Cuda toolkit docker11/24/2023 Just to be fair, it is mentioned here…) 4. (I also installed nvidia-docker2 on the server before adding this line to the jupyterhub-config, but I am not sure if this was necessary and did any contribution to this solution. ![]() RUN echo "/usr/local/nvidia/lib" > /etc/ld.so.conf.d/nf & \Įcho "/usr/local/nvidia/lib64" > /etc/ld.so.conf.d/nfĮNV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$Īfter adding this line to the jupyterhub_config.py and restarting the jupyterhub service, all GPUs were visible wihtn the container and nvidia-smi showed all avaliable GPU-devices as expected. # For libraries in the cuda-compat-* package: RUN apt-get update & apt-get install -y -no-install-recommends \Įcho "deb /" > /etc/apt//cuda.list & \Įcho "deb /" > /etc/apt//nvidia-ml.list & \ĮNV CUDA_PKG_VERSION 10-1=$CUDA_VERSION-1 | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. Here ist the info for the GPUs: nvidia-smi This means, only max 4 containers run with GPU enables (each one of the Teslas) and the other containers have no GPU. We have 4 Tesla-GPUS, thet we want to enable within 4 docker-containers on the jupyterhub. Systemuser spawner, as we want to enable to access to home directories of the users that are logged into jupyterhub The following nvidia packages are installed on the headles server:ĭocker-ce-cli 5:19.03.2~3-0~ubuntu-bionic We have installed jupyterhub version 1.0.0 For our appication we use a kernel/env other than “base”. These evironments are accessible within the jupyter notebook through different installed kernels, that one may select when running the notebook. ![]() ![]() Miniconda 4.7.10 on a each user base (under each user dir a separate installation, so that each use has his own environments asf.) Maybe it is important to say, that I have different conda environments installed on the host for the uses which spawns the container. Older builds of CUDA, Docker, and the NVIDIA drivers may require additional steps. We are using a headless Ubuntu server 18.0.4 LTS. The latest release of NVIDIA Container Toolkit is designed for combinations of CUDA 10 and Docker Engine 19.03 and later. We cannot access a GPU in our docker container spawned with jupyterhub… 1.) The situation: I saw several Q&As on this topic and tried both approaches.
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