Open xiaosefengyun opened 2 years ago
I don't use Docker. The images tend to get updated by the community. I'm tagging @rushic24 who PR'd related #1232 in case he has any insights. In the meantime, please post the output of the following:
From inside your virtual environment, inside your faceswap folder, run:
python -c "from lib.sysinfo import sysinfo ; print(sysinfo)"
and post output
@xiaosefengyun
Because the base image nvidia/cuda:11.7.0-runtime-ubuntu18.04 do not installed the libcudnn8.
docker exec -it inside the docker and run
apt-get install libcudnn8=8.2.4.15-1+cuda11.4
depend on your cuda version inside the docker,run
apt-cache show libcudnn8
and find and install the proper version of libcudnn8,and then wil solve the problem.
Hey did you fix it? Whats your base nvidia and cuda version in the ubuntu OS? This dockerfile was for Arch, most probably @kwxiaozhu solution would work.
Using the docker-gpu of the latest master branch of faceswap, when running to
python faceswap/faceswap.py train -A aobamadst -B trupdst -m model/
it is found that nvidia-smi in docker is basically not used, and the cpu usage rate is 100%