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Which GPU(s) to Get for Deep Learning

timdettmers.com

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Re: Which GPU(s) to Get for Deep Learning

#21

Post by jbmorgado »

AlphaSite wrote: post #13

Interestingly, AMD is claiming that their upcoming Vega Frontier Editions chips outperform Nvidia's current P100 Deep Learning chip. http://hexus.net/media/uploaded/2017/5/30f5633b-1bbf-49b7-9f...

The question is not AMD's processing power, the question is the tools available for AMD cards, which are scant to say the best.

While AMD doesn't create something like cuDNN to go along with their cards, no real work will start being done in porting most important DL libraries to AMD cards. And even in that case, it will be a lost generation for AMD, only in the 2nd generation where AMD actually offers a real alternative in DL to NVIDIA, you will start to see some real progress being done with it.

Re: Which GPU(s) to Get for Deep Learning

#22

Post by dragandj »

sgt101 wrote: post #16
chronic940 wrote: post #12

Earlier quoted context omitted.

K80s and K40s have a K. That's two generations old. We are currently on P and about to be V. If you think K80s are good, you are far far behind the times in machine learning. A single 1080Ti even in a 4U server outperforms 2 K80s.

Ok - p100's? The thing is that a K80 costs.... £4k what is it that people are paying for?

ECC memory (not that everyone really need that)...

Re: Which GPU(s) to Get for Deep Learning

#23

Post by tanilama »

My recommendation: 1080 Ti or 1080 GTX.

There is no reason, not to get a Pascal GPU at this point, the performance is simply superior. For a lot of models, we are talking about days of training time, so 20%-30% time saving is significant, and we are not even touching the part that large VRAM enables bigger batch size which you won't get in middle/low end GPUs.

Re: Which GPU(s) to Get for Deep Learning

#25

Post by redtuesday »

jbmorgado wrote: post #21
AlphaSite wrote: post #13

Interestingly, AMD is claiming that their upcoming Vega Frontier Editions chips outperform Nvidia's current P100 Deep Learning chip. http://hexus.net/media/uploaded/2017/5/30f5633b-1bbf-49b7-9f...

The question is not AMD's processing power, the question is the tools available for AMD cards, which are scant to say the best. While AMD doesn't create something like cuDNN to go along with their cards, no real work will start being done in porting most important DL libraries to AMD cards. And even in that case, it will be a lost generation for AMD, only in the 2nd generation where AMD actually offers a real alterna…

They stated at the analyst day that they will support TensorFlow, Theano, Caffee etc. And will release something called MiOpen.

They also stated this last year:

> On top of ROCm, deep-learning developers will soon have the opportunity to use a new open-source library of deep learning functions called MIOpen that AMD intends to release in the first quarter of next year. This library offers a range of functions pre-optimized for execution on Radeon Instinct cards, like convolution, pooling, activation, normalization, and tensor operations

http://techreport.com/review/31093/amd-opens-up-machine-lear...

Re: Which GPU(s) to Get for Deep Learning

#26

Post by gbrown »

dragandj wrote: post #19
sp332 wrote: post #14

Earlier quoted context omitted.

I think signs are pointing to Vega being a HPC beast. But what they really need is the software ecosystem and support, and so far that hasn't been there. So while the new hardware looks cool, I'm really waiting for an announcement that OpenCL tooling got a lot better, or that CUDA is getting first-party support from AMD, anything to tempt those customers away from nvidia.

CUDA is getting support from AMD in the form of HIP. In my opinion, the problem is different: even if AMD supported CUDA proper, that still won't lead them anywhere because the reason people use Nvidia is not so much CUDA itself (although it has good tools) but Nvidia's proprietary libraries: cuBLAS, cuDNN, cuFFT etc. If AMD provided good implementations of similar libraries with OpenCL and shipped them with their dr…

https://github.com/clMathLibraries

Re: Which GPU(s) to Get for Deep Learning

#27

Post by zeptomu »

There is an interesting offer by Hetzner that's available for some month now.

They provide a dedicated server with a GTX 1080 for ~99e/month (111$/month) with adequate CPU (i7-6700), 64G Memory, 500G disk space and 50TB Traffic - there are also on-demand offerings by GCP and AWS, but I do not think they can match the offer by Hetzner: https://www.hetzner.de/us/hosting/produkte_rootserver/ex51ss.... Keep in mind that I am talking about R&D, in particular training of networks - which has lower expectations on availability than e.g. later inference.

Disclosure: I am not affiliated with Hetzner and did not test it yet, but I have a ~40e dedicated machine there for occasional number crunching and everything worked so far (no availability or hardware issues).

Furthermore I am not sure about the exact specifications (Memory-Size of GPU) or if there are different types of 1080s that differ significantly in Deep Learning Performance (see also: https://github.com/jcjohnson/cnn-benchmarks).

Re: Which GPU(s) to Get for Deep Learning

#28

Post by ThePhysicist »

I recently bought an Alienware 13 R3 for deep learning (okay, and gaming...) and I'm quite satisfied so far. In addition to the built-in Titan 1060 with 6 GB of DDR5 it's possible to get an external graphics accelerator for additional performance as well (I haven't tried this yet though).

Re: Which GPU(s) to Get for Deep Learning

#30

Post by dragandj »

gbrown wrote: post #26
dragandj wrote: post #19

Earlier quoted context omitted.

CUDA is getting support from AMD in the form of HIP. In my opinion, the problem is different: even if AMD supported CUDA proper, that still won't lead them anywhere because the reason people use Nvidia is not so much CUDA itself (although it has good tools) but Nvidia's proprietary libraries: cuBLAS, cuDNN, cuFFT etc. If AMD provided good implementations of similar libraries with OpenCL and shipped them with their dr…

https://github.com/clMathLibraries

There are even better independent open-source equivalents for some of those, but that is not the point. Good luck compiling some of AMD's clMath libraries without issues. Then, you're "just" left with packaging them properly. Performance is also not stellar compared to Nvidia. Meanwhile, when you install Nvidia's toolkit, you're set with everything.
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