Using GPU from a docker container?
I’m searching for a way to use the GPU from inside a docker container.
The container will execute arbitrary code so i don’t want to use the privileged mode.
From previous research i understood that
run -v and/or LXC
cgroup was the way to go but i’m not sure how to pull that off exactly
5 Solutions collect form web for “Using GPU from a docker container?”
Regan’s answer is great, but it’s a bit out of date, since the correct way to do this is avoid the lxc execution context as Docker has dropped LXC as the default execution context as of docker 0.9.
Instead it’s better to tell docker about the nvidia devices via the –device flag, and just use the native execution context rather than lxc.
These instructions were tested on the following environment:
- Ubuntu 14.04
- CUDA 6.5
- AWS GPU instance.
Install nvidia driver and cuda on your host
See CUDA 6.5 on AWS GPU Instance Running Ubuntu 14.04 to get your host machine setup.
$ sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9 $ sudo sh -c "echo deb https://get.docker.com/ubuntu docker main > /etc/apt/sources.list.d/docker.list" $ sudo apt-get update && sudo apt-get install lxc-docker
Find your nvidia devices
ls -la /dev | grep nvidia crw-rw-rw- 1 root root 195, 0 Oct 25 19:37 nvidia0 crw-rw-rw- 1 root root 195, 255 Oct 25 19:37 nvidiactl crw-rw-rw- 1 root root 251, 0 Oct 25 19:37 nvidia-uvm
Run Docker container with nvidia driver pre-installed
I’ve created a docker image that has the cuda drivers pre-installed. The dockerfile is available on dockerhub if you want to know how this image was built.
You’ll want to customize this command to match your nvidia devices. Here’s what worked for me:
$ sudo docker run -ti --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm tleyden5iwx/ubuntu-cuda /bin/bash
Verify CUDA is correctly installed
This should be run from inside the docker container you just launched.
Install CUDA samples:
$ cd /opt/nvidia_installers $ ./cuda-samples-linux-6.5.14-18745345.run -noprompt -cudaprefix=/usr/local/cuda-6.5/
Build deviceQuery sample:
$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery $ make $ ./deviceQuery
If everything worked, you should see the following output:
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 1, Device0 = GRID K520 Result = PASS
Ok i finally managed to do it without using the –privileged mode.
I’m running on ubuntu server 14.04 and i’m using the latest cuda (6.0.37 for linux 13.04 64 bits).
Install nvidia driver and cuda on your host. (it can be a little tricky so i will suggest you follow this guide https://askubuntu.com/questions/451672/installing-and-testing-cuda-in-ubuntu-14-04)
ATTENTION : It’s really important that you keep the files you used for the host cuda installation
Get the Docker Daemon to run using lxc
We need to run docker daemon using lxc driver to be able to modify the configuration and give the container access to the device.
One time utilization :
sudo service docker stop sudo docker -d -e lxc
Modify your docker configuration file located in /etc/default/docker
Change the line DOCKER_OPTS by adding ‘-e lxc’
Here is my line after modification
DOCKER_OPTS="--dns 184.108.40.206 --dns 220.127.116.11 -e lxc"
Then restart the daemon using
sudo service docker restart
How to check if the daemon effectively use lxc driver ?
The Execution Driver line should look like that :
Execution Driver: lxc-1.0.5
Build your image with the NVIDIA and CUDA driver.
Here is a basic Dockerfile to build a CUDA compatible image.
FROM ubuntu:14.04 MAINTAINER Regan <http://stackoverflow.com/questions/25185405/using-gpu-from-a-docker-container> RUN apt-get update && apt-get install -y build-essential RUN apt-get --purge remove -y nvidia* ADD ./Downloads/nvidia_installers /tmp/nvidia > Get the install files you used to install CUDA and the NVIDIA drivers on your host RUN /tmp/nvidia/NVIDIA-Linux-x86_64-331.62.run -s -N --no-kernel-module > Install the driver. RUN rm -rf /tmp/selfgz7 > For some reason the driver installer left temp files when used during a docker build (i don't have any explanation why) and the CUDA installer will fail if there still there so we delete them. RUN /tmp/nvidia/cuda-linux64-rel-6.0.37-18176142.run -noprompt > CUDA driver installer. RUN /tmp/nvidia/cuda-samples-linux-6.0.37-18176142.run -noprompt -cudaprefix=/usr/local/cuda-6.0 > CUDA samples comment if you don't want them. RUN export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64 > Add CUDA library into your PATH RUN touch /etc/ld.so.conf.d/cuda.conf > Update the ld.so.conf.d directory RUN rm -rf /temp/* > Delete installer files.
Run your image.
First you need to identify your the major number associated with your device.
Easiest way is to do the following command :
ls -la /dev | grep nvidia
If the result is blank, use launching one of the samples on the host should do the trick.
The result should look like that
As you can see there is a set of 2 numbers between the group and the date.
These 2 numbers are called major and minor numbers (wrote in that order) and design a device.
We will just use the major numbers for convenience.
Why do we activated lxc driver?
To use the lxc conf option that allow us to permit our container to access those devices.
The option is : (i recommend using * for the minor number cause it reduce the length of the run command)
–lxc-conf=’lxc.cgroup.devices.allow = c [major number]:[minor number or *] rwm’
So if i want to launch a container (Supposing your image name is cuda).
docker run -ti --lxc-conf='lxc.cgroup.devices.allow = c 195:* rwm' --lxc-conf='lxc.cgroup.devices.allow = c 243:* rwm' cuda
We just released an experimental GitHub repository which should ease the process of using NVIDIA GPUs inside Docker containers.
Updated for cuda-8.0 on ubuntu 16.04
Build the following image that includes the nvidia drivers and the cuda toolkit
FROM ubuntu:16.04 MAINTAINER Jonathan Kosgei <firstname.lastname@example.org> # A docker container with the Nvidia kernel module and CUDA drivers installed ENV CUDA_RUN https://developer.nvidia.com/compute/cuda/8.0/prod/local_installers/cuda_8.0.44_linux-run RUN apt-get update && apt-get install -q -y \ wget \ module-init-tools \ build-essential RUN cd /opt && \ wget $CUDA_RUN && \ chmod +x cuda_8.0.44_linux-run && \ mkdir nvidia_installers && \ ./cuda_8.0.44_linux-run -extract=`pwd`/nvidia_installers && \ cd nvidia_installers && \ ./NVIDIA-Linux-x86_64-367.48.run -s -N --no-kernel-module RUN cd /opt/nvidia_installers && \ ./cuda-linux64-rel-8.0.44-21122537.run -noprompt # Ensure the CUDA libs and binaries are in the correct environment variables ENV LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64 ENV PATH=$PATH:/usr/local/cuda-8.0/bin RUN cd /opt/nvidia_installers &&\ ./cuda-samples-linux-8.0.44-21122537.run -noprompt -cudaprefix=/usr/local/cuda-8.0 &&\ cd /usr/local/cuda/samples/1_Utilities/deviceQuery &&\ make WORKDIR /usr/local/cuda/samples/1_Utilities/deviceQuery
- Run your container
sudo docker run -ti --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm <built-image> ./deviceQuery
You should see output similar to:
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GRID K520
Result = PASS
Recent enhancements by NVIDIA have produced a much more robust way to do this.
Essentially they have found a way to avoid the need to install the CUDA/GPU driver inside the containers and have it match the host kernel module.
Instead, drivers are on the host and the containers don’t need them.
It requires a modified docker-cli right now.
This is great, because now containers are much more portable.
A quick test on Ubuntu:
# Install nvidia-docker and nvidia-docker-plugin wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb # Test nvidia-smi nvidia-docker run --rm nvidia/cuda nvidia-smi
For more details see:
GPU-Enabled Docker Container