[Numpy-discussion] Proposed Roadmap Overview
Mon Feb 20 14:01:33 CST 2012
Looks like Dag forked the discussion of lazy evaluation to a new thread
([Numpy-discussion] ndarray and lazy evaluation).
There are actually several projects inspired by this sort of design: off
the top of my head I can think of Theano, copperhead, numexpr, arguably
sympy, and some non-public code by Nicolas Pinto. So I think the strengths
of the approach in principle are established... the big question is how to
make this approach easy to use in all the settings where it could be
useful. I don't think any of these projects has gotten that totally right.
On Mon, Feb 20, 2012 at 2:41 PM, Lluís <email@example.com> wrote:
> Lluís writes:
> > Francesc Alted writes:
> >> On Feb 20, 2012, at 6:18 PM, Dag Sverre Seljebotn wrote:
> >>> You need at least a slightly different Python API to get anywhere, so
> >>> numexpr/Theano is the right place to work on an implementation of this
> >>> idea. Of course it would be nice if numexpr/Theano offered something as
> >>> convenient as
> >>> with lazy:
> >>> arr = A + B + C # with all of these NumPy arrays
> >>> # compute upon exiting…
> >> Hmm, that would be cute indeed. Do you have an idea on how the code in
> the with
> >> context could be passed to the Python AST compiler (à la
> numexpr.evaluate("A + B
> >> + C"))?
> > Well, I started writing some experiments to "almost transparently"
> > regular ndarray operations to numexpr strings (or others) using only
> > code.
> > My target was to use this to also generate optimized GPU kernels
> in-flight using
> > pycuda, but I think some other relatively recent project already
> > something similar (w.r.t. generating cuda kernels out of python
> Aaahhh, I just had a quick look at Theano and it seems it's the project I
> referring to.
> Good job! :)
> "And it's much the same thing with knowledge, for whenever you learn
> something new, the whole world becomes that much richer."
> -- The Princess of Pure Reason, as told by Norton Juster in The Phantom
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