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I'm not sure where the disdain for ROCm is coming from, but tensorflow-rocm and the rocm pytorch container were fairly easy to setup and use from scratch once I got the correct Linux kernel installed along with the rest of the necessary ROCm components needed to use tensorflow and pytorch for rocm. TBF Intel Extension for Tensorflow wasn't too bad to setup either (except for the lack of float16 mixed precision training support, that was definitely a pain point to not be able to have), but Intel Extension for Pytorch for Intel GPUs (a.k.a. IPEX-GPU) however, has been a PITA to use for my i5 11400H iGPU NOT because the iGPU itself is slow, BUT because the current i915 driver in the mainline linux kernel simply doesn't work with IPEX-GPU (every script that I've ran ends up freezing when using even the i915 drivers as recent as Kernel version 6), and when I ended up installing drivers that were meant for the Arc GPUs that finally got IPEX-GPUs to work, I ended up with even more issues such as sh*tty FP64 emulation support that basically meant I had to do some really janky workarounds for things to not break while FP64 emulation was enabled (disabling was simply not an option for me, long story short). And yea unlike Intel, both Nvidia AND AMD actually do support FP64 instructions AND FLOAT16 mixed precision training natively on their GPUs so that one doesn't have to worry about running into "unsupported FP64 instructions" and "unsupported training modes" no matter what software they're running on those GPUs.
Windows support for ROCm is pending. You can see the signs on our public repositories on github.com. https://github.com/amd/rocm-examples shows work on adding Visual Studio support. Many other repositories have windows build scripts.
Radeon VII came out at least 3 years ago and it's still supported by ROCm officially; heck even cards at least as far back those released in 2016 are still "unofficially" supported according to here: https://community.amd.com/t5/knowledge-base/amd-rocm-hardware-and-software-support-document/ta-p/489937 And also all current RDNA2 dGPUs are basically supported at least "unofficially" one way or the other you just have to do some tinkering like using workarounds like these: https://github.com/RadeonOpenCompute/ROCm/issues/1756