This post is by way of trying to articulate some concerns from the end
user side of things.
It looks like NumPy 19.1 supports Python 3.6.
> This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be used when building
> with Python 3.9 for testing purposes.
https://numpy.org/devdocs/release/1.19.1-notes.html
The section of the release notes for NumPy 1.20.0 does not contain the
section at the top saying which versions of Python are supported.
(https://numpy.org/devdocs/release/1.20.0-notes.html)
For Python itself, I find on their releases, the 3.6.12 schedule was
3.6.12 final: 2020-08-14 (expected)
https://www.python.org/dev/peps/pep-0494/#id9
but does not seem to have made it, and Python 3.6.11 was released June
27, 2020. The Python plans for 3.6.13 and beyond are Security fixes
only, as needed, until 2021-12. That seems, from my user perspective,
a better date for deprecation and not too far from the just proposed
one.
Acknowledging the additional burden of 'supporting' older versions of
Python, still it would seem that matching NumPy is not a bad thing.
>From a strictly consumer perspective, where much of the work is
getting all of the non-NumPy and non-SciPy functionality to work and
be stable, upgrading Python can be very disruptive. Time spent
getting the 'glue' around analytics to work is time we cannot spend on
science. There are large projects that do keep up, but they tend also
to be funded well. The many small labs on my campus do not have
funding for software development, are unlikely to ever get any, so any
work required to fix software because of updates comes from their
budget for science and from their scientists who are not as proficient
and therefore not as efficient at adapting to a newer version of
Python and all the reinstallation of libraries that attends it.
If there were a way to provide _only_ actual bug fixes for some
versions longer, a la the LTS releases of some Linux distributions,
that might be of benefit to users and would reduce the support burden
on developers who would not need to worry about fitting new features
into the older framework.
All of that is not to say that you should not continue with current
deprecation plans. Just to be sure that these concerns were voiced at
least once. I hope that this perspective is of some use in your
considerations.
Thanks for your thoughts Bennet. All of this was brought up in the NEP 29 discussion which recommended timelines for Python and NumPy version support. I don't think any of it is specific to SciPy. It's a trade-off: users may want perfect stability and updates for whatever version they're on for as long as possible, but that comes at the cost of making maintainers doing tedious work, like more release effort and figuring out why CI fails on some particular combination of old Python and NumPy versions. That tedious work then reduces the amount of time for tackling more relevant bugs and adding new features, and it likely also reduces total effort spent by maintainers over time (most people do this voluntarily, so motivation comes partly from enjoying to contribute).
Cheers,
Ralf
Thanks for all the software, regardless!
On Sat, Aug 15, 2020 at 6:48 PM Ralf Gommers <ralf.gommers@gmail.com> wrote:
>
> Hi all,
>
> Python 3.9 is coming out before the next SciPy release, and NEP 29 recommends to support only Python >=3.7 and NumPy >= 1.16 from last month onwards [1]. I think supporting 3 Python versions and 5 NumPy versions in SciPy 1.6.0 is easily enough. That would also bring us back in line with SciPy on conda-forge, which built against NumPy 1.16 for 1.5.2.
>
> Any objections?
>
> Cheers,
> Ralf
>
> [1] https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table
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