One thing we should consider is maybe use only numpy 1.9 to simplify this. (I could not get numpy 1.7 to build with python >=3.5). What do you think @jjhelmus?

@conda-forge/core -- are we sure we want to do this? I agree that having fewer builds is better, but if I remember correctly, the original motivation for numpy pinning was that the ABI is not always compatible across releases.

If that has changed, great...and I know 100% for sure that I'm not up-to-date with the latest on numpy pinning. Mostly I'm writing based painful memories of about 4 years ago... 😰

This is the same strategy used for wheels and we need to watch for future numpy releases that may break the ABI, but we hope those will be well documented. For more info see @jjhelmus original PR to the pandas feedstock: conda-forge/pandas-feedstock#32

Thanks for the reference, @ocefpaf. A few more questions:

• How will this interact with defaults? For example, is conda-forge has astropy 2.0.1 with python, but not numpy, pinned, and defaults has both numpy and python pinned, which gets installed? Does that break anything?
• Will this change be compatible with what continuum plans to do in the future? That is not a show-stopper, but if we are trying to move towards using the same recipe for both we should make sure they are on board (I know @jjhelmus is at continuum now, so maybe this is already done).
• Can we ask for feedback on the conda-forge list and/or open a CFEP to get feedback (and advertise the change)?

This seems like a big enough change in how we build that we should commit to it cautiously, and maybe ask for helping in additional testing.

For example, is conda-forge has astropy 2.0.1 with python, but not numpy, pinned, and defaults has both numpy and python pinned, which gets installed? Does that break anything?

In theory it should not b/c the numpy chosen would still be compatible as long as (a) it is within the version range and, (b) the mkl vs openblas linkages are properly resolved. The latter is the same situation we have now BTW.

Will this change be compatible with what continuum plans to do in the future?

We discussed this extensively at SciPy and I believe that is the direction defaults is going. Still, once we move to conda-build 3, this will be relatively easier to do.

Can we ask for feedback on the conda-forge list and/or open a CFEP to get feedback (and advertise the change)?

The changes are actually more trivial than they seem. Not sure we need a CFEP, but if you really want we can compile the various texts in the issue and submit one.

Thanks for the additional explanation!

The changes are actually more trivial than they seem. Not sure we need a CFEP, but if you really want we can compile the various texts in the issue and submit one.

Given your comments then maybe just announce on the conda-forge mailing list. If this is an opt-in thing (i.e. we are not going to run a script to rewrite recipes), then agree that it is not very disruptive.

If this is an opt-in thing (i.e. we are not going to run a script to rewrite recipes), then agree that it is not very disruptive.

It is opt-in but highly recommended, particularly for big python x numpy matrices that take too long to build. Still, there is nothing preventing you to use numpy x.x for as long as you want.

Thanks for looking at this @mwcraig!

As noted in the pandas thread, I think trying to support all the way back to NumPy 1.7 is too ambitious. Currently we are unable to get such builds to link against OpenBLAS on macOS and I expect it will be a lot of work to make them work correctly. Only NumPy 1.9 seems to be built correctly. Would recommend that we simply drop NumPy 1.7. Then go with NumPy 1.9 and up for Python 2.7 instead. As a separate point, I think we should pull the broken NumPy 1.7 builds on macOS. ( conda-forge/numpy-feedstock#57 )

As noted in the pandas thread, I think trying to support all the way back to NumPy 1.7 is too ambitious.

I disagree that is ambitious b/c is works fine with all packages that supports np17, like matplolitb and pandas, and that does not links to openblas.

With that said I already mentioned that we should consider np19 as the lower bound for simplicity.

Currently we are unable to get such builds to link against OpenBLAS on macOS an I expect it will be a lot of work to make them work correctly. Only NumPy 1.9 seems to be built correctly. Would recommend that we simply drop NumPy 1.7. Then go with NumPy 1.9 and up for Python 2.7 instead. As a separate point, I think we should pull the broken NumPy 1.7 builds on macOS. ( conda-forge/numpy-feedstock#57 )

That is not an issue. See the logs of those builds for more information.

There is a concern regarding other blas than openblas. The only numpy without openblas is np17 and I could not find a single feedstock that could be built with np17 and links to openblas. Meaning that the use of np17 is safe to all who wants to support np17 and np18. (Also remember, conda-inspect is your best friend here!)

There seems to be some confusion on how this is supposed to work. This docs are examples only! And the guideline is "oldest possible numpy. So if your package does not support np17 go for np19, etc. (I am considering creating an np18 branch BTW. So maybe we could flatten the recommendation to a single numpy version.)

Merging this so people can modify the lingo here to fit their interpretations.

How does conda smithy rerender work with this? It shouldn't do anything since this only depends on the meta.yaml right?

Also how can I best test a package when I want to convert it to use this scheme?

How does conda smithy rerender work with this? It shouldn't do anything since this only depends on the meta.yaml right?

We do need to re-render with conda-smithy after removing the numpy x.x just to remove the numpy versions from the build matrix. See https://github.com/conda-forge/pandas-feedstock/pull/32/commits for an example.

Also how can I best test a package when I want to convert it to use this scheme?

Using that building scheme the package will be built with the older numpy and run against the latest so, you need to come up with tests that would fail in that scenario. Usually just importing or running some basic stuff is enough, but you can try to run the package test suit too.

PS: here is the latest version of this docs https://github.com/conda-forge/conda-forge.github.io/pull/421/files