Getting Started
For installing Docker CE, follow the official instructions for your supported Linux distribution. For convenience, the documentation below includes instructions on installing Docker for various Linux distributions.
Warning
If you are migrating fron nvidia-docker 1.0, then follow the instructions in the Migration from nvidia-docker 1.0 guide.
Installing on Ubuntu and Debian
The following steps can be used to setup NVIDIA Container Toolkit on Ubuntu LTS - 16.04, 18.04, 20.4 and Debian - Stretch, Buster distributions.
Setting up Docker
Docker-CE on Ubuntu can be setup using Docker’s official convenience script:
$ curl https://get.docker.com | sh \
&& sudo systemctl --now enable docker
See also
Follow the official instructions for more details and post-install actions.
Setting up NVIDIA Container Toolkit
Setup the stable repository and the GPG key:
$ 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
Note
To get access to experimental features such as CUDA on WSL or the
new MIG capability on A100,
you may want to add the experimental branch to the repository listing:
$ curl -s -L https://nvidia.github.io/nvidia-container-runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
Install the nvidia-docker2 package (and dependencies) after updating the package listing:
$ sudo apt-get update
$ sudo apt-get install -y nvidia-docker2
Restart the Docker daemon to complete the installation after setting the default runtime:
$ sudo systemctl restart docker
At this point, a working setup can be tested by running a base CUDA container:
$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
This should result in a console output shown below:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 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 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Installing on CentOS 7/8
The following steps can be used to setup the NVIDIA Container Toolkit on CentOS 7/8.
Setting up Docker on CentOS 7/8
Note
If you’re on a cloud instance such as EC2, then the official CentOS images may not include
tools such as iptables which are required for a successful Docker installation. Try this command to get a more functional VM,
before proceeding with the remaining steps outlined in this document.
$ sudo dnf install -y tar bzip2 make automake gcc gcc-c++ vim pciutils elfutils-libelf-devel libglvnd-devel iptables
Setup the official Docker CE repository:
$ sudo dnf config-manager --add-repo=https://download.docker.com/linux/centos/docker-ce.repo
$ sudo yum-config-manager --add-repo=https://download.docker.com/linux/centos/docker-ce.repo
Now you can observe the packages available from the docker-ce repo:
$ sudo dnf repolist -v
$ sudo yum repolist -v
Since CentOS does not support specific versions of containerd.io packages that are required for newer versions
of Docker-CE, one option is to manually install the containerd.io package and then proceed to install the docker-ce
packages.
Install the containerd.io package:
$ sudo dnf install -y https://download.docker.com/linux/centos/7/x86_64/stable/Packages/containerd.io-1.4.3-3.1.el7.x86_64.rpm
$ sudo yum install -y https://download.docker.com/linux/centos/7/x86_64/stable/Packages/containerd.io-1.4.3-3.1.el7.x86_64.rpm
And now install the latest docker-ce package:
$ sudo dnf install docker-ce -y
$ sudo yum install docker-ce -y
Ensure the Docker service is running with the following command:
$ sudo systemctl --now enable docker
And finally, test your Docker installation by running the hello-world container:
$ sudo docker run --rm hello-world
This should result in a console output shown below:
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
0e03bdcc26d7: Pull complete
Digest: sha256:7f0a9f93b4aa3022c3a4c147a449bf11e0941a1fd0bf4a8e6c9408b2600777c5
Status: Downloaded newer image for hello-world:latest
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/get-started/
Setting up NVIDIA Container Toolkit
Setup the stable repository and the GPG key:
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo
Install the nvidia-docker2 package (and dependencies) after updating the package listing:
$ sudo dnf clean expire-cache --refresh
$ sudo yum clean expire-cache
$ sudo dnf install -y nvidia-docker2
$ sudo yum install -y nvidia-docker2
Restart the Docker daemon to complete the installation after setting the default runtime:
$ sudo systemctl restart docker
At this point, a working setup can be tested by running a base CUDA container:
$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
This should result in a console output shown below:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 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 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Installing on RHEL 7
The following steps can be used to setup the NVIDIA Container Toolkit on RHEL 7.
Setting up Docker on RHEL 7
RHEL includes Docker in the Extras repository. To install Docker on RHEL 7, first enable this repository:
$ sudo subscription-manager repos --enable rhel-7-server-extras-rpms
Docker can then be installed using yum
$ sudo yum install docker -y
See also
More information is available in the KB article.
Ensure the Docker service is running with the following command:
$ sudo systemctl --now enable docker
And finally, test your Docker installation. We can query the version info:
$ sudo docker -v
You should see an output like below:
Docker version 1.13.1, build 64e9980/1.13.1
And run the hello-world container:
$ sudo docker run --rm hello-world
Giving you the following result:
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/get-started/
Setting up NVIDIA Container Toolkit
Setup the stable repository and the GPG key:
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo
On RHEL 7, install the nvidia-container-toolkit package (and dependencies) after updating the package listing:
$ sudo yum clean expire-cache
$ sudo yum install nvidia-container-toolkit -y
Note
On POWER (ppc64le) platforms, the following package should be used: nvidia-container-hook instead of nvidia-container-toolkit
Restart the Docker daemon to complete the installation after setting the default runtime:
$ sudo systemctl restart docker
At this point, a working setup can be tested by running a base CUDA container:
$ sudo docker run --rm -e NVIDIA_VISIBLE_DEVICES=all nvidia/cuda:11.0-base nvidia-smi
This should result in a console output shown below:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 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 Tesla T4 Off | 00000000:00:1E.0 Off | 0 |
| N/A 43C P0 20W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Note
Depending on how your RHEL 7 system is configured with SELinux, you may have to use --security-opt=label=disable on
the Docker command line to share parts of the host OS that can not be relabeled. Without this option, you may observe this
error when running GPU containers: Failed to initialize NVML: Insufficient Permissions. However, using this option disables
SELinux separation in the container and the container is executed in an unconfined type. Review the SELinux policies
on your system.
Installing on SUSE 15
The following steps can be used to setup the NVIDIA Container Toolkit on SUSE SLES 15 and OpenSUSE Leap 15.
Setting up Docker on SUSE 15
To install the latest Docker 19.03 CE release on SUSE 15 (OpenSUSE Leap or SLES), you can use the Virtualization::containers
project.
First, set up the repository:
$ sudo zypper addrepo https://download.opensuse.org/repositories/Virtualization:containers/openSUSE_Leap_15.2/Virtualization:containers.repo \
&& sudo zypper refresh
Install the docker package:
$ sudo zypper install docker
Ensure the Docker service is running with the following command:
$ sudo systemctl --now enable docker
And finally, test your Docker installation by running the hello-world container:
$ sudo docker run --rm hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
0e03bdcc26d7: Pull complete
Digest: sha256:7f0a9f93b4aa3022c3a4c147a449bf11e0941a1fd0bf4a8e6c9408b2600777c5
Status: Downloaded newer image for hello-world:latest
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/get-started/
Setting up NVIDIA Container Toolkit
Setup the stable repository and the GPG key:
Note
You may have to set $distribution variable to opensuse-leap15.1 in the command below
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& sudo zypper ar https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo
Install the nvidia-docker2 package (and dependencies) after updating the package listing:
$ sudo zypper refresh
$ sudo zypper install -y nvidia-docker2
Restart the Docker daemon to complete the installation after setting the default runtime:
$ sudo systemctl restart docker
At this point, a working setup can be tested by running a base CUDA container:
$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
This should result in a console output shown below:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 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 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Installing on Amazon Linux
The following steps can be used to setup the NVIDIA Container Toolkit on Amazon Linux 1 and Amazon Linux 2.
Setting up Docker on Amazon Linux
Amazon Linux is available on Amazon EC2 instances. For full install instructions, see Docker basics for Amazon ECS.
After launching the official Amazon Linux EC2 image, update the installed packages and install the most recent Docker CE packages:
$ sudo yum update -y
Install the docker package:
$ sudo amazon-linux-extras install docker
Ensure the Docker service is running with the following command:
$ sudo systemctl --now enable docker
And finally, test your Docker installation by running the hello-world container:
$ sudo docker run --rm hello-world
This should result in a console output shown below:
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
0e03bdcc26d7: Pull complete
Digest: sha256:7f0a9f93b4aa3022c3a4c147a449bf11e0941a1fd0bf4a8e6c9408b2600777c5
Status: Downloaded newer image for hello-world:latest
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/get-started/
Setting up NVIDIA Container Toolkit
Setup the stable repository and the GPG key:
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo
Install the nvidia-docker2 package (and dependencies) after updating the package listing:
$ sudo yum clean expire-cache
Restart the Docker daemon to complete the installation after setting the default runtime:
$ sudo systemctl restart docker
At this point, a working setup can be tested by running a base CUDA container:
$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
This should result in a console output shown below:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 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 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+