Docker Setup Instruction

8 minute read

Published: October 16, 2023

This guide provides instructions on installing the Docker engine on Ubuntu, alongside essential commands for frequent use. It also covers setting up environments for PyTorch and TensorFlow, optimizing GPU usage, and managing the export and import of containers.

1. Install Docker Engine on Ubuntu

1.1 Prerequisites

1.1.1 OS requirements

To install Docker Engine, you need the 64-bit version of one of these Ubuntu versions:

Ubuntu Lunar 23.04
Ubuntu Kinetic 22.10
Ubuntu Jammy 22.04 (LTS)
Ubuntu Focal 20.04 (LTS)

Docker Engine for Ubuntu is compatible with x86_64 (or amd64), armhf, arm64, s390x, and ppc64le (ppc64el) architectures.

# check ubuntu version
lsb_release -a

No LSB modules are available.
Distributor ID:	Ubuntu
Description:	Ubuntu 22.04.2 LTS
Release:	    22.04
Codename:	    jammy

1.1.2 Uninstall conflicting packages

for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done

apt-get might report that you have none of these packages installed.

If the following results appear:

You might want to run 'apt --fix-broken install' to correct these.
The following packages have unmet dependencies:
 nvidia-dkms-535 : Depends: nvidia-kernel-common-535 (>= 535.104.05) but 535.86.10-0ubuntu1 is to be installed
 nvidia-driver-535 : Depends: libnvidia-gl-535 (= 535.104.05-0ubuntu0.22.04.4) but 535.86.10-0ubuntu1 is to be installed
                     Depends: nvidia-kernel-common-535 (>= 535.104.05) but 535.86.10-0ubuntu1 is to be installed
                     Recommends: libnvidia-compute-535:i386 (= 535.104.05-0ubuntu0.22.04.4)
                     Recommends: libnvidia-decode-535:i386 (= 535.104.05-0ubuntu0.22.04.4)
                     Recommends: libnvidia-encode-535:i386 (= 535.104.05-0ubuntu0.22.04.4)
                     Recommends: libnvidia-fbc1-535:i386 (= 535.104.05-0ubuntu0.22.04.4)
                     Recommends: libnvidia-gl-535:i386 (= 535.104.05-0ubuntu0.22.04.4)
E: Unmet dependencies. Try 'apt --fix-broken install' with no packages (or specify a solution).

Try:

# First
sudo apt update && sudo apt upgrade -y
# Second
sudo apt --fix-broken install
# Third
sudo apt autoremove

1.2 Installation

Docker Engine comes bundled with Docker Desktop for Linux. This is the easiest and quickest way to get started.
Set up and install Docker Engine from Docker’s Apt repository.

1.2.1 Docker’s Apt repository

Before you install Docker Engine for the first time on a new host machine, you need to set up the Docker repository. Afterward, you can install and update Docker from the repository.

Set up Docker’s Apt repository.

# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

# Add the repository to Apt sources:
echo \
  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

Install the Docker packages.

# Latest Version
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
# Specific Version
# List the available versions:
apt-cache madison docker-ce | awk '{ print $3 }'
# Select the desired version and install:
VERSION_STRING=5:24.0.0-1~ubuntu.22.04~jammy
sudo apt-get install docker-ce=$VERSION_STRING docker-ce-cli=$VERSION_STRING containerd.io docker-buildx-plugin docker-compose-plugin

Verify that the Docker Engine installation is successful by running the hello-world image.

sudo docker run hello-world

1.3 Upgrade and Uninstall

1.3.1 Upgrade Docker Engine

To upgrade Docker Engine, follow step 2 of the installation instructions, choosing the new version you want to install.

1.3.2 Uninstall Docker Engine

Uninstall the Docker Engine, CLI, containerd, and Docker Compose packages:

sudo apt-get purge docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-ce-rootless-extras

Images, containers, volumes, or custom configuration files on your host aren’t automatically removed. To delete all images, containers, and volumes:

sudo rm -rf /var/lib/docker
sudo rm -rf /var/lib/containerd

You have to delete any edited configuration files manually.

2 Fundamental Commands

display the containers currently running:

docker ps

display all the containers (even those not running anymore):

docker ps -a

display the images locally saved:

docker images

remove a docker container:

docker stop container_name # if container is running
docker rm container_name

remove all docker containers (not running anymore):

docker container prune

remove an image:

docker rmi image_name

remove all docker images (be very careful with this one!):

docker image prune -a

3 PyTorch on Docker

3.1 PyTorch Image

In Github, there are some repositories created by good developers. PyTorch Docker image is repository has many PyTorch images for most of the previous versions with compatible CUDA versions.

# docker pull image_name:image_tag
docker pull pytorch/pytorch:latest
docker pull pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
docker pull pytorch/pytorch:2.1.0-cuda12.1-cudnn8-devel

The latest image comes with the latest stable versions of PyTorch, CUDA and cuDNN. There are also other tags of the form X-cuda-Y-cudnn-Z-runtime/devel, where X is the pytorch version, Y is the CUDA version and Z is the cuDNN version. The images tagged with devel come preinstalled with various compiler configurations.

4 TensorFlow on Docker

TensorFlow provides a number of images depending on your use case such as latest, nightly and devel. Assuming you have Docker installed on your computer we can download these images using commands such as

docker pull tensorflow/tensorflow 
docker pull tensorflow/tensorflow:latest-gpu

These commands will install the latest stable release and the latest GPU compatible release respectively. For more detailed instructions please refer to the official documentation.

5 Using GPU on PyTorch Image

5.1 Prerequisites

Create an environment where the host can use the GPU. To test the installation, run:

nvidia-smi

5.2 Installing Nvidia Container Toolkit

Docker containers share your host’s kernel but bring their own operating system and software packages. This means they lack the NVIDIA drivers used to interface with your GPU. Docker doesn’t even add GPUs to containers by default, so a plain Docker run won’t see your hardware at all.

To get your GPU to work, you’ll have to install the drivers within your image and then instruct Docker to add GPU devices to your containers at runtime.

You’ll add NVIDIA Container Toolkit to your machine to enable your docker container to use your GPUs. This integrates into Docker Engine to automatically configure your containers for GPU support.

Setup the package 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

Install the nvidia-container-toolkit package (and dependencies) after updating the package listing:

sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

Now, configure the Docker daemon to recognize the NVIDIA Container Runtime:

sudo nvidia-ctk runtime configure --runtime=docker

Restart the Docker daemon to complete the installation after setting the default runtime:

sudo systemctl restart docker

At this point, the NVIDIA Container Toolkit is up and running, you’re ready to test its operation.

5.3 Docker with GPU

Docker doesn’t provide your system’s GPUs by default, you need to create containers with the --gpus flag for your hardware to show up. The nvidia/cuda images are preconfigured with the CUDA binaries and GPU tools.

sudo docker run -t -i -d --gpus all pytorch/pytorch

docker run = docker pull + docker create + docker start

You’ll get the CONTAINER ID like:

4056fed7496b7b88872f43f6087c7b76415eceff06f86a72f61f994b0357da63

Enter the container:

sudo docker exec -it 4056 /bin/bash # 4056 is the CONTAINER ID

To check if Docker can access your GPU, run the nvidia-smi command in your container.

nvidia-gpu

6 Docker Export and Import

If you want to save the entire image, including all its history and tags, then you should use the docker save and docker load commands. And if you just want to save the current state of a container, then you should use the docker export and docker import commands.

6.1 Docker Container Export and Import

This operation will package all the image layers and metadata into a tar file. You can then use the docker load command to import this tar file into any Docker environment.

sudo docker export <container ID> > mycontainer.tar
# sudo docker export e2ca7008de40 > hallu_container.tar

This approach is primarily used to share or migrate an entire image, including all versions, tags, and history.

6.1.2 Import and Use

Import as an image

sudo docker import mycontainer.tar <image name>
# sudo docker import hallu_container.tar hallu_backup

Create a container with the same command as original one

sudo docker run <image name> /bin/bash
# sudo docker run -t -i -d --gpus all hallu_backup:latest /bin/bash

6.2 Docker Image Export and Import

The docker export command exports the file system of a running container to a tar file. You can then use the docker import command to import this tar file as a new image.

sudo docker save -o <save path>/myimage.tar myimage:latest

sudo docker load -i <path>/myimage.tar

This approach is primarily used to share or migrate the current state of a container. This does not include the container’s history or metadata, such as environment variables, so it is often used to take a snapshot of the container.

References

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