Docker Nvidia GL + CUDA

Docker containers with nvidia drivers that allow CUDA and GL applications;

We assume that:

• 1: you have NVIDIA drivers installed.
• 2: if you are on optimus system with bumblebee use the bumblebee branch of this repository.

First, make sure the drivers are working. Install mesa-utils with apt install mesa-utils. If mesa-utils is not available, try the glmark2 package. These are OpenGL benchmarking applications that display the rendering settings.

Now execute one of the commands below and make sure the output displays Nvidia information:

$ glxinfo | grep -i vendor OR $ glmark2

# Command output must display NVIDIA information

server glx vendor string: NVIDIA Corporation
client glx vendor string: primus
OpenGL vendor string: NVIDIA Corporation

This shows that your driver package is well configured and you will be OK to run applications within your GPU, otherwise you have to make this work before continuing.

Installing docker

Below we describe the steps to run it.

Step 1: Install docker: Install a docker version >= 19. On Ubuntu 18.04 you can have it by installing the docker.io package.

$ apt install docker.io

Docker operates with sudo by default, but you can add your user do docker group to be able to run commands without sudo.

Create the docker group. $ sudo groupadd docker

Add your user to the docker group. $ sudo usermod -aG docker $USER

Log out and log back in so that your group membership is re-evaluated.

Verify that you can run docker commands without sudo, i.e.g. $ docker ps

Step 2: Install nvidia docker toolkit: To run docker with nvidia support we an additional package that allow using gpu profiles when executing docker containers.

You can refer to the official reposiyory, but here follows the instructions for Ubuntu 16.04/18.04/20.04, Debian Jessie/Stretch/Buster distros.

# 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

Step 3: Build the docker image that matches your driver: This step should be straightforward. But you need to check if the driver that you installed matches the docker image from the Dockerfile.

The image set is the nvidia/cudagl:10.1-base-ubuntu18.04. If you have installed a driver version prior to 418.39, you have to change it as referred in the nvidia docker hub. Otherwise, just continue.

According to their documentation the supported images and drivers are:

Image Tag	Driver Version
10.1-{base,runtime,devel}	>= 418.39
10.0-{base,runtime,devel}	>= 410.48
9.2-{base,runtime,devel}	>= 396.26

After that, just run the build script. It will basically run the docker build command with a few arguments. It might take a few minutes to complete.

$ ./build

Step 4: Make sure your docker image can execute: We provide a script to execute commands in the container you just built. To verify that it is working, you can execute a simple command that checks the integration with the nvidia drivers.

$ ./run.sh nvidia-smi

Make sure it displays your nvidia-card information.

Sun Aug  2 19:42:28 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce 940MX       Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   30C    P0    N/A /  N/A |      5MiB /  4046MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

Step 5: Make sure you can execute GL applications with nvidia: Rendering is a separate thing and we need to make sure the container uses the Nvidia GPU card to render GL applications as well. You can check this with the same previous glxinfo command, but now from inside the container, to see if Nvidia is the driver in use:

$ ./run.sh glxinfo | grep -i vendor

If the output contains NVIDIA information CONGRATULATIONS!!!

Support

Successful installations

Below we list configurations where we correctly executed the project. Cuda version and GCC version only applies to native installation, since its fixed on docker to Cuda 10.1 and GCC 7.0.

OS	GPU	Driver Version	Optimus?	Docker image
Ubuntu 18.04	Nvidia 940mx	440	Yes	nvidia/cudagl:10.1-base-ubuntu18.04, nvidia/cudagl:10.1-devel-ubuntu18.04