* It seems there was not a single commit in the past ~6 months which by itself is already a deal breaker.
* Documentation is lackluster
* You need to use Keras, I use PyTorch. This is not a deal breaker, but a significant annoyance.
* Major features are still lacking. E.g. No support for quantization (afaik), which for me is fundamental.
* Most importantly there seem to be no major community around it.
It feels a bit bad to say that, because clearly a lot of work went into this project, and some people have to start adopting it to drive the momentum, but from an egoistical point of view, I just don't have the courage to deal with all the mess that comes with introducing an experimental layer in my workflow. Especially in ML where stuff can still appear to "work" (as in, not crashing) despite major bugs in the underlying code, leading to days or week of lost work before realizing where the issue is.
I've been on plaidml w/ keras for a bit, got over most bottlenecks for now.
Check out keras-helper.. I made it to switch between various backend implementations which are non NVidia specific.
Pytorch may eventually need porting, but for now I don't need it. I've been trying out Coriander and DeepCL now but I decided to stick to Keras, which seems to be a decent compromise. Not using 2.4.x though, do not need it.
OpenCL based backends are cutting it for me, running production workloads without needing to install CUDA/ROCm is the best way to go.
Why would a data scientist making >200k$/year (~1k$/ work day) spend a single second of their time trying to “workaround” something whose solution only costs 1.5k$?
Spending a week/year working around ROCm would already cost you 5k$ plus the opportunity cost. For a whole team that’s a money sink.
Except, not everyone is going to do that. And you don't get to choose what computer a client has when you're shipping software that needs to work out of the box and can take advantage of the said computer.
The analogy is that everyone should get NVIDIA Ampere units (non consumer) units worth $30k because it's fast and you'd rather be spending less time in a lab with millions of dollars in funding. insert don't be poor T Shirt reference
PlaidML is not ROCm. Nobody needs ROCm, what people need is just linear algebra well implemented with OpenCL primitives. That's what PlaidML is. And it works quite well, even on those integrated Intel GPUs on most laptops.
Have you also looked at DirectML and WSL2? They seem to be running tensorflow quite well too. Those things may be the key to bringing these in adoption outside the well paid class of data scientists you came up with.
To be fair, it's not uncommon for a ML researcher / engineer to use tens (~$10/hr on cloud, $100k from Nvidia) or even hundreds (~$100/hr on cloud, $1M from Nvidia) of GPUs to speed up their iteration time. If there was a way to spend half as much on hundreds of AMD GPUs instead that would be a huge win, well worth even months of the researcher's time.
The catch is that ML software stacks have had hundreds if not thousands of man-years of effort put into things like cuDNN, CUDA operator implementations, and Nvidia-specific system code (eg. for distributed training). Many formidable competitors like Google TPU have emerged, but Nvidia is currently holding onto its leadership position for now because the wide support and polish is just not there for any of the competitors yet.
There are data scientist out of the Silicon Valley. In France, salaries for data scientist are mostly 30k€ after tax and social contributions, especially outside of Paris. 1k€ is still largely manageable on this salary but it isn't insignificant.
Sadly I'm not ^^ This correspond to a full cost for the company a bit bellow 60k€ [0]. We do get many advantages that are paid through those contributions, but the available income is useful when comparing to prices.
