Hey all, you may remember about 6 months ago L1 did a review of the ASUS GTX 1080 Ti Strix, which featured some machine learning in TensorFlow.
TensorFlow is a python machine learning library that leverages GPU hardware with CUDA. There has been much grumbling in many corners of the internets about porting it and other machine learning libraries to OpenCL so that it isn’t tied to Nvidia hardware… From what I’ve read on the topic, it seems the major sticking point was the cuDNN library and cuBLAS library that provided a lot of low level primitives and were deeply integrated into every machine learning library from TensorFlow to Torch.
A few years ago, AMD announced their intention to change things with their ‘ROCm’ ‘HIP’, and a few other catchy acronyms. Their approach was to solve things in the classic computer science way: Add another layer of abstraction, hooray!
Somehow, the HIP toolchain compiles CUDA to C++ that will run on the GPU, or something like that.
TL;DR
There’s a version of TensorFlow that should run on Vega.
EDIT: In case you’re interested and have AMD but not vega, I think it is supposed to run on >= 380X
I haven’t been able to find any benchmarks anywhere yet, and I for one am really interested in what the performance is like on AMD.
I’m kindof in the market for a GPU but I am loathe to pay money to Nvidia, but I also want to be able to use TF. If anyone on the forums has any experience with this, hit me up.
L1 Team: If you guys are interested in trying this out, I think it would make good content, and you would be literally the first to cover it. I may be able to help sponsor it, if that makes any difference(And maybe hit up Ed from Sapphire too, sounds like something he would dig)
