[Numpy-discussion] Created NumPy 1.7.x branch
Wed Jun 27 01:02:41 CDT 2012
I do understand the issues around ABI breakage. I just want to speak up for the people who are affected by API breakage who are not as vocal on this list. I believe we should have similar frustration and concern at talk of API breakage as there is about talk of ABI breakage.
On Jun 27, 2012, at 12:59 AM, Fernando Perez wrote:
> On Tue, Jun 26, 2012 at 10:25 PM, Ralf Gommers
> <email@example.com> wrote:
>> On Tue, Jun 26, 2012 at 5:33 AM, Travis Oliphant <firstname.lastname@example.org>
>>> What should have happened in this case, in my mind, is that NumPy 1.4.0
>>> should have been 1.5.0 and advertised that there was a break in the ABI and
>>> that all extensions would have to be re-built against the new version.
>>> This would have been some pain for one class of users (primarily package
>>> maintainers) and no pain for another class.
>> Please please stop asserting this. It's plain wrong. It has been explained
>> to you multiple times by multiple people how bad the consequences of
>> breaking the ABI are. It leads to random segfaults when existing installers
>> are not updated or when users pick the wrong installer by accident (which
>> undoubtedly some will). It also leads to a large increase in the number of
>> installers that maintainers for every single package that depends on numpy
>> will have to build. Including for releases they've already made in the past.
> An additional perspective on the issue of ABI breakage: even for those
> of us who live in a distro-managed universe (ubuntu in my case), the
> moment numpy breaks ABI means that it becomes *much* harder to use the
> new numpy because I'd have to start recompiling all binary
> dependencies, some of which are not pleasant to start rebuilding (such
> as VTK for mayavi). So that means I'm much less likely to use an
> ABI-incompatible numpy for everyday work, and therefore less likely to
> find bugs, report them, etc. I typically run dev versions of numpy,
> scipy and matplotlib all the time, except when numpy breaks ABI,
> which means I have to 'pin' numpy to the system one and only update
> the others.
> Now, obviously that doesn't mean that ABI can never be broken, but
> it's just another data point for you as you evaluate the cost of ABI
> breakage. It is significant even for those who operate under the
> benefit of managed packages, because numpy is effectively the root
> node of the dependency tree for virtually all scientific python
> I hope this is useful as additional data on the issue.
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