[Numpy-discussion] Numpy 1.11.0b2 released

[Andreas Mueller]

Thanks, that is very helpful!

On 01/30/2016 01:40 PM, Jeff Reback wrote:
> just my 2c
>
> it's fairly straightforward to add a test to the Travis matrix to grab 
> numpy wheels built numpy wheels (works for conda or pip installs).
>
> so in pandas we r testing 2.7/3.5 against numpy master continuously
>
> https://github.com/pydata/pandas/blob/master/ci/install-3.5_NUMPY_DEV.sh
>
> On Jan 30, 2016, at 1:16 PM, Nathaniel Smith <njs at pobox.com 
> <mailto:njs at pobox.com>> wrote:
>
>> On Jan 30, 2016 9:27 AM, "Ralf Gommers" <ralf.gommers at gmail.com 
>> <mailto:ralf.gommers at gmail.com>> wrote:
>> >
>> >
>> >
>> > On Fri, Jan 29, 2016 at 11:39 PM, Nathaniel Smith <njs at pobox.com 
>> <mailto:njs at pobox.com>> wrote:
>> >>
>> >> It occurs to me that the best solution might be to put together a 
>> .travis.yml for the release branches that does: "for pkg in 
>> IMPORTANT_PACKAGES: pip install $pkg; python -c 'import pkg; pkg.test()'"
>> >> This might not be viable right now, but will be made more viable 
>> if pypi starts allowing official Linux wheels, which looks likely to 
>> happen before 1.12... (see PEP 513)
>> >>
>> >> On Jan 29, 2016 9:46 AM, "Andreas Mueller" <t3kcit at gmail.com 
>> <mailto:t3kcit at gmail.com>> wrote:
>> >> >
>> >> > Is this the point when scikit-learn should build against it?
>> >>
>> >> Yes please!
>> >>
>> >> > Or do we wait for an RC?
>> >>
>> >> This is still all in flux, but I think we might actually want a 
>> rule that says it can't become an RC until after we've tested 
>> scikit-learn (and a list of similarly prominent packages). On the 
>> theory that RC means "we think this is actually good enough to 
>> release" :-). OTOH I'm not sure the alpha/beta/RC distinction is very 
>> helpful; maybe they should all just be betas.
>> >>
>> >> > Also, we need a scipy build against it. Who does that?
>> >>
>> >> Like Julian says, it shouldn't be necessary. In fact using old 
>> builds of scipy and scikit-learn is even better than rebuilding them, 
>> because it tests numpy's ABI compatibility -- if you find you *have* 
>> to rebuild something then we *definitely* want to know that.
>> >>
>> >> > Our continuous integration doesn't usually build scipy or numpy, 
>> so it will be a bit tricky to add to our config.
>> >> > Would you run our master tests? [did we ever finish this 
>> discussion?]
>> >>
>> >> We didn't, and probably should... :-)
>> >
>> > Why would that be necessary if scikit-learn simply tests 
>> pre-releases of numpy as you suggested earlier in the thread (with 
>> --pre)?
>> >
>> > There's also https://github.com/MacPython/scipy-stack-osx-testing 
>> by the way, which could have scikit-learn and scikit-image added to it.
>> >
>> > That's two options that are imho both better than adding more 
>> workload for the numpy release manager. Also from a principled point 
>> of view, packages should test with new versions of their 
>> dependencies, not the other way around.
>>
>> Sorry, that was unclear. I meant that we should finish the 
>> discussion, not that we should necessarily be the ones running the 
>> tests. "The discussion" being this one:
>>
>> https://github.com/numpy/numpy/issues/6462#issuecomment-148094591
>> https://github.com/numpy/numpy/issues/6494
>>
>> I'm not saying that the release manager necessarily should be running 
>> the tests (though it's one option). But the 1.10 experience seems to 
>> indicate that we need *some* process for the release manager to make 
>> sure that some basic downstream testing has happened. Another option 
>> would be keeping a checklist of downstream projects and making sure 
>> they've all checked in and confirmed that they've run tests before 
>> making the release.
>>
>> -n
>>
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