I think there are several reasons. Firstly, nVidia has been at it much longer. Just because of this tools on nVidia side feel easier to set up / are more polis

I think there are several reasons. Firstly, nVidia has been at it much longer. Just because of this tools on nVidia side feel easier to set up / are more polished (at least that was my feeling when fiddling with ROCm like a year ago). Second but still related to #1, from the beginning even consumers nVidia cards were able to run CUDA and this made so that hobbyist and prosumers/researchers on a budget bought nVidia cards compounding even further the time/tooling advantage nVidia had. I.e. a huge user base of not only gamers but people that use their cards to do other things than gaming and know that things work on these cards. These are, IMHO, the main reasons why everyone targets CUDA and explain why frameworks like Tensorflow or Pytorch targeted it as a first class citizen.