[Numpy-discussion] linux wheels coming soon

Mon Apr 4 14:26:26 EDT 2016

Matthew, you are correct.  A lot of things happened with random integer
generation recently (including deprecating random_integers), but I believe
those warnings should be squashed in the up and coming version of SciPy
from what I remember.

On Mon, Apr 4, 2016 at 6:47 PM, Matthew Brett <matthew.brett at gmail.com>
wrote:

> Hi,
>
> On Mon, Apr 4, 2016 at 9:02 AM, Peter Cock <p.j.a.cock at googlemail.com>
> wrote:
> > On Sun, Apr 3, 2016 at 2:11 AM, Matthew Brett <matthew.brett at gmail.com>
> wrote:
> >> On Fri, Mar 25, 2016 at 6:39 AM, Peter Cock <p.j.a.cock at googlemail.com>
> wrote:
> >>> On Fri, Mar 25, 2016 at 3:02 AM, Robert T. McGibbon <
> rmcgibbo at gmail.com> wrote:
> >>>> I suspect that many of the maintainers of major scipy-ecosystem
> projects are
> >>>> aware of these (or other similar) travis wheel caches, but would
> guess that
> >>>> the pool of travis-ci python users who weren't aware of these wheel
> caches
> >>>> is much much larger. So there will still be a lot of travis-ci clock
> cycles
> >>>> saved by manylinux wheels.
> >>>>
> >>>> -Robert
> >>>
> >>> Yes exactly. Availability of NumPy Linux wheels on PyPI is definitely
> something
> >>> I would suggest adding to the release notes. Hopefully this will help
> trigger
> >>> a general availability of wheels in the numpy-ecosystem :)
> >>>
> >>> In the case of Travis CI, their VM images for Python already have a
> version
> >>> of NumPy installed, but having the latest version of NumPy and SciPy
> etc
> >>> available as Linux wheels would be very nice.
> >>
> >> We're very nearly there now.
> >>
> >> The latest versions of numpy, scipy, scikit-image, pandas, numexpr,
> >> statsmodels wheels for testing at
> >>
> http://ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com/
> >>
> >> Please do test with:
> >> ...
> >>
> >> We would love to get any feedback as to whether these work on your
> machines.
> >
> > Hi Matthew,
> >
> > Testing on a 64bit CentOS 6 machine with Python 3.5 compiled
> > from source under my home directory:
> >
> >
> > $ python3.5 -m pip install --upgrade pip
> > Requirement already up-to-date: pip in ./lib/python3.5/site-packages
> >
> > $ python3.5 -m pip install
> > --trusted-host=
> ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com
> > --find-links=
> http://ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com
> > numpy scipy
> > Requirement already satisfied (use --upgrade to upgrade): numpy in
> > ./lib/python3.5/site-packages
> > Requirement already satisfied (use --upgrade to upgrade): scipy in
> > ./lib/python3.5/site-packages
> >
> > $ python3.5 -m pip install
> > --trusted-host=
> ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com
> > --find-links=
> http://ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com
> > numpy scipy --upgrade
> > Collecting numpy
> >   Downloading
> http://ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com/numpy-1.11.0-cp35-cp35m-manylinux1_x86_64.whl
> > (15.5MB)
> >     100% |████████████████████████████████| 15.5MB 42.1MB/s
> > Collecting scipy
> >   Downloading
> http://ccdd0ebb5a931e58c7c5-aae005c4999d7244ac63632f8b80e089.r77.cf2.rackcdn.com/scipy-0.17.0-cp35-cp35m-manylinux1_x86_64.whl
> > (40.8MB)
> >     100% |████████████████████████████████| 40.8MB 53.6MB/s
> > Installing collected packages: numpy, scipy
> >   Found existing installation: numpy 1.10.4
> >     Uninstalling numpy-1.10.4:
> >       Successfully uninstalled numpy-1.10.4
> >   Found existing installation: scipy 0.16.0
> >     Uninstalling scipy-0.16.0:
> >       Successfully uninstalled scipy-0.16.0
> > Successfully installed numpy-1.11.0 scipy-0.17.0
> >
> >
> > $ python3.5 -c 'import numpy; numpy.test("full")'
> > Running unit tests for numpy
> > NumPy version 1.11.0
> > NumPy relaxed strides checking option: False
> > NumPy is installed in /home/xxx/lib/python3.5/site-packages/numpy
> > Python version 3.5.0 (default, Sep 28 2015, 11:25:31) [GCC 4.4.7
> > 20120313 (Red Hat 4.4.7-16)]
> > nose version 1.3.7
> >
> 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> > ----------------------------------------------------------------------
> > Ran 6332 tests in 243.029s
> >
> > OK (KNOWNFAIL=7, SKIP=2)
> >
> >
> >
> > So far so good, but there are a lot of deprecation warnings etc from
> SciPy,
> >
> >
> > $ python3.5 -c 'import scipy; scipy.test("full")'
> > Running unit tests for scipy
> > NumPy version 1.11.0
> > NumPy relaxed strides checking option: False
> > NumPy is installed in /home/xxx/lib/python3.5/site-packages/numpy
> > SciPy version 0.17.0
> > SciPy is installed in /home/xxx/lib/python3.5/site-packages/scipy
> > Python version 3.5.0 (default, Sep 28 2015, 11:25:31) [GCC 4.4.7
> > 20120313 (Red Hat 4.4.7-16)]
> > nose version 1.3.7
> > [snip]
> > /home/xxx/lib/python3.5/site-packages/numpy/lib/utils.py:99:
> > DeprecationWarning: `rand` is deprecated!
> > numpy.testing.rand is deprecated in numpy 1.11. Use numpy.random.rand
> instead.
> >   warnings.warn(depdoc, DeprecationWarning)
> > [snip]
> >
> /home/xxx/lib/python3.5/site-packages/scipy/io/arff/tests/test_arffread.py:254:
> > DeprecationWarning: parsing timezone aware datetimes is deprecated;
> > this will raise an error in the future
> >   ], dtype='datetime64[m]')
> > /home/xxx/lib/python3.5/site-packages/scipy/io/arff/arffread.py:638:
> > PendingDeprecationWarning: generator '_loadarff.<locals>.generator'
> > raised StopIteration
> > [snip]
> >
> /home/xxx/lib/python3.5/site-packages/scipy/sparse/tests/test_base.py:2425:
> > DeprecationWarning: This function is deprecated. Please call
> > randint(-5, 5 + 1) instead
> >   I = np.random.random_integers(-M + 1, M - 1, size=NUM_SAMPLES)
> > [snip]
> > 0-th dimension must be fixed to 3 but got 15
> > [snip]
> > ----------------------------------------------------------------------
> > Ran 21407 tests in 741.602s
> >
> > OK (KNOWNFAIL=130, SKIP=1775)
> >
> >
> > Hopefully I didn't miss anything important in hand editing the scipy
> output.
>
> Thanks a lot for testing.
>
> I believe the deprecation warnings are expected, because numpy 1.11.0
> introduced a new deprecation warning when using `random_integers`.
> Scipy 0.17.0 is using `random_integers` in a few places.
>
> Best,
>
> Matthew
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>
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