Perhaps a pip + virtualenv build as well since that's one way that is mentioned in the online docs for installing source code. I can't think of anything else beyond that and what you suggested for the time being.
Greg On Tue, Jan 26, 2016 at 6:59 PM, Ralf Gommers <ralf.gomm...@gmail.com> wrote: > > > On Tue, Jan 26, 2016 at 2:13 AM, G Young <gfyoun...@gmail.com> wrote: > >> Ah, yes, that is true. That point had completely escaped my mind. In >> light of this, it seems that it's not worth the while then to completely >> switch over to pip + virtualenv. It's might be better actually to rewrite >> the current Appveyor tests to use environments so that the test suite can >> be expanded, though I'm not sure how prudent that is given how slow >> Appveyor tests run. >> > > At the moment Appveyor is already a bit of a bottleneck - it regularly > hasn't started yet when TravisCI is already done. This can be solved via a > paid account, we should seriously consider that when we have a bit more > experience with it (Appveyor tests have been running for less than a month > I think). But it does mean we should go for a sparse test matrix, and use a > more complete one (all Python versions for example) on TravisCI. In the > near future we'll have to add MingwPy test runs to Appveyor. Beyond that > I'm not sure what needs to be added? > > Ralf > > > >> >> Greg >> >> On Tue, Jan 26, 2016 at 12:13 AM, Bryan Van de Ven <bry...@continuum.io> >> wrote: >> >>> >>> > On Jan 25, 2016, at 5:21 PM, G Young <gfyoun...@gmail.com> wrote: >>> > >>> > With regards to testing numpy, both Conda and Pip + Virtualenv work >>> quite well. I have used both to install master and run unit tests, and >>> both pass with flying colors. This chart here illustrates my point nicely >>> as well. >>> > >>> > However, I can't seem to find / access Conda installations for >>> slightly older versions of Python (e.g. Python 3.4). Perhaps this is not >>> much of an issue now with the next release (1.12) being written only for >>> Python 2.7 and Python 3.4 - 5. However, if we were to wind the clock >>> slightly back to when we were testing 2.6 - 7, 3.2 - 5, I feel Conda falls >>> short in being able to test on a variety of Python distributions given the >>> nature of Conda releases. Maybe that situation is no longer the case now, >>> but in the long term, it could easily happen again. >>> >>> Why do you need the installers? The whole point of conda is to be able >>> to create environments with whatever configuration you need. Just pick the >>> newest installer and use "conda create" from there: >>> >>> bryan@0199-bryanv (git:streaming) ~/work/bokeh/bokeh $ conda create -n >>> py26 python=2.6 >>> Fetching package metadata: .............. >>> Solving package specifications: .......... >>> Package plan for installation in environment >>> /Users/bryan/anaconda/envs/py26: >>> >>> The following packages will be downloaded: >>> >>> package | build >>> ---------------------------|----------------- >>> setuptools-18.0.1 | py26_0 343 KB >>> pip-7.1.0 | py26_0 1.4 MB >>> ------------------------------------------------------------ >>> Total: 1.7 MB >>> >>> The following NEW packages will be INSTALLED: >>> >>> openssl: 1.0.1k-1 >>> pip: 7.1.0-py26_0 >>> python: 2.6.9-1 >>> readline: 6.2-2 >>> setuptools: 18.0.1-py26_0 >>> sqlite: 3.9.2-0 >>> tk: 8.5.18-0 >>> zlib: 1.2.8-0 >>> >>> Proceed ([y]/n)? >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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