Numpy pinning going forward about conda-forge-pinning-feedstock HOT 10 OPEN

Wouldn't it be better to deal with it for 2.0? That's less than 6 months away, and at that point there is a hard necessity to deal with C API/ABI stuff.

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Yeah, that's part of what I wanted to discuss here, not just the backwards compat by default, but also 2.0.

It also doesn't need an immediate decision, there's no urgency AFAICT.

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I think this will be useful with the 2.0 release, we could pin to 2 and set the environment variables like we do for the C compilers at build time.

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Reading this, I see the drawback, that we will have an activation script with numpy and thus some (unexpected) hurdles for maintainers with numpy as a build dependency (if they want a newer numpy version). What would be the benefit of providing numpy=1.25 as the default? I don't see it.

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It's perhaps possible to do this without an activation script, that was just the first thing that came to mind...

What would be the benefit of providing numpy=1.25 as the default?

I don't have a strong argument (or preference) here. But whenever we get to numpy>=1.25 as a default, we'd IMO have to adapt the run-export. It would also be a bit weird to jump from (a future) >=1.24 back to >=1.19 (based on the API default of 1.25), but I guess that could be a one-time transition. It also wouldn't match NEP 29 anymore...

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I would argue to not move away from the current setup. Even if we set NPY_TARGET_VERSION using an environment variable, there are 2 issues.

1. It might not get picked up by the build system.
2. If a project itself sets NPY_TARGET_VERSION, the metadata will not be correct.

However if we build with NumPy 1.25 and have >=1.25, we are guaranteed that the metadata is correct even though it could have been looser.

(This is exactly what we do with macos SDK and deployment target by setting them to the same version by default. For eg: if SDK = 11 and target = 10.9, the symbols introdued in 10.15 are visible, but they need to treated as weak symbols in 10.9 which require the developer to handle it correctly in their C/C++ code)

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However if we build with NumPy 1.25 and have >=1.25, we are guaranteed that the metadata is correct even though it could have been looser.

Also, a looser pin in that case is not necessary better. Most users will want an updated numpy anyway and having that in place will make it easier (faster) for the solver to provide a solution with it.

Sure, there may be a small portion of users who may need older numpy and won't be able to install it but I believe the advantages outweigh the disadvantages.

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If a project itself sets NPY_TARGET_VERSION, the metadata will not be correct.

Isn't that a general problem that we'll have to look out for in any case?

I'm not sure if that is something we could easily determine from a compiled artefact (numpy does embed the C-API level AFAIK), but it seems it would be good to check after building what numpy target version got used

That way we could verify that things didn't get lost or overridden by the project or the build system.

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Isn't that a general problem that we'll have to look out for in any case?

No. See my comment highlighted below

However if we build with NumPy 1.25 and have >=1.25, we are guaranteed that the metadata is correct even though it could have been looser.

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I spoke with @rgommers recently, and he mentioned one thing about this that wasn't clear to me before:

Packages compiled with numpy 2.0 will continue to be compatible with the 1.x ABI.

In other words, if this works out as planned, we could support numpy 2.0 right away without having to do a full CI-bifurcation of all numpy-dependent packages. It would mean using 2.0 as a baseline earlier than we'd do it through NEP29, but given the now built-in backwards compatibility, we could set the pinning to 2.0, and manually set the numpy run-export to do something like numpy >=1.19 (which apparently won't be changed until numpy drops python 3.9 support).

No. See my comment highlighted below

I wasn't talking about the tightness/looseness of the constraints, but about projects setting NPY_TARGET_VERSION in their build scripts somewhere, which has the potential to conflict (in terms of expectations, not constraints) with whatever we do.

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