It's a vicious cycle though, since everything uses CUDA/CuDNN everyone buys nVidia cards, which means that developer interest in getting AMD cards fast isn't there.
If AMD GPU cores were in phones etc, they would probably be getting TF support for client work at least. Heck, considering they sold off that dept to Qualcomm, which is getting supported...
Considering how small of a dent that would make in their bottom line (financially), it's really amazing they haven't yet... The potentially upside is easily in the tens (if not hundreds) of millions of dollars.
So definitely, I agree they intentionally have decided to not support deep learning. For the life of me, I don't know why.
https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/iss...
The problem is that it isn't just TensorFlow. It is also Caffe, Theano, Torch, etc., etc., etc.. For deep learning, Nvidia GPU acceleration is almost a given.
80% of deep learning users use the TensorFlow, Caffe, or Torch frameworks.
CUDA simply feels less hacked together to me. It's like working with a well thought out and documented code base (CUDA) vs working with some library that has little documentation and contradicting syntax and formats (OpenCL).
IMO that's why AMD is losing the "deep learning" battle, it's just not easy to develop using OpenCL. At least, not as easy as it should be.