properly declares all of the shared library
dependencies it actually needs.

-Robert

On Sat, Feb 6, 2016 at 3:42 PM, Michael Sarahan <msarahan at gmail.com> wrote:

> FWIW, we (Continuum) are working on a CI system that builds conda
> recipes.  Part of this is testing not only individual packages that change,
> but also any downstream packages that are also in the repository of
> recipes.  The configuration for this is in
> https://github.com/conda/conda-recipes/blob/master/.binstar.yml and the
> project doing the dependency detection is in
> https://github.com/ContinuumIO/ProtoCI/
>
> This is still being established (particularly, provisioning build
> workers), but please talk with us if you're interested.
>
> Chris, it may still be useful to use docker here (perhaps on the build
> worker, or elsewhere), also, as the distinction between build machines and
> user machines is important to make.  Docker would be great for making sure
> that all dependency requirements are met on end-user systems (we've had a
> few recent issues with libgfortran accidentally missing as a requirement of
> scipy).
>
> Best,
> Michael
>
> On Sat, Feb 6, 2016 at 5:22 PM Chris Barker <chris.barker at noaa.gov> wrote:
>
>> On Fri, Feb 5, 2016 at 3:24 PM, Nathaniel Smith <njs at pobox.com> wrote:
>>
>>> On Fri, Feb 5, 2016 at 1:16 PM, Chris Barker <chris.barker at noaa.gov>
>>> wrote:
>>>
>>
>>
>>> >> > If we set up a numpy-testing conda channel, it could be used to
>>> cache
>>> >> > binary builds for all he versions of everything we want to test
>>> >> > against.
>>>
>>   Anaconda doesn't always have the
>>> > latest builds of everything.
>>
>>
>> OK, this may be more or less helpful, depending on what we want to built
>> against. But a conda environment (maybe tied to a custom channel) really
>> does make  a nice contained space for testing that can be set up fast on a
>> CI server.
>>
>> If whoever is setting up a test system/matrix thinks this would be
>> useful, I'd be glad to help set it up.
>>
>> -Chris
>>
>>
>>
>>
>>
>> --
>>
>> Christopher Barker, Ph.D.
>> Oceanographer
>>
>> Emergency Response Division
>> NOAA/NOS/OR&R            (206) 526-6959   voice
>> 7600 Sand Point Way NE   (206) 526-6329   fax
>> Seattle, WA  98115       (206) 526-6317   main reception
>>
>> Chris.Barker at noaa.gov
>> _______________________________________________
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>>
>
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>


-- 
-Robert
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