3 ms·
But my point is that even if you use some closed-source driver or whatever is required to "set up" your project - you still can't re-use your regular code whole
by yboris 5y ago
But my point is that even if you use some closed-source driver or whatever is required to "set up" your project - you still can't re-use your regular code wholesale, but will have to modify to fit within whatever features they support. Right?
- mirker 5y agoThe API of Tensorflow and PyTorch is quite large. Even Tensorflow has a dialect specifically for TPU, so the notion of accelerator independence (especially when considering performance) has not been realized yet (though this is still a work in progress). Anyway, how could you reuse your code wholesale when one of the most common operations is calling “.cuda()” on a tensor?
- my123 5y agoUnsupported layers will transparently fall back to CPU execution. You can then choose to implement those who can be as TPC kernels. It's fundamentally much less flexible than a GPU. Efficiency of Habana hardware isn't that great either, but that's another story... (they're still using TSMC 16nm in 2022 notably). Where Habana has an advantage in some workloads is cost.