rosdocker 🤖 🐳

• rosdocker 🤖 🐳
  • Requirements
  • Usage
    • Configuration
    • Build and run
  • Available Images and Dockerfiles
    • 3DMR
    • pyslam
  • Troubleshooting
  • References
  • Credits

This repository contains a set of tools that simplifies the management of docker containers and (ROS) images with transparent support of NVIDIA drivers. You can play with it to create your own custom docker containers, smoothly open many terminals connected to them, and move from one container to another one. A docker cheatsheet is available here.

Main features:

• Run a container and transparently attach many terminals to it: text and color of the bash prompt will inform you where you are. Try it!
• Applications with a GUI can be smoothly run from each container terminal (NVIDIA drivers are transparently managed). For instance, you can run rviz from a run container terminal without any issue.
• Each container shares its network interface with the host.
• Your favorite working folder is conveniently mounted into the docker containers when you run them and you can easily build and run applications from there.

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Requirements

Check you have installed the nvidia-docker-toolkit:
$ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit-base

Usage

Configuration

In the file config.sh, set your favorite working folder WORKING_FOLDER_TO_MOUNT_IN_CONTAINER. This folder will be mounted from the host into the run container so that we can conveniently work ont it and preserve changes. By default:
WORKING_FOLDER_TO_MOUNT_IN_CONTAINER="$HOME/Work"

Build and run

Consider one of our available dockerfiles Dockerfile_<NAME>.

• Build the image:
  $ ./build.sh <NAME>
• Run the container from a first terminal (this spawns the container)
  $ ./run.sh <NAME>
• Open a new terminal and connect to the running container
  $ ./run.sh <NAME>
• Stop the container
  $ ./stop.sh <NAME>

For instance, if you want to build, run and stop the noetic container/image corresponding to Dockerfile_noetic, then run:
$ ./build.sh noetic
$ ./run.sh noetic
$ ./stop.sh noetic

NOTE1: When you update your Nvidia drivers, rebuild the image with the build.sh script.

NOTE2: any change made outside of our folder WORKING_FOLDER_TO_MOUNT_IN_CONTAINER from within the docker environment will not persist. If you want to add additional binary packages without having to reinstall them each time, add them to the Dockerfile and rebuild. This is a well known rule for docker users.

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Available Images and Dockerfiles

Each image <NAME> listed below corresponds to a dockerfile: Dockerfile_<NAME>.

• ubuntu18 built on top of ubuntu:18.04 (no ROS)
• ubuntu18_cuda built on top of nvidia/cuda:11.8.0-devel-ubuntu18.04 (no ROS)
• melodic built on top of ros:melodic-ros-base-bionic (ubuntu:18.04)
• noetic built on top of ros:noetic-ros-base-focal (ubuntu:20.04 with ROS)
• noetic_cuda built on top of nvidia/cuda:12.2.2-devel-ubuntu20.04 (ubuntu:20.04 with ROS and CUDA)
• noetic_3dmr built on top of the previous noetic and installing all the deps of 3DMR (ubuntu:20.04)
• ubuntu20 built on top of ubuntu:20.04 (no ROS)
• ubuntu20_conda built on top of ubuntu:20.04 with conda for managing python (no ROS)
• ubuntu20_cuda built on top of nvidia/cuda:12.2.2-devel-ubuntu20.04 (no ROS, with CUDA)
• pyslam built on top of ubuntu:20.04 and installing all the deps of pyslam => you can now pull the image from here
• pyslam_cuda built on top of nvidia/cuda:12.1.0-devel-ubuntu20.04 and installing all the deps of pyslam with CUDA support => you can now pull the image from here
• humble built on the top of ros:humble (ubuntu:22.04)
• ubuntu22 built on top of ubuntu:22.04 (no ROS)
• ubuntu22_cuda built on top of nvidia/cuda:11.8.0-devel-ubuntu22.04 (no ROS, with CUDA)
• ubuntu24 built on top of ubuntu:24.04 (no ROS)
• ubuntu24_cuda built on top of nvidia/cuda:12.5.1-devel-ubuntu24.04 (no ROS, with CUDA)

An old heritage:

• tradr (built on top of ros:indigo) (ubuntu:14.04) (it was used during the TRADR program)

Now, you can easily add your new custom docker file.

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3DMR

In order to build the noetic_3dmr image, which can host the 3DMR project, check you are connected to the network and run these commands:
$ ./build.sh noetic
$ ./build.sh noetic_3dmr

Now, set your folder $WORKING_FOLDER_TO_MOUNT_IN_CONTAINER (see the configuration section). From your host, open a terminal and clone the 3DMR project into $WORKING_FOLDER_TO_MOUNT_IN_CONTAINER. Then, run the container noetic_3dmr:
$ ./run.sh noetic_3dmr.
Next, from within the run container, get into the folder $WORKING_FOLDER_TO_MOUNT_IN_CONTAINER, and build the workspace (see the instructions and scripts in 3DMR).

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pyslam

To build the pyslam / pyslam_cuda image, which can host the pyslam project, check you are connected to the network and run:
$ ./build.sh pyslam
or
$ ./build.sh pyslam_cuda if your system supports NVIDIA CUDA
Then, open a terminal and run:
$ ./run.sh pyslam
or
$ ./run.sh pyslam_cuda if you built the image with NVIDIA CUDA support.
Now, within the run container, you can find a copy of pyslam ready to be used in your user's home folder.

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Troubleshooting

If you get the following error related to permission problems:

Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Post ": dial unix /var/run/docker.sock: connect: permission denied

then run the following commands:

$ sudo groupadd -f docker
$ sudo usermod -aG docker $USER
$ newgrp docker
$ sudo service docker restart

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References

• I created a docker cheatsheet here.
• Nice intro/demo for docker: https://www.youtube.com/watch?v=XcJzOYe3E6M
• Install Nvidia container toolkit:
  https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
• Permission denied issue:
  https://phoenixnap.com/kb/docker-permission-denied
• Nice cheatsheet/tutorials: https://www.edureka.co/blog/docker-commands/#rm https://shisho.dev/blog/posts/docker-remove-cheatsheet/
• NVIDIA CUDA and cuDNN images from gitlab.com/nvidia/cuda
  https://hub.docker.com/r/nvidia/cuda

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Credits

This repository was initially inspired by https://github.com/jbohren/rosdocked. Thanks to its Author. I've been using and improving this repo in the background for years. Now, it's time to share it back.