OpenPose Runtime/Development Docker Image [Linux]

With these docker images you can have an isolated working environment to work on or run the OpenPose project.

More info in this blog post.

Getting Started

Non-GPU Image

Go to Build Image

NVIDIA docker

For machines that are using NVIDIA graphics cards we need to have the nvidia-docker-plugin.

IMPORTANT: This repo supports nvidia-docker version 2.x!!!

Install nvidia-docker-plugin

Assuming the NVIDIA drivers and Docker® Engine are properly installed (see installation)

Ubuntu 14.04/16.04/18.04, Debian Jessie/Stretch

# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker

# Add the package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi

CentOS 7 (docker-ce), RHEL 7.4/7.5 (docker-ce), Amazon Linux 1/2

If you are not using the official docker-ce package on CentOS/RHEL, use the next section.

# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo yum remove nvidia-docker

# Add the package repositories
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 nvidia-docker2 and reload the Docker daemon configuration
sudo yum install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi

If yum reports a conflict on /etc/docker/daemon.json with the docker package, you need to use the next section instead.

For docker-ce on ppc64le, look at the FAQ.

Arch-linux

# Install nvidia-docker and nvidia-docker-plugin
# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f

sudo rm /usr/bin/nvidia-docker /usr/bin/nvidia-docker-plugin

# Install nvidia-docker2 from AUR and reload the Docker daemon configuration
yaourt -S aur/nvidia-docker
sudo pkill -SIGHUP dockerd

# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi

Proceed only if nvidia-smi works

If the nvidia-smi test was successful you may proceed. Otherwise please visit the official NVIDIA support.

Build desired Docker Image

You can either browse to directory of the version you want to install and issue manually a docker build command or just use the makefile:

# Prints Help
make

# E.g. Build image with CUDA 10 and cuDNN 7 support
make openpose_cuda10_cudnn7

Note: The build process takes a while.

Running the image (as root)

Once the container has been built, you can issue the following command to run it:

docker run --rm -it --runtime=nvidia --privileged --net=host --ipc=host \
-v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \
-v $HOME/.Xauthority:/root/.Xauthority -e XAUTHORITY=/root/.Xauthority \
-v <PATH_TO_YOUR_OPENPOSE_PROJECT>:/root/openpose \
turlucode/openpose:cuda10-cudnn7

A terminator window will pop-up and the rest you know it! :)

Important Remark: This will launch the container as root. This might have unwanted effects! If you want to run it as the current user, see next section.

Running the image (as current user)

You can also run the script as the current linux-user by passing the DOCKER_USER_* variables like this:

docker run --rm -it --runtime=nvidia --privileged --net=host --ipc=host \
-v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \
-v $HOME/.Xauthority:/home/$(id -un)/.Xauthority -e XAUTHORITY=/home/$(id -un)/.Xauthority \
-e DOCKER_USER_NAME=$(id -un) \
-e DOCKER_USER_ID=$(id -u) \
-e DOCKER_USER_GROUP_NAME=$(id -gn) \
-e DOCKER_USER_GROUP_ID=$(id -g) \
-v <PATH_TO_YOUR_OPENPOSE_PROJECT>:/root/openpose \
turlucode/openpose:cuda10-cudnn7

Important Remark:

• Please note that you need to pass the Xauthority to the correct user's home directory.
• You may need to run xhost si:localuser:$USER or worst case xhost local:root if get errors like Error: cannot open display

Other options

Mount the local openpose project

To mount your local openpose you can just use the following docker feature:

# for root user
-v $HOME/<some_path>/openpose:/root/openpose
# for local user
-v $HOME/<some_path>/openpose:/home/$(id -un)/openpose

Mount PyCharm

If you use localy PyCharm you can also expose it to your docker image and used inside the container.

# Mount PyCharm program folder
-v $HOME/<some_path>/pycharm-community:/home/$(id -un)/pycharm-community
# Mount PyCharm config folder
-v $HOME/<some_path>/pycharm_config:/home/$(id -un)/.PyCharmCE2018.2

Important Remark: Note that depending on the PyCharm version you are using, the mounting point .PyCharmCE2018.2 might be different. Verify it first in your local machine.

Mount your ssh-keys

For both root and custom user use:

-v $HOME/.ssh:/root/.ssh

For the custom-user the container will make sure to copy them to the right location.

Passing a camera device

If you have a virtual device node like /dev/video0, e.g. a compatible usb camera, you pass this to the docker container like this:

--device /dev/video0

Building OpenPose

Assuming you either mounted the OpenPose project or clonned it inside docker, a typical build sequence is:

cd <openpose_project_root_folder>
mkdir build && cd build

cmake -DCMAKE_BUILD_TYPE=Release -DBUILD_PYTHON=ON ..
make -j8 && sudo make install

For more details check the official instructions

Base images

The images on this repository are based on the following work:

• nvidia-opengl
• nvidia-cuda - Hierarchy is base->runtime->devel

Issues and Contributing

• Please let us know by filing a new issue.
• You can contribute by opening a pull request.