Yep. This is the same lesson that I learned in 2013 and again in 2015 with OpenCL, both for professional apps and for programming. On the professional app side,

Yep. This is the same lesson that I learned in 2013 and again in 2015 with OpenCL, both for professional apps and for programming. On the professional app side, there was a lot of "support" for OpenCL that had caveats severe enough to make it unusable. Sure, blender supports OpenCL! You just have to make a custom build and then the GPU target is slower than the CPU target. Sure, Adobe apps support OpenCL -- but any GPU render contexts are solid black boxes and forum posts indicate it has been that way for a year and nobody cared to fix it. Same thing with programming and debugging: there were slides suggesting feature parity with CUDA on a bunch of fronts that just weren't implemented or locked up the computer if you tried to actually use them. It got so bad that I walked away from my sunk costs, sold my AMD cards, ate the ebay tax, ate the nvidia tax, and bought cards that actually worked. Now I'm in "twice bitten, once shy" mode with AMD. I hate paying the green tax as much as the next guy and I desperately want to have a second source of professional GPUs, but I'm not going to be the guinea pig. Not again. Not for the 3rd time. I want to see someone else successfully using AMD cards for common ML workflows and for blender before I even consider risking it again.