[pypy-dev] pickle numpy array from pypy to cpython?

[David Brochart]

Hi,

I'd like to use the (numerical) performances of PyPy as an equivalent to
Numba's @jit decorator (https://github.com/davidbrochart/piopio). The only
thing preventing that right now is the passing around (pickling) of Numpy
arrays, so it would be great to have that compatibility.

David.

On Mon, Jul 11, 2016 at 6:43 PM, Eli Stevens (Gmail) <wickedgrey at gmail.com>
wrote:

> FWVLIW, I think that conforming to upstream numpy makes the most sense.
>
> I think that the issue would go away if the `_numpypy` module were
> renamed to `numpy` (and appropriate things moved into `numpy.core`).
> Is there a technical reason to keep the actual implementation in a
> separately named module?
>
> Thinking larger picture, would it be possible and sensible to switch
> to using the slow cpyext numpy approach for compatability, then
> overlay custom implementation of things on top of that when speed is
> needed?  I'm imagining a vague inverse of the cpython approach, where
> modules are implemented in C when the python performance isn't
> acceptable.
>
> Eli
>
> On Wed, Jun 29, 2016 at 10:58 PM, Armin Rigo <arigo at tunes.org> wrote:
> > Hi Eli, hi Matti,
> >
> > On 29 June 2016 at 21:37, Eli Stevens (Gmail) <wickedgrey at gmail.com>
> wrote:
> >> To make sure I'm understanding, are you saying that upstream/cpython
> >> numpy should pick up an alternate way to import multiarray (via
> >> _numpypy.multiarray, instead of numpy.core.multiarray)
> >
> > Hum, in my opinion we should always pickle/unpickle arrays by
> > reproducing and expecting the exact same format as CPython's numpy,
> > with no warnings.  Any difference (e.g. with complicated dtypes) is a
> > bug that should eventually be fixed.
> >
> >
> > A bientôt,
> >
> > Armin.
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