有人用 AMD 的 Vega 显卡 跑 深度学习框架么？

[Rorysky]

路子好像野了一点……但是 考虑到最近 MBP 高配 vega 系列显卡了，想问问有没有坑

已经抽象成框架，应该和 n 卡体验差不多吧？

[WuwuGin]

没用过，但是听他们说的和 n 卡差别还是挺大的，硬件限制。

[Rorysky]

@WuwuGin 不是 ROcm port 了 tensorflow 么。。。

[WuwuGin]

AMD: Powerful But Lacking Support

HIP via ROCm unifies NVIDIA and AMD GPUs under a common programming language which is compiled into the respective GPU language before it is compiled to GPU assembly. If we would have all our GPU code in HIP this would be a major milestone, but this is rather difficult because it is difficult to port the TensorFlow and PyTorch code bases. TensorFlow has some support for AMD GPUs and all major networks can be run on AMD GPUs, but if you want to develop new networks some details might be missing which could prevent you from implementing what you need. The ROCm community is also not too large and thus it is not straightforward to fix issues quickly. There also does not seem to be much money allocated for deep learning development and support from AMD ’ s side which slows the momentum.

However, AMD GPUs show strong performance compared to NVIDIA GPUs and the next AMD GPU the Vega 20 will be a computing powerhouse which will feature Tensor-Core-like compute units.

Overall I think I still cannot give a clear recommendation for AMD GPUs for ordinary users that just want their GPUs to work smoothly. More experienced users should have fewer problems and by supporting AMD GPUs and ROCm/HIP developers they contribute to the combat against the monopoly position of NVIDIA as this will greatly benefit everyone in the long-term. If you are a GPU developer and want to make important contributions to GPU computing, then an AMD GPU might be the best way to make a good impact over the long-term. For everyone else, NVIDIA GPUs might be the safer choice.

[Greendays]

反正比 CPU 快，至于有没有 CUDA 快。。。还是等专业人士来回答吧

[Rorysky]

http://blog.gpueater.com/en/2018/04/23/00011_tech_cifar10_bench_on_tf13/