Last time I used it the step is just "Choose your engine", in the command line plaidml first detect the drivers, then you choose which driver you want to use, after that the system will run according to your preferences. (This is reference to use plaidml btw, for using amd outside plaidml I heard you use ROCm but it's locked to enterprise grade gpu afaik)

But yeah, cuda literally just install tensorflow cuda and you're good to go (I think, I never do it myself since I only have laptop version of 1050 and my family's PC use Radeon)...

Last time I check the github there is an update early 2022 iirc, hopefully someone revive it again since it's the only way so far for non-Nvidia user can use gpu power for deep learning. If only recent generation nvidia isn't that expensive... Is there any news for ROCm in consumer AMD gpu (the rx 6000 series and the like)?

https://plaidml.github.io/plaidml/ Tips by my experience, uninstall tensorflow if you already have one but keep the keras. Somehow plaidml don't want to run if tensorflow already there. It should work whether you have tensorflow or not but I found it's the otherwise

Edit: if you have any progress to it please tell me about the experience.

Yeah, looking at some forums talking about it (and I have tested their benchmark myself in RX 5600XT) it is doable. But the question is why no one literally talking or hyping about it? This thing exist since 2017 iirc...

I think you are in the wrong subreddit..

The only method I know is matrix multiplication but that was just the forward part. The backward part need an understanding of partial derivation. The code will adapt according to the language...