• Failure Prediction
  • Prerequisite
    • Visual Studio Code
    • Docker
    • Nvidia Container Toolkit
  • Build and Run the Container
  • Test the image

Failure Prediction

Prerequisite

This project requires you to have a linux (preferably debian based) host running on an x86/amd64 platform.
Additionally, the following requirements need to be met:

• Visual Studio Code
• Docker
• Nvidia Container Toolkit for Docker

Please refer to the individual sections for detailed instructions.

Visual Studio Code

• Download the .deb package for VS Code from this link.
• Make sure you are using the .deb package distribution and not using VS Code downloaded from snap or other distribution channels. Once you have downloaded the .deb package, you can run the following command (for debian based host):

  sudo dpkg -i <path/to/code*.deb>
  

• Next, install the Dev Container extension in VS Code.
• For documentation on VS Code dev container, see this.

Docker

• Please follow the instructions in this link to setup docker on your system.
• It is recommended to install only the docker engine. You do not need to install docker desktop.
• You can use the provided .deb package for the docker installation. Or alternatively, install it using the following commands (recommended):

  # update your package database
  sudo apt-get update
  
  # installed required packages
  sudo apt-get install \
  apt-transport-https \
  ca-certificates \
  curl \
  software-properties-common
  
  # add docker's official gpg repository
  curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
  
  # setup the stable docker repository
  sudo add-apt-repository \
  "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) \
  stable"
  
  # update your package database again
  sudo apt-get update
  
  # install docker
  sudo apt-get install -y docker-ce
  

• Post installation of docker engine, you need to setup a docker group with your local user.

  # add current user to docker group
  sudo usermod -aG docker $USER
  
  # start new shell session with docker group as the primary group
  newgrp docker
  
  # start docker
  sudo systemctl start docker
  
  # enable docker to start at boot
  sudo systemctl enable docker
  
  # verify docker installation
  docker version
  

Nvidia Container Toolkit

• To allow gpu access from within a docker container, you need to have the nvidia container toolkit installed.
• You can get install it by following this link.

sudo apt-get purge nvidia-docker2 nvidia-container-toolkit
sudo apt-get autoremove
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

• Make sure to configure the docker after installation (see this).
• Troubleshoot: If the MuJoCo environment fails to start, you can try to set this value no-cgroups = false, in the /etc/nvidia-container-runtime/config.toml file.

Build and Run the Container

• After you have the prerequisites set up, you can build the image and launch the container.
• Press ctrl + shift + P to bring up the command palette in VS Code.
• Select the Rebuild and reopen in the Container Command.
• Alternatively, if you see the below popup, click on the Reopen in Container button.

Test the Image

• After the image builds, open a terminal in VS Code and run python3 -m mujoco.viewer.
• You should see an empty MuJoCo viewer window as shown below.