# Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"

*Date*: Wed, 22 May 2019 08:43:27 -0400*From*: Sam Hartman <hartmans@xxxxxxxxxx>*Subject*: Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"

>>>>> "Mo" == Mo Zhou <lumin@xxxxxxxxxx> writes: Mo> Hi Holger, Yes, that section is about bit-by-bit Mo> reproducibility, and identical hashsum is expected. Let's call Mo> it "Bit-by-Bit reproducible". Mo> I updated that section to make the definition of "reproducible" Mo> explicit. And the strongest one is discussed by default. Mo> However, I'm not sure whether "bit-by-bit" is easy to break by Mo> some obscure reasons in a complex system (e.g. float point Mo> precision problems, time stamps hidden in the stored model). And Mo> I've never tried to compared my neural nets with hashsums... I Mo> compare curves and digits instead ... I need some time to think Mo> about it, verify, and refine the definition. So, I think it's problematic to apply old assumptions to new areas. The reproducible builds world has gotten a lot further with bit-for-bit identical builds than I ever imagined they would. However, what's actually needed in the deep learning context is weaker than bit-for-bit identical. What we need is a way to validate that two models are identical for some equality predicate that meets our security and safety (and freedom) concerns. Parallel computation in the training, the sort of floating point issues you point to, and a lot of other things may make bit-for-bit identical models hard to come by. Obviously we need to validate the correctness of whatever comparison function we use. The checksums match is relatively easy to validate. Something that for example understood floating point numbers would have a greater potential for bugs than an implementation of say sha256. So, yeah, bit-for-bit identical is great if we can get it. But validating these models is important enough that if we need to use a different equality predicate it's still worth doing. --Sam

**Follow-Ups**:

**References**:**Bits from /me: A humble draft policy on "deep learning v.s. freedom"***From:*Mo Zhou

**Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"***From:*Andreas Tille

**Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"***From:*Tzafrir Cohen

**Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"***From:*Mo Zhou

**Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"***From:*Holger Levsen

**Re: Bits from /me: A humble draft policy on "deep learning v.s. freedom"***From:*Mo Zhou

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