[Numpy-discussion] Numpy 1.11.0b2 released

[Nathaniel Smith]

On Jan 30, 2016 9:27 AM, "Ralf Gommers" <ralf.gommers at gmail.com> wrote:
>
>
>
> On Fri, Jan 29, 2016 at 11:39 PM, Nathaniel Smith <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> 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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