In case someone from AMD is reading here, you should probably gift out some of the cards to generate some traction. For example, send one to me ;) But joke asid

In case someone from AMD is reading here, you should probably gift out some of the cards to generate some traction. For example, send one to me ;) But joke aside, the ROCm documentation seems so wacky that I would not dare to purchase an expensive AMD card without recommendation by someone that I trust. For example, rocmdocs.amd.com appears to be the official documentation, but the front page doesn't mention any popular AI framework. Opening the "Deep Learning" box in the sidebar, I see that there should be a TensorFlow 2.2.0-beta1 release. But on https://github.com/ROCmSoftwarePlatform/tensorflow-upstream https://github.com/ROCmSoftwarePlatform/tensorflow-upstream the featured release is "2.0.0-rocm", the docker link says it's TensorFlow 2.3 and the "Official Builds" at the bottom of the readme point to CUDA versions ... The whole GitHub repo kind of feels like a bad copy&paste job. I manage to eventually download the TensorFlow 2.3.1 + ROCm source from GitHub, only to find that it doesn't compile. I need the hip_hcc library https://github.com/ROCm-Developer-Tools/HIP/ https://github.com/ROCm-Developer-Tools/HIP/ but apparently that one has been renamed some months ago, but the ROCm TensorFlow port has not been updated yet. After spending half a day trying to install a ROCm-compatible TensorFlow, I gave up. Obviously, I wouldn't want to hope on those issues getting resolved when I buy an as-is unusable $6400 accelerator card. That said, the MI100 could be a great deal if I could get it to work. AMD hardware is a lot more affordable than comparable NVIDIA products, plus the MI100 has lots of fast RAM. I work on optical flow (think autonomous selfie drone) and that needs lots and lots of GPU RAM. Plus I'm very happy with my AMD CPU. But like I said in the beginning, I wouldn't dare buying an AMD GPU unless there was believable evidence that I can get my TF2.3 model running with ROCm in a reasonable timeframe. I'm not trying to blame AMD for having bugs here. I have run into a fair share of issues with NVIDIA GPUs, too, like for example a RAM corruption bug inside CUDA 10.0 and a reproducible kernel freeze in CUDA 10.1. But for NVIDIA, the initial setup is quick and easy. And the CUDA documentation looks like they have a dedicated team that cares about developer productivity. For AMD's ROCm, on the other hand, even the marketing material reads more like someone hopes that the open source community will fix things for free. "first open-source software development platform for HPC/Hyperscale-class GPU computing" sounds great until you notice that in this context, "open-source" apparently means "no binaries + no support".