[Numpy-discussion] locking np.random.Generator in a cython nogil context?

[Evgeni Burovski]

On Thu, Dec 17, 2020 at 1:01 PM Matti Picus <matti.picus at gmail.com> wrote:
>
>
> On 12/17/20 11:47 AM, Evgeni Burovski wrote:
> > Just as a side note, this is not very prominent in the docs, and I'm
> > ready to volunteer to send a doc PR --- I'm only not sure which part
> > of the docs, and would appreciate a pointer.
>
> Maybe here
>
> https://numpy.org/devdocs/reference/random/bit_generators/index.html#seeding-and-entropy
>
> which is here in the sources
>
> https://github.com/numpy/numpy/blob/master/doc/source/reference/random/bit_generators/index.rst#seeding-and-entropy
>
>
> And/or in the SeedSequence docstring documentation
>
> https://numpy.org/devdocs/reference/random/bit_generators/generated/numpy.random.SeedSequence.html#numpy.random.SeedSequence
>
> which is here in the sources
>
> https://github.com/numpy/numpy/blob/master/numpy/random/bit_generator.pyx#L255


Here's the PR, https://github.com/numpy/numpy/pull/18014

Two minor comments, both OT for the PR:

1. The recommendation to seed the generators from the OS --- I've been
bitten by exactly this once. That was a rather exotic combination of a
vendor RNG and a batch queueing system, and some of my runs did end up
with identical random streams. Given that the recommendation is what
it is, it probably means that experience is a singular point and it no
longer happens with modern generators.

2. Robert's comment that `SeedSequence(..., spawn_key=(num,))`  is not
equivalent to `SeedSequence(...).spawn(num)[num]` and that the former
is not recommended. I'm not questioning the recommendation, but then
__repr__ seems to suggest the equivalence:

In [2]: from numpy.random import PCG64, SeedSequence

In [3]: base_seq = SeedSequence(1234)

In [4]: base_seq.spawn(8)
Out[4]:
[SeedSequence(
     entropy=1234,
     spawn_key=(0,),
 ),

<snip>

Evgeni