[Numpy-discussion] linux wheels coming soon

[Jonathan Helmus]

On 4/14/16 1:26 PM, Matthew Brett wrote:
> Hi,
>
> On Thu, Apr 14, 2016 at 11:11 AM, Benjamin Root <ben.v.root at gmail.com> wrote:
>> Are we going to have to have documentation somewhere making it clear that
>> the numpy wheel shouldn't be used in a conda environment? Not that I would
>> expect this issue to come up all that often, but I could imagine a scenario
>> where a non-scientist is simply using a base conda distribution because that
>> is what IT put on their system. Then they do "pip install ipython" that
>> indirectly brings in numpy (through the matplotlib dependency), and end up
>> with an incompatible numpy because they would have been linked against
>> different pythons?
>>
>> Or is this not an issue?
> I'm afraid I don't know conda at all, but I'm guessing that pip will
> not install numpy when it is installed via conda.
Correct, pip will not (or at least should not, and did not in my tests) 
install numpy over top of an existing conda installed numpy. 
Unfortunately from my testing, conda will install a conda version of 
numpy over top of a pip installed version.  This may be the expected 
behavior as conda maintains its own list of installed packages.
> So the potential difference is that, pre-wheel, if numpy was not
> installed in your conda environment, then pip would build numpy from
> source, whereas now you'll get a binary install.
>
> I _think_ that Python's binary API specification
> (pip.pep425tags.get_abi_tag()) should prevent pip from installing an
> incompatible wheel.  Are there any conda experts out there who can
> give more detail, or more convincing assurance?
I tested "pip install numpy" in conda environments (conda's equivalent 
to virtualenvs) which did not have numpy installed previously for Python 
2.7, 3.4 and 3.5 in a Ubuntu 14.04 Docker container.  In all cases numpy 
was installed from the whl file and appeared to be functional.  Running 
the numpy test suite found three failing tests for Python 2.7 and 3.5 
and 21 errors in Python 3.4. The 2.7 and 3.5 failures do not look 
concerning but the 3.4 errors are a bit strange.
Logs are in 
https://gist.github.com/jjhelmus/a433a66d56fb0e39b8ebde248ad3fe36


Cheers,

     - Jonathan Helmus
>
> Cheers,
>
> Matthew
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