I’ve installed pytorch and numpy following the instructions here https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano-version-1-3-0-now-available/1.
They work fine it seems but they only use one CPU core at all time instead of the 4 available.
If I run something like this for example, the job stops at 100% usage.
import torch a = torch.rand(100, 1000, 1000) b = torch.rand(100, 1000, 1000) while True: c = torch.bmm(a, b)
Same goes for a numpy computation that would spread accross all cores otherwise.
Tensorflow, however, uses all available ressources.
Any idea why?
Do I have to install some special library like openBLAS or MKL for pytorch and numpy to use all available ressources? Or is this a problem with the wheel which was distributed?
Thanks in advance,