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.

-JB

On Mon, Feb 20, 2012 at 2:41 PM, Lluís <xscript@gmx.net> 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" translate
> regular ndarray operations to numexpr strings (or others) using only python
> 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 performed
> something similar (w.r.t. generating cuda kernels out of python expressions).

Aaahhh, I just had a quick look at Theano and it seems it's the project I was
referring to.

Good job! :)


Lluis

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