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From Chris Olivier <>
Subject Re: [DISCUSS] Seeding and determinism on multi-gpu systems.
Date Mon, 08 Jan 2018 18:29:50 GMT
Is it explicitly defined somewhere that random number generators should
always return a deterministic set of numbers given the same seed, or is
that just a side-effect of some hardware not having a better way to
generate random numbers so they use a user-defined seed to kick off the
randomization starting point?

On Mon, Jan 8, 2018 at 9:27 AM, kellen sunderland <> wrote:

> Hello MXNet devs,
> I wanted to see what people thought about the follow section of code, which
> I think has some subtle pros/cons:
> d2a856a3a2abb4e72edc301b8b821f0b75f30722/src/
> Tobi (tdomhan) from sockeye pointed it out to me after he spent some time
> debugging non-determinism in his model training.
> This functionality is well documented here:
> html#mxnet.random.seed
> but I don't think the current api meets all use cases due to this section:
> "Random number generators in MXNet are device specific. Therefore, random
> numbers generated from two devices can be different even if they are seeded
> using the same seed."
> I'm guessing this is a feature that makes distributed training easier in
> MXNet, you wouldn't want to train the same model on each GPU.  However the
> downside of this is that if you run unit tests on a multi-gpu system, or in
> a training environment where you don't have control over which GPU you use,
> you can't count on deterministic behaviour which you can assert results
> against.  I have a feeling there are non-unit test use cases where you'd
> also want deterministic behaviour independent of which gpu you happen to
> have your code scheduled to run on.
> How do others feel about this?  Would it make sense to have some optional
> args in the seed call to have the seed-per-device functionality turned off?
> -Kellen

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