On Thu, Jul 13, 2017 at 6:16 PM, Stefan van der Walt <stef...@berkeley.edu> wrote:
> Hi everyone, > > As many of you know, speed has been a point of contention in > scikit-image for a long time. We've made a very deliberate decision to > focus on writing high-level, understandable code (via Python and > Cython): both to lower the barrier to entry for newcomers, and to lessen > the burden on maintainers. But execution time comparisons, vs OpenCV > e.g., left much to be desired. > > I think we have hit a turning point in the road. Binary wheels for > Numba (actually, llvmlite) were recently uploaded to PyPi, making this > technology available to users on both pip and conda installations. The > importance of this release on pypi should not be dismissed, and I am > grateful to the numba team and Continuum for making that decision. > Agreed. Note that there are no Windows wheels up on PyPI (yet, or not coming?). Given that there are no SciPy wheels for Windows either I don't think that that changes your argument much - people should just use a binary distribution on Windows - but I thought I'd point it out anway. > > So, how does that impact scikit-image? Well, imagine we choose to > optimize various procedures via numba (see Juan's blog post for exactly > how impactful this can be: > https://ilovesymposia.com/2017/03/15/prettier- > lowlevelcallables-with-numba-jit-and-decorators/). > That's a great post. @Juan: get yourself on Planet Python! > The only question we have to answer (from a survival point of view) > needs to be: if, somehow, something happens to numba, will an > alternative will be available at that time? Looking at the Python JIT > landscape (which is very active), and the current state of numba > development, I think this is likely. And, if we choose to use numba, of > course we'll help to keep it healthy, as far as we can. > > I'd love to hear your thoughts. I'm only an occasional user at the moment, so won't express an opinion either way. But will be following this thread with interest. Cheers, Ralf > I, for one, am excited about the > prospect of writing kernels as simply as: > > >>> @jit_filter_function > ... def fmin(values): > ... result = np.inf > ... for v in values: > ... if v < result: > ... result = v > ... return result > > >>> ndi.generic_filter(image, fmin, footprint=fp) > > Best regards > Stéfan > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image >
_______________________________________________ scikit-image mailing list scikit-image@python.org https://mail.python.org/mailman/listinfo/scikit-image
