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Radeon Instinct – Optimized Machine and Deep Learning

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51–60 of 88 posts

Re: Radeon Instinct – Optimized Machine and Deep Learning

#51
post #12

What's particularly interesting here is that the Fiji card they propose is a very different beast than any of the NVIDIA offerings. The MI8 card's HBM has a great power and performance advantage (512 GB/s peak bandwidth) even if it's on 28 nm. NVIDIA has nothing that has even remotely comparable bandwidth in this price/perf/TDP regime. None of the NVIDIA GP10[24] Teslas have GDDR5X -- not to surprising given that it…

[deleted]

Re: Radeon Instinct – Optimized Machine and Deep Learning

#52
post #10
post #4

Earlier quoted context omitted.

For what it's worth, AMD has been working on a CUDA compatibility solution for at least a year now. Announcement[0] and Progress[1] [0] http://www.anandtech.com/show/9792/amd-sc15-boltzmann-initia... [1] http://www.anandtech.com/show/10831/amd-sc16-rocm-13-release...

Additionally, here's an example of how Caffe was porting using HIP [1]. To be honest, if the approach really does work, you might see a very quick increase in the number of applications ported. All in all, given how elegant HIP is and that HCC seems to make GPUs more approachable than CUDA (and less silly than OpenACC), there is a great potential for AMD to gain some traction. My greatest concern is the quality and r…

Things got a bit heated in the LKML thread, but if you read the more recent messages things have cooled down significantly. It's unclear whether they will ultimately be able to achieve all their goals while also getting the code into an upstreamable state, but they do seem committed to working out the issues with upstream.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#53
post #28

Earlier quoted context omitted.

So it's a headless passively cooled server version of the R9 Fury X? I figured this was a faster and improved version. Or is this Polaris based? http://www.anandtech.com/show/9390/the-amd-radeon-r9-fury-x-... Not sure if AMD is going to go all HBM on all it's high performance GPUs in 2017, or only offer one or two models with it. http://arstechnica.com/gadgets/2016/03/amd-gpu-vega-navi-rev... EDIT: MI6 is Polaris bas…

> So it's a headless passively cooled server version of the R9 Fury X? I figured this was a faster and improved version. Almost. My guess is R9 Nano [1] given the same 8.2 Tflops (SP) Flop rate [2]. [1] http://www.anandtech.com/show/9621/the-amd-radeon-r9-nano-re... [2] http://images.anandtech.com/doci/10905/AMD%20Radeon%20Instin... > Not sure if AMD is going to go all HBM on all it's high performance GPUs in 2017, o…

From the photos and perf numbers it looks like the linup is RX480, R9 Nano, and whatever the Vega10 gets called, minus some of the connectors and passively cooled.

Re: Radeon Instinct – Optimized Machine and Deep Learning

#55
post #33
post #3

Earlier quoted context omitted.

If AMD can release solid hardware, all they would need to do is add support for their hardware to popular open-source projects like TensorFlow. I just hope they do the second part correctly.

FYI, AMD's people are helping TF to add OpenCL support. https://github.com/tensorflow/tensorflow/issues/22

Contributions from AMD are sorely lacking. It's mostly people from https://www.codeplay.com/ and https://github.com/hughperkins/

Re: Radeon Instinct – Optimized Machine and Deep Learning

#56
There seems to be considerable effort being undertaken to allow TensorFlow to work with OpenCL [0]. Also see [1]. This coincides nicely with the introduction of these AMD cards.

I'm looking forward to the day that Nvidia gets some competition in the GPUs-for-deeplearning market. Further, doing some smaller Deep learning experiments on my MacBook Pro with AMD discrete GPU is another benefit I'm looking forward to ;)

[0] https://github.com/tensorflow/tensorflow/issues/22

[1] https://github.com/benoitsteiner/tensorflow-opencl

Re: Radeon Instinct – Optimized Machine and Deep Learning

#57
post #10
post #4

Earlier quoted context omitted.

For what it's worth, AMD has been working on a CUDA compatibility solution for at least a year now. Announcement[0] and Progress[1] [0] http://www.anandtech.com/show/9792/amd-sc15-boltzmann-initia... [1] http://www.anandtech.com/show/10831/amd-sc16-rocm-13-release...

Additionally, here's an example of how Caffe was porting using HIP [1]. To be honest, if the approach really does work, you might see a very quick increase in the number of applications ported. All in all, given how elegant HIP is and that HCC seems to make GPUs more approachable than CUDA (and less silly than OpenACC), there is a great potential for AMD to gain some traction. My greatest concern is the quality and r…

That's a better relationship than nvidia's. https://www.wired.com/2012/06/torvalds-nvidia-linux/

Re: Radeon Instinct – Optimized Machine and Deep Learning

#59

Does anyone use AMD for deep learning in scientific / industry ? All the libraries for deep learning I have seen require CUDA and NVIDIA is winning by merely being the most popular API. Searching github it looks like they are university assignment projects see https://github.com/search?utf8=%E2%9C%93&q=opencl+deep+learn...

I remember that a few years ago AMD had the only sensible solution for virtualizing GPUs, and you could make a bunch of them work together as a single unit without much trouble. But I didn't have a Radeon card so I never got to try it. Don't know what happened with that, it's true that they really lagged behind NVIDIA.

AMD has a new virtualization solution which provides SR-IOV hardware based partitioning on their AMD FirePro S7150 or S7150 x2 gpu cards. http://www.amd.com/en-us/solutions/professional/virtualizati...

This video explains what you get https://www.youtube.com/watch?v=tKBthlKTtvQ

Re: Radeon Instinct – Optimized Machine and Deep Learning

#60
post #47
post #35

What's a good GPU / setup for someone doing deep learning at home? Does anyone have recommendations?

And for another naive question: Why kind of practical/"boring" things can be done with this kind of hardware? I'm in Latin America, and wonder which kind of niche could be satisfied with this, for more "normal" kind of customers.

Deep learning is useful for predictions (including classification) based on audio, image, or video. There are many boring applications.
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