[shoyer]

This post is yet another example of why you should never use APIs for random number generation that rely upon and mutate hidden global state, like the functions in numpy.random. Instead, use APIs that explicitly deal with RNG state, e.g., by calling methods on an explicitly created numpy.random.Generator object. JAX takes this one step further: there are no mutable RNG objects at all, and the users has to explicitly manipulate RNG state with pure functions.

It’s a little annoying to have to set and pass RNG state explicitly, but on the plus side you never hit these sorts of issues. Your code will also be completely reproducible, without any chance of spooky “action at a distance.” Once you’ve been burned by this a few times, you’ll never go back.

You might think that explicitly seeding the global RNG would solve reproducibility issues, but it really doesn’t. If you call into any code you didn’t write, it might also be using the same global RNG.

[warsheep]

The solution you suggest is irrelevant to the issue mentioned in the article. Even if you use np.random.RandomState, or any other "explicit RNG state", that state will still be copied in the fork() call.

The post just stresses that one should be careful when using random states and multiprocessing, so you should either reseed after forking or using multiprocess/multithread-aware RNG API.

[nemetroid]

I believe the point is that the error will be more obvious if the state is passed around explicitly.