[Numpy-discussion] big-bangs versus incremental improvements (was: Re: SciPy 2014 BoF NumPy Participation)

Nathaniel Smith njs at pobox.com
Thu Jun 5 10:04:42 EDT 2014


On Thu, Jun 5, 2014 at 2:29 AM, Travis Oliphant <travis at continuum.io> wrote:
> At some point there *will* be a NumPy 2.0.   What features go into NumPy
> 2.0, how much backward compatibility is provided, and how much porting is
> needed to move your code from NumPy 1.X to NumPy 2.X is the real user
> question --- not whether it is characterized as "incremental" change or
> "re-write".

There may or may not ever be a numpy 2.0. Maybe there will be a numpy
1.20 instead. Obviously there will be changes, and I think we
generally agree on the end goal, but the question is how we get from
here to there.

> What I call a re-write and what you call an
> "incremental-change" are two points on a spectrum and likely overlap
> signficantly if we really compared what we are thinking about.
[...]
> Ultimately, I don't disagree that NumPy can continue to exist in
> "incremental" change mode ( though if you are swapping out whole swaths of
> C-code for Cython code --- it sounds a lot like a "re-write") as long as
> there are people willing to put the effort into changing it.

This is why I'm trying to emphasize the a contrast between big-bang
versus incremental, rather than rewrite-versus-not-rewrite. If Theseus
goes through replacing every timber in his ship, and does it one at a
time, then the boat still floats. If he tries to do it all at once,
then the end goal may be the same but the actual results are rather
different.

And perception matters. If we set out to design "numpy 2.0" then that
conversation will go one way. If we set out to design "numpy 1.20",
then the conversation will be different. I want to convince people
that the numpy 1.20 approach is a worthwhile place to put our efforts.

> I think this
> is actually benefited by the existence of other array objects that are
> pushing the feature envelope without the constraints --- in much the same
> way that the Python standard library is benefitted by many versions of
> different capabilities being tried out before moving into the standard
> library.

Indeed!

-n

-- 
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org



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