This post is the second part in setting up of RAPIDS AI on a ASUS laptop. You can find the first part here. So, after the setup I was ready to go forward with the RAPIDS container, However, I was faced with this error :
$ docker run –gpus all –rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.7
docker: Error response from daemon: could not select device driver “” with capabilities: [[gpu]].
CUDA seems to be working fine:
$ nvcc –version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
The driver is working as well, However, docker does not seem to be recognizing the gpu. Docker version 19.03 onwards have inbuilt NVIDIA capabilities and my Docker version is 19.03.3. So we are good there. I believe the issue is the “nvidia-container-toolkit“. There was the issue of using 19.10 version again, since the highest Ubuntu version that is supported is 18.04. So, replaced dist variable with 18.04 in the commands from the website :
# Add the package repositories $ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) $ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - $ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list $ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit $ sudo systemctl restart docker
Now, lets test docker again with the command :
$ docker run -it –rm –gpus all ubuntu nvidia-smi
And Voila !!!
Next, I had to run the Jupyter notebook server manually using: “bash /rapids/notebooks/utils/start-jupyter.sh”. And going to http://127.0.0.1:8888/ on the browser leads to the right place.
Now onto doing some real stuff. Will update with more soon.