I would not recommend installing CUDA/cuDNN on the host if you can use docker. Since at least CUDA 8 it has been possible to "stand on the shoulders of giants" and use nvidia/cuda
base images maintained by NVIDIA in their Docker Hub repo. Go for the newest and biggest one (with cuDNN if doing deep learning) if unsure which version to choose.
A starter CUDA container:
mkdir ~/cuda11
cd ~/cuda11
echo "FROM nvidia/cuda:11.0-cudnn8-devel-ubuntu18.04" > Dockerfile
echo "CMD [\"/bin/bash\"]" >> Dockerfile
docker build --tag mirekphd/cuda11 .
docker run --rm -it --gpus 1 mirekphd/cuda11 nvidia-smi
Sample output:
(if nvidia-smi
is not found in the container, do not try install it there - it was already installed on thehost with NVIDIA GPU driver and should be made available from the host to the container system if docker has access to the GPU(s)):
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.57 Driver Version: 450.57 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 00000000:01:00.0 On | N/A |
| 0% 50C P8 17W / 280W | 409MiB / 11177MiB | 7% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
Prerequisites
Appropriate NVIDIA driver with the latest CUDA version support to be installed first on the host (download it from NVIDIA Driver Downloads and then mv driver-file.run driver-file.sh && chmod +x driver-file.sh && ./driver-file.sh
). These are have been forward-compatible since CUDA 10.1.
GPU access enabled in docker
by installing sudo apt get update && sudo apt get install nvidia-container-toolkit
(and then restarting docker daemon using sudo systemctl restart docker
).