[Numpy-discussion] Behavior of .base
Dag Sverre Seljebotn
Tue Oct 2 02:38:11 CDT 2012
On 10/01/2012 04:56 PM, Charles R Harris wrote:
> On Mon, Oct 1, 2012 at 8:40 AM, Thouis (Ray) Jones <email@example.com
> <mailto:firstname.lastname@example.org>> wrote:
> On Mon, Oct 1, 2012 at 8:20 AM, Nathaniel Smith <email@example.com
> <mailto:firstname.lastname@example.org>> wrote:
> > [...]
> > How can we discourage people from doing this in the future? Can we
> > make .base write-only from the Python level (with suitable
> > period)? Rename it to ._base (likewise) so that it's still
> possible to
> > peek under the covers but we remind people that it's really an
> > implementation detail with poorly defined semantics that might
> Could we use the simpler .base behavior (fully collapsing the .base
> chain), but be more aggressive about propagating information like
> address/filename/offset for np.arrays that are created by slicing,
> asarray(), etc.?
> (Sorry if I'm missing some context that makes this suggestion idiotic.
> I'm still trying to catch back up on the list and may have missed
> relevant discussion on other threads.)
> It might be productive to step back a bit and ask if this is a memmap
> problem or a workflow problem. My impression is that pickling memmaps is
> a solution to a higher level problem in Scikits.learn workflow and I'd
> like more details on what that problem is.
I'm not scikits-learn, but I'm pretty sure this is about wanting to use
multiprocessing to parallelise code. You send pickled views of arrays,
but the memory is shared amongst all processes (using either a file, or
process shared memory).
It would be cool to have some support for this in NumPy itself. The
scikits-learn people should chime in here, but a suggestion:
# pickles by reference to process-shared memory, or raises an exception
# if memory can't be process-shared
s = dumps(arr.byref)
# in another process:
arr = loads(s)
Of course, *real* fixes would be to remove the GIL, or push forward the
work in CPython on multiple independent interpreters in the same
process. But that's rather more difficult.
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