[scikit-image] 回复： Numba on pypi

[imagepy at sina.com]

Hi Stéfan:
  I appreciate Numba. for sometimes, we must do a 'for' in our python code, but just a 'for' with a 'if', It is fussy to compile a so/dll or write cython. Numba is very portable, and can run anywhere, just need to install numba and llvmlite. (That means our package could be a light-weight library, undepended any native so/dll)
  As I metioned befor, many scikit-image's algrisms are not exquisite enough(just my own opinion),      the mid_axi function results too many branch and sometimes with hols,        the local_max has not a tolerance, so the result is too massy, then do a watershed end with too many fragments...,      and how to build a graph from the skeleton, then do a network analysis.
I want to do a contribute to scikit-image, But after some effort, I give up, I prefor to write a dynamic lib rather then Cython. In the end, I wrote them in Numba. So I appreciate to use Numba.
BestYXDragon
----- 原始邮件 -----
发件人：Stefan van der Walt <stefanv at berkeley.edu>
收件人：scikit-image at python.org
主题：[scikit-image] Numba on pypi
日期：2017年07月13日 14点17分

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.
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/).
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, 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
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