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Fri Oct 12 10:49:28 EDT 2018 · https://mail.python.org/pipermail/python-dev/2018-October/155490.html

Yes - “func” (and “self” which func is bound to) would be copied to each
child worker process, where they are stored and applied to each element of
the iterable being mapped over.
On Fri, Oct 12, 2018 at 10:41 AM Antoine Pitrou <solipsis at pitrou.net> wrote:

> On Fri, 12 Oct 2018 09:42:50 -0400
> Sean Harrington <seanharr11 at gmail.com> wrote:
> > I would contend that this is much more granular than Dask - this is just
> an
> > optimization of Pool.map() to avoid redundantly passing the same `func`
> > repeatedly, once per task, to each worker, with the primary goal of
> > eliminating redundant serialization of large-memory-footprinted
> Callables.
> > This is a different use case than Dask - I don't intend to approach the
> > shared memory or distributed computing realms.
> >
> > And the second call to Pool.map would update the cached "self" as a part
> of
> > its initialization workflow, s.t. "the latest version of self when map()
> is
> > called is taken into account".
>
> I still don't understand how that works.  If you "updated the cached
> self", then surely you must transmit it to the child, right?
>
> Regards
>
> Antoine.
>
>
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Showing content from https://mail.python.org/pipermail/python-dev/2018-October/155490.html below:

[Python-Dev] bpo-34837: Multiprocessing.Pool API Extension