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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 be

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+learning&type=Repositories&ref=searchresults https://github.com/search?utf8=%E2%9C%93&q=opencl+deep+learn...

mabbo 10y ago
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.
- AlphaSite 10y ago
  It looks like they're doing it, or already have done so. https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/issues/45 https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/iss...
  - RockyMcNuts 10y ago
    TensorFlow is evolving rapidly and it's a challenge to keep up with GPU driver updates, CUDA updates, TensorFlow updates, and not fall into version hell. For instance I took the Udemy course a few months back, given by a Google scientist, and in at least one instance the code was pinned to CPU because it didn't run on GPU, presumably due to a TensorFlow issue. If they can get TensorFlow running well on AMD and release images that make it painless, it will be a huge win, but I'm a little skeptical. Big difference between getting a benchmark win and a stable platform.
- devy 10y ago
  FYI, AMD's people are helping TF to add OpenCL support. https://github.com/tensorflow/tensorflow/issues/22 https://github.com/tensorflow/tensorflow/issues/22
  - dharma1 10y ago
    Contributions from AMD are sorely lacking. It's mostly people from https://www.codeplay.com/ https://www.codeplay.com/ and https://github.com/hughperkins/ https://github.com/hughperkins/
    - devy 10y ago
      I noticed Hugh Perkins has a AMD badge under "Organizations". Is he not working for AMD? Also one of the earlier participants of the TF OpenCL support conversations https://github.com/gujunli https://github.com/gujunli was working for AMD at the time[1]. https://electrek.co/2016/02/26/tesla-machine-learning-expert-autopilot/ https://electrek.co/2016/02/26/tesla-machine-learning-expert...
      - dharma1 10y ago
        No, Hugh works at asapp.com. AMD could really invest more in optimising deep learning libraries for their hardware if they want to be relevant
        devy 10y ago
        Interesting! He's definitely got some interesting background there. Normally someone wouldn't be added into a Github organization without an employment relationship there. > AMD could really invest more in optimising deep learning libraries for their hardware if they want to be relevant I am optimistic about AMD's involvement in AI. Just do a search of recent news on here, tons of exciting devleopment from AMD, especially Radeon Instinct. And here is an interesting comment about the comparison between Nvidia and AMD[1]. [1] https://news.ycombinator.com/item?id=13161050 https://news.ycombinator.com/item?id=13161050
peller 10y ago
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-initiative-announced-c-and-cuda-compilers-for-amd-gpus http://www.anandtech.com/show/9792/amd-sc15-boltzmann-initia... [1] http://www.anandtech.com/show/10831/amd-sc16-rocm-13-released-boltzmann-realized http://www.anandtech.com/show/10831/amd-sc16-rocm-13-release...
- dogma1138 10y ago
  It only works for the basic functionality they can't interop the "operational" CUDA features.
  - slizard 10y ago
    > can't interop the "operational" CUDA features What exactly do you that mean?
    - dogma1138 10y ago
      DMA, networking, storage and direct i/o, virtualization etc.
      - slizard 10y ago
        The tricky, but fun parts. It's certainly not trivial to port those, but I'm not too worried about it as long as there is solid runtime support in ROCm. More fun work for perf engineers like me. ;) Massive amount of boilerplate, heavy APIs or crappy software stack are more dangerous IMO than having to drop in replacements for NVIDIA-specific optimizations of GPU-to-NIC or GPU-to-GPU communication.
        dogma1138 10y ago
        Not only not trivial to port those but they are constantly will have to play catch up and be at the mercy of NVIDIA in regards to spec. AMD is in a catch 22, support CUDA and be effectively in a constant catch up position, not support it and continue to be ignored by the market at large simply because the momentum NVIDIA has managed to achieve with CUDA over the years.
madenine 10y ago
Some people do, but they're by far the minority. CUDA / cuDNN are well tuned and integrated with the most popular libraries. At this point, in order to get people to switch not only would your hardware have to be faster, but your replacement for cuDNN would have to be better as well. Which is by no means impossible, just difficult.
- x0x0 10y ago
  I disagree; most people use caffe / keras / theano / tensorflow / etc hiding the cuDNN details so end users won't care much. Offering more performance / shorter training loops is a big deal. Your typical ML learning loop with an expensive human regularly waiting for experiment results shows clear benefits.
  - ogrisel 10y ago
    The point is that there is no optimized cuDNN equivalent for AMD hardware at this point.
    - deleted 10y ago
      [deleted]
    - eightysixfour 10y ago
      AMD has built this middleware for all of those applications (keras, tensorflow, caffe, etc) and they show the performance results in their slides.
    - deleted 10y ago
      [deleted]
eightysixfour 10y ago
Looks like AMD is building replacement middleware for most of the major ML stacks, some more information on this page: http://www.anandtech.com/show/10905/amd-announces-radeon-instinct-deep-learning-2017/3 http://www.anandtech.com/show/10905/amd-announces-radeon-ins...
dr_zoidberg 10y ago
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.
- viewtransform 10y ago
  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/virtualization http://www.amd.com/en-us/solutions/professional/virtualizati... This video explains what you get https://www.youtube.com/watch?v=tKBthlKTtvQ https://www.youtube.com/watch?v=tKBthlKTtvQ
  - deleted 10y ago
    [deleted]
deepnotderp 10y ago
They desperately need a cudnn competitor.
hedgehog 10y ago
Yes, we use and like the R9 Nano. We wrote our own tools that are portable across GPUs so third party software support (eg cuDNN) is not a factor for us.

mabbo 10y ago

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.

AlphaSite 10y ago
It looks like they're doing it, or already have done so. https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/issues/45 https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/iss...
- RockyMcNuts 10y ago
  TensorFlow is evolving rapidly and it's a challenge to keep up with GPU driver updates, CUDA updates, TensorFlow updates, and not fall into version hell. For instance I took the Udemy course a few months back, given by a Google scientist, and in at least one instance the code was pinned to CPU because it didn't run on GPU, presumably due to a TensorFlow issue. If they can get TensorFlow running well on AMD and release images that make it painless, it will be a huge win, but I'm a little skeptical. Big difference between getting a benchmark win and a stable platform.
devy 10y ago
FYI, AMD's people are helping TF to add OpenCL support. https://github.com/tensorflow/tensorflow/issues/22 https://github.com/tensorflow/tensorflow/issues/22
- dharma1 10y ago
  Contributions from AMD are sorely lacking. It's mostly people from https://www.codeplay.com/ https://www.codeplay.com/ and https://github.com/hughperkins/ https://github.com/hughperkins/
  - devy 10y ago
    I noticed Hugh Perkins has a AMD badge under "Organizations". Is he not working for AMD? Also one of the earlier participants of the TF OpenCL support conversations https://github.com/gujunli https://github.com/gujunli was working for AMD at the time[1]. https://electrek.co/2016/02/26/tesla-machine-learning-expert-autopilot/ https://electrek.co/2016/02/26/tesla-machine-learning-expert...
    - dharma1 10y ago
      No, Hugh works at asapp.com. AMD could really invest more in optimising deep learning libraries for their hardware if they want to be relevant
      - devy 10y ago
        Interesting! He's definitely got some interesting background there. Normally someone wouldn't be added into a Github organization without an employment relationship there. > AMD could really invest more in optimising deep learning libraries for their hardware if they want to be relevant I am optimistic about AMD's involvement in AI. Just do a search of recent news on here, tons of exciting devleopment from AMD, especially Radeon Instinct. And here is an interesting comment about the comparison between Nvidia and AMD[1]. [1] https://news.ycombinator.com/item?id=13161050 https://news.ycombinator.com/item?id=13161050

peller 10y ago

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-initiative-announced-c-and-cuda-compilers-for-amd-gpus http://www.anandtech.com/show/9792/amd-sc15-boltzmann-initia... [1] http://www.anandtech.com/show/10831/amd-sc16-rocm-13-released-boltzmann-realized http://www.anandtech.com/show/10831/amd-sc16-rocm-13-release...

dogma1138 10y ago
It only works for the basic functionality they can't interop the "operational" CUDA features.
- slizard 10y ago
  > can't interop the "operational" CUDA features What exactly do you that mean?
  - dogma1138 10y ago
    DMA, networking, storage and direct i/o, virtualization etc.
    - slizard 10y ago
      The tricky, but fun parts. It's certainly not trivial to port those, but I'm not too worried about it as long as there is solid runtime support in ROCm. More fun work for perf engineers like me. ;) Massive amount of boilerplate, heavy APIs or crappy software stack are more dangerous IMO than having to drop in replacements for NVIDIA-specific optimizations of GPU-to-NIC or GPU-to-GPU communication.
      - dogma1138 10y ago
        Not only not trivial to port those but they are constantly will have to play catch up and be at the mercy of NVIDIA in regards to spec. AMD is in a catch 22, support CUDA and be effectively in a constant catch up position, not support it and continue to be ignored by the market at large simply because the momentum NVIDIA has managed to achieve with CUDA over the years.

madenine 10y ago

Some people do, but they're by far the minority. CUDA / cuDNN are well tuned and integrated with the most popular libraries. At this point, in order to get people to switch not only would your hardware have to be faster, but your replacement for cuDNN would have to be better as well. Which is by no means impossible, just difficult.

x0x0 10y ago
I disagree; most people use caffe / keras / theano / tensorflow / etc hiding the cuDNN details so end users won't care much. Offering more performance / shorter training loops is a big deal. Your typical ML learning loop with an expensive human regularly waiting for experiment results shows clear benefits.
- ogrisel 10y ago
  The point is that there is no optimized cuDNN equivalent for AMD hardware at this point.
  - deleted 10y ago
    [deleted]
  - eightysixfour 10y ago
    AMD has built this middleware for all of those applications (keras, tensorflow, caffe, etc) and they show the performance results in their slides.
  - deleted 10y ago
    [deleted]

eightysixfour 10y ago

Looks like AMD is building replacement middleware for most of the major ML stacks, some more information on this page: http://www.anandtech.com/show/10905/amd-announces-radeon-instinct-deep-learning-2017/3 http://www.anandtech.com/show/10905/amd-announces-radeon-ins...

dr_zoidberg 10y ago

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.

viewtransform 10y ago
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/virtualization http://www.amd.com/en-us/solutions/professional/virtualizati... This video explains what you get https://www.youtube.com/watch?v=tKBthlKTtvQ https://www.youtube.com/watch?v=tKBthlKTtvQ
- deleted 10y ago
  [deleted]

deepnotderp 10y ago

They desperately need a cudnn competitor.

hedgehog 10y ago

Yes, we use and like the R9 Nano. We wrote our own tools that are portable across GPUs so third party software support (eg cuDNN) is not a factor for us.