related but how do I check which GPU is being used if nvidia-smi is not working for me?
This would sound a bit concerning, as it could indicate that your driver installation is broken, so I would expect PyTorch would also not be able to detect GPUs.
is there a pytorch recommended way to install gpus in linux ubuntu?
This is my current way:
apt update;
apt install build-essential;
sudo add-apt-repository ppa:graphics-drivers
sudo apt install ubuntu-drivers-common
ubuntu-drivers devices
sudo apt-get install nvidia-driver-460
sudo reboot now
to uninstall things I’ve done:
# ubuntu-drivers list
# sudo apt-get --purge remove nvidia-driver-460
# sudo apt-get --purge remove nvidia-driver*
# modinfo nvidia
#sudo apt-get install nvidia-driver-450
when trying to start from scratch on my ubuntu vm
I don’t think there is a “PyTorch recommended way” to install the drivers/CUDA and I would stick to an approach, which works for you. Personally, I use the .run files to install the drivers and/or CUDA toolkit, but I’m also reinstalling it quite often (e.g. to test new bringup versions etc.), so I don’t really care for stability.
something like this:
apt update;
apt install build-essential;
wget https://us.download.nvidia.com/tesla/460.73.01/NVIDIA-Linux-x86_64-460.73.01.run;
chmod +x NVIDIA-Linux-x86_64-460.73.01.run;
sudo ./NVIDIA-Linux-x86_64-460.73.01.run
I’ve tried it too and it didn’t work 
Yes, this would be my approach to install the drivers alone.
If you want to install the CUDA toolkit with the drivers, you could use e.g. cuda_11.3.0_465.19.01_linux.run.