While it's not wholly surprising these two are slow, it is surprising they are *that* slow.

W.r.t. docstrings, I think there's room for adding a "skip_focstring" kwarg or some such to rv_generic. It'll need to be propagated to `rv_frozen.dist`. I can send a PR or one, if that helps.

(I don't know about pickling, sadly.)

All that said, maybe it's easier to share the shapes between processes and use regular distributions if rv_frozen is a bottleneck, will that help?

ср, 15 июл. 2020 г., 2:03 Andrew Nelson <andyfaff@gmail.com>:

I have some code that uses multiprocessing.Pool for parallelisation. This requires that an object is pickled. This object has an `rv_frozen` distribution as an attribute. It turns out that a performance is much improved if the `rv_frozen` distribution is not present --> pickling of `rv_frozen` objects is expensive. Creation of `rv_frozen` objects is also expensive.

```
>>> import scipy.stats as stats
>>> import pickle
>>> %timeit stats.norm(scale=1, loc=1)
694 µs ± 123 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> rv = stats.norm(scale=1, loc=1)
>>> %timeit s = pickle.dumps(rv); pickle.loads(s)
1.02 ms ± 24 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

I'd be hoping for an order of magnitude less in time for either of those. Using line profiling two of the big culprits for slowness during object creation are `rv_continuous._construct_doc` (50% of the total time, with a large part spent in `_lib.doccer.docformat`!!) and `rv_continuous._construct_argparser`

My questions are:

1) Is it possible to speed up pickling/unpickling of these objects? (e.g. __setstate__/__getstate__, custom reduction, copyreg magic, ...)
2) Is there any way to turn off docstring creation (or speeding it up), besides starting the interpreter with -OO?

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Dr. Andrew Nelson

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