[SciPy-Dev] Numba as a dependency for SciPy?
Stanley Seibert
sseibert at anaconda.com
Thu Mar 8 09:44:04 EST 2018
TBH, I agree with this general sentiment. This thread has been very
valuable to clarify the Numba team's understanding of what the SciPy
community needs from a compilation solution. Our trajectory is good, but
we're not quite there yet for a project that needs to be as conservative
about dependencies as SciPy. We will keep working to get there, though.
However, if someone is interested in trying to implement some SciPy
functions with Numba implementations, there's nothing blocking that work in
a separate repository as an experiment. (One of the Numba developers has
already named this hypothetical project "Scumba," which I quite like.) If
anyone does decide to try this, please make sure to ping the Numba
developers on Gitter. We will learn a great deal from the effort, I think.
On Thu, Mar 8, 2018 at 4:00 AM, Matthew Brett <matthew.brett at gmail.com>
wrote:
> Hi,
>
> On Thu, Mar 8, 2018 at 9:38 AM, Pauli Virtanen <pav at iki.fi> wrote:
> > Ralf Gommers kirjoitti 08.03.2018 klo 08:04:
> > [clip]
> >>
> >> Also, I don't think performance will necessarily be unacceptable. There
> >> are
> >> a bunch of places in the existing code base where we can throw in @jit
> and
> >> get speedups basically for free. Performance in the noop case will then
> be
> >> what it is today - not great, but apparently also not enough of a
> problem
> >> that someone has attempted to go to Cython.
> >
> >
> > I guess you agree that Numba would regardless be declared a dependency in
> > setup.py? People on unsupported arches can edit it away manually.
> >
> > For computational tight loops operating on arrays when Numba is used as
> an
> > alternative to Cython/C/Fortran, there probably will be a performance
> hit in
> > the ballpark of 100x.
> >
> > If we are planning to use numba features more fully, e.g. numba.cfunc
> e.g.
> > to write callback functions, that would also require Numba as a hard
> > dependency.
>
> If we were at the top of the stack, like pystatsmodels, then this
> would be reasonable, but, if we make numba a dependency, that makes
> numba a dependency for almost anyone doing scientific computing. I
> think we do have to care about people not running on Intel. If we
> make numba an optional dependency, it gives us an additional
> maintenance burden, because we'd have to check for each numba segment,
> whether it is going to be disabling for a user without numba.
>
> Is there anything we have at the moment where Cython won't get us into
> the ballpark? If not, my preference would be to wait for a year or
> so, to see how things turn out.
>
> Cheers,
>
> Matthew
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