TensorFlow Benchmarks
github.com
TensorFlow Benchmarks
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Re: TensorFlow Benchmarks
#2It takes 4x as long as CuDNN on AlexNet due to lack of in-place ops. What is up with that?
Re: TensorFlow Benchmarks
#3It's almost like Google wanted everyone to use slow obsolete software and keep the really good stuff for itself, while still making it look like they're doing a great thing for the community.
Re: TensorFlow Benchmarks
#4Interesting benchmarks. One hopefully constructive critique, if you say things go out of memory, it'd be really useful to know what your setup is. Maybe you've got a big array of massive GPUs or you're running it on a more normal consumer GPU+box.
Re: TensorFlow Benchmarks
#5It's almost like Google wanted everyone to use slow obsolete software and keep the really good stuff for itself, while still making it look like they're doing a great thing for the community.
I think it makes more sense that:
a) It's Google, they can throw hardware at a problem such as out of memory
b) TensorFlow makes methods development so much easier that it's worth the loss of performance
c) It's early days and the compute graph scheduler has lots of opportunities, and is designed, for optimization, and in a more flexible fashion than other frameworks.
When I work on developing methods for scientific code, I worry more about whether the code is bug free/easy to understand and that it's giving the right answer. I don't usually worry about performance unless I'm actually not able to run things. Especially since when developing stuff you waste way more time on runs with bugs - I dread to think what my (published / unpublished) CPU hour ratio is. If the new approach allows less buggy implementations then that's a resource win.
Re: TensorFlow Benchmarks
#6In the announcement they called this the "reference implementation".
Re: TensorFlow Benchmarks
#7It's almost like Google wanted everyone to use slow obsolete software and keep the really good stuff for itself, while still making it look like they're doing a great thing for the community.
Maybe they got a pile of old software they give to their PR people.
"Hey, we would throw this away, but maybe this could be used to shine up our brand a bit"
Re: TensorFlow Benchmarks
#8It's almost like Google wanted everyone to use slow obsolete software and keep the really good stuff for itself, while still making it look like they're doing a great thing for the community.
Seeing as Google has one of the most powerful computing networks in the world, I bet it's more that they can just throw resources at it until it's fast enough. This helps justify the trade off between speed and ease of use.
Re: TensorFlow Benchmarks
#9It's almost like Google wanted everyone to use slow obsolete software and keep the really good stuff for itself, while still making it look like they're doing a great thing for the community.
I think it makes more sense that: a) It's Google, they can throw hardware at a problem such as out of memory b) TensorFlow makes methods development so much easier that it's worth the loss of performance c) It's early days and the compute graph scheduler has lots of opportunities, and is designed, for optimization, and in a more flexible fashion than other frameworks. When I work on developing methods for scientific…
> b) TensorFlow makes methods development so much easier that it's worth the loss of performance
Indeed, if TensorFlow means I can try out an idea with 1 day of coding and 2 days of training rather than 3 days of coding and 1 day of training then I can spend a day drinking cocktails and reading books and still be finished sooner.
Based on the tutorials it seems like I'd be able to pretty quickly build a translation pipeline, and in fact there's an implementation of that I think I'll try. If it takes a week or two to train, that's fine by me, I've got other things to be getting on with.
Re: TensorFlow Benchmarks
#10From Google's perspective it is probably more about how TensorFlow scales out horizontally. If a researcher fires off a Borg run (or whatever they use now) and the job takes a few thousand CPUs, no problem, at least for research.
They must have better optimization a for running in production, such as in place operations.