Skip to main content

Software Prerequisites

Overview​

With your Ubuntu 22.04 workstation ready, it's time to configure the development environment. This chapter will guide you through installing and configuring the essential software stack for Physical AI development.

What We'll Install:

  1. Docker – Containerized environments for ROS 2 and Isaac Sim
  2. VS Code – Primary code editor with robotics extensions
  3. Python 3.10+ – Programming language for robot control
  4. Git – Version control for your code
  5. Essential Tools – Curl, wget, build tools

Estimated Time: 45 minutes

Why Docker?​

The Problem Docker Solves​

ROS 2 and robotics libraries have complex dependency chains:

ROS 2 Humble requires:
├── Python 3.10
├── Cyclone DDS
├── Gazebo 11
├── TF2 (Transform Library)
├── OpenCV 4.5+
├── ... (50+ packages)

Installing these manually:

  • Takes 3-4 hours
  • Breaks often (version conflicts)
  • Pollutes your system with global packages

Docker's Solution​

Docker containers are isolated environments that include:

  • All dependencies pre-installed
  • Specific versions locked (no conflicts)
  • Reproducible across machines

Analogy: Think of Docker as a "virtual environment on steroids" that includes not just Python packages, but the entire operating system, libraries, and configuration.

Installing Docker​

Step 1: Remove Old Versions (If Any)​

# Remove conflicting packages
sudo apt remove docker docker-engine docker.io containerd runc

Step 2: Install Docker from Official Repository​

# Update package index
sudo apt update

# Install prerequisites
sudo apt install -y \
    ca-certificates \
    curl \
    gnupg \
    lsb-release

# Add Docker's official GPG key
sudo mkdir -p /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | \
    sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

# Set up Docker repository
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
  https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# Install Docker Engine
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin

Step 3: Configure Docker Permissions​

By default, Docker requires sudo. Let's fix that:

# Add your user to the docker group
sudo usermod -aG docker $USER

# Apply group changes (logout/login or run this)
newgrp docker

# Verify (should work without sudo)
docker run hello-world

Expected Output:

Hello from Docker!
This message shows that your installation appears to be working correctly.
Why Not Use Snap?

Ubuntu offers Docker via Snap (snap install docker), but it has known issues with GPU passthrough for NVIDIA containers. Always use the official Docker repository.

Step 4: Install NVIDIA Container Toolkit​

This allows Docker containers to access your GPU:

# Add NVIDIA Container Toolkit repository
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 NVIDIA Docker runtime
sudo apt update
sudo apt install -y nvidia-container-toolkit

# Restart Docker
sudo systemctl restart docker

Step 5: Verify GPU Access in Docker​

# Run a test container with GPU access
docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi

Expected Output:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 535.54.03    Driver Version: 535.54.03    CUDA Version: 12.0   |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0  On |                  N/A |
|  0%   45C    P0    30W / 285W |   1024MiB / 12288MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
Troubleshooting GPU Access

If you see "could not select device driver," ensure:

  1. NVIDIA drivers are installed (nvidia-smi works outside Docker)
  2. You ran sudo systemctl restart docker after installing nvidia-container-toolkit
  3. You're using --gpus all flag in docker run command

Installing Visual Studio Code​

Step 1: Download and Install​

Method 1: Via Snap (Easiest)

sudo snap install --classic code

Method 2: Via .deb Package (Recommended)

# Download .deb package
wget -O code.deb 'https://code.visualstudio.com/sha/download?build=stable&os=linux-deb-x64'

# Install
sudo apt install ./code.deb

# Clean up
rm code.deb

Step 2: Launch VS Code​

code

Step 3: Install Essential Extensions​

Open the Extensions panel (Ctrl+Shift+X) and install:

  1. Python (Microsoft)

    • Syntax highlighting, IntelliSense, debugging

  2. Pylance (Microsoft)

    • Fast Python language server

  3. Docker (Microsoft)

    • Manage containers from VS Code

  4. ROS (Microsoft)

    • ROS 2 syntax, debugging, visualization

  5. C/C++ (Microsoft)

    • Required for compiling ROS 2 packages

  6. CMake Tools (Microsoft)

    • Build system integration

  7. XML (Red Hat)

    • For URDF robot description files

  8. YAML (Red Hat)

    • For ROS 2 launch files and configuration

Quick Install via CLI:

code --install-extension ms-python.python
code --install-extension ms-python.vscode-pylance
code --install-extension ms-azuretools.vscode-docker
code --install-extension ms-iot.vscode-ros
code --install-extension ms-vscode.cpptools
code --install-extension ms-vscode.cmake-tools
code --install-extension redhat.vscode-xml
code --install-extension redhat.vscode-yaml

Step 4: Configure Python Interpreter​

  1. Open Command Palette (Ctrl+Shift+P)
  2. Type "Python: Select Interpreter"
  3. Choose /usr/bin/python3 (system Python 3.10)

Python Setup​

Verify Python Version​

Ubuntu 22.04 comes with Python 3.10:

python3 --version
# Expected: Python 3.10.12 or newer

Install pip (Package Manager)​

# Install pip for Python 3
sudo apt install python3-pip

# Verify
pip3 --version
# Expected: pip 22.0.2 or newer

Install Development Tools​

# Install Python development headers (needed for compiling C extensions)
sudo apt install python3-dev

# Install virtualenv (for isolated environments)
pip3 install virtualenv

For non-Docker projects, use virtual environments to isolate dependencies:

# Create a virtual environment
python3 -m venv ~/ros2_venv

# Activate it
source ~/ros2_venv/bin/activate

# Your prompt will now show (ros2_venv)

# Install packages in this isolated environment
pip install numpy scipy matplotlib

# Deactivate when done
deactivate
Docker vs Virtual Environments
  • Docker: Use for ROS 2, Gazebo, Isaac Sim (system-level dependencies)
  • venv: Use for pure Python projects (data analysis, ML training)

Git Version Control​

Install Git​

# Install Git
sudo apt install git

# Verify
git --version
# Expected: git version 2.34.1 or newer

Configure Git​

# Set your name and email (used in commits)
git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

# Set default branch name to 'main'
git config --global init.defaultBranch main

# Enable colored output
git config --global color.ui auto

# Set default editor to VS Code
git config --global core.editor "code --wait"

Verify Configuration​

git config --list

Essential Command-Line Tools​

Install Build Tools​

# GCC, Make, and other compilation tools
sudo apt install build-essential

# CMake (required for building ROS 2 packages)
sudo apt install cmake

# Verify
gcc --version    # Should show gcc (Ubuntu 11.x)
make --version   # Should show GNU Make 4.3
cmake --version  # Should show cmake 3.22+

Install Network Tools​

# wget (download files from URLs)
sudo apt install wget

# curl (transfer data with URLs, used in many scripts)
sudo apt install curl

# net-tools (ifconfig, netstat)
sudo apt install net-tools

Install Productivity Tools​

# htop (system monitor)
sudo apt install htop

# tree (visualize directory structure)
sudo apt install tree

# tmux (terminal multiplexer for managing multiple sessions)
sudo apt install tmux

Docker Compose Setup​

Docker Compose lets you define multi-container applications in YAML files.

Install Docker Compose Plugin​

(Already installed if you followed Docker installation steps above)

# Verify installation
docker compose version
# Expected: Docker Compose version v2.20.0 or newer

Example: Multi-Container ROS 2 Setup​

Create a docker-compose.yml:

version: '3.8'

services:
  ros2-master:
    image: ros:humble
    container_name: ros2_master
    network_mode: host
    environment:
      - ROS_DOMAIN_ID=42
    command: ros2 run demo_nodes_cpp talker

  ros2-listener:
    image: ros:humble
    container_name: ros2_listener
    network_mode: host
    environment:
      - ROS_DOMAIN_ID=42
    command: ros2 run demo_nodes_cpp listener

Run it:

docker compose up

Expected Output:

ros2_master     | [INFO] Publishing: 'Hello World: 1'
ros2_listener   | [INFO] I heard: [Hello World: 1]

Verification Checklist​

Before proceeding to ROS 2 installation, verify:

# 1. Docker works without sudo
docker ps
# Expected: Empty list (no errors)

# 2. Docker has GPU access
docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
# Expected: GPU info displayed

# 3. VS Code installed
code --version
# Expected: Version number

# 4. Python 3.10+ available
python3 --version
# Expected: Python 3.10.x

# 5. Git configured
git config user.name
# Expected: Your name

# 6. Build tools available
gcc --version && cmake --version
# Expected: Version numbers for both
Checkpoint Passed

If all commands above work, you're ready to install ROS 2 Humble! 🎉

Common Issues and Solutions​

Issue: Docker Permission Denied​

Symptom:

docker: Got permission denied while trying to connect to the Docker daemon socket

Solution:

# Add yourself to docker group
sudo usermod -aG docker $USER

# Log out and log back in, or run:
newgrp docker

Issue: NVIDIA-SMI Not Found in Docker​

Symptom:

docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
# Error: nvidia-smi: command not found

Solution:

# Reinstall NVIDIA Container Toolkit
sudo apt purge nvidia-container-toolkit
sudo apt install nvidia-container-toolkit

# Restart Docker
sudo systemctl restart docker

Issue: VS Code Extensions Not Working​

Symptom: Python extension doesn't provide autocomplete or linting.

Solution:

# Ensure Python extension is installed
code --install-extension ms-python.python

# Ensure Pylance is installed
code --install-extension ms-python.vscode-pylance

# Reload VS Code window
# Ctrl+Shift+P → "Developer: Reload Window"

Issue: Git Push Requires Username/Password Every Time​

Symptom: GitHub asks for credentials on every push.

Solution (Use SSH Keys):

# Generate SSH key
ssh-keygen -t ed25519 -C "your.email@example.com"

# Press Enter to accept default location
# Set a passphrase (or press Enter for none)

# Copy public key to clipboard
cat ~/.ssh/id_ed25519.pub

# Add this key to GitHub:
# 1. Go to github.com → Settings → SSH and GPG keys
# 2. Click "New SSH key"
# 3. Paste your public key
# 4. Save

# Test connection
ssh -T git@github.com
# Expected: "Hi [username]! You've successfully authenticated..."

Development Workflow Example​

Here's a typical development session:

# 1. Start Docker containers
cd ~/physical_ai_project
docker compose up -d

# 2. Open VS Code
code .

# 3. Attach VS Code to running container
# (Use "Dev Containers" extension)

# 4. Write code, test in container

# 5. Commit changes
git add .
git commit -m "Implemented robot navigation"
git push

# 6. Stop containers when done
docker compose down

Next Steps​

With your development environment configured, you're ready to:

  1. Install ROS 2 Humble (Module 1, Chapter 1)
  2. Set up Gazebo Simulator (Module 2, Chapter 1)
  3. Install Isaac Sim (Module 3, Chapter 1)

The foundation is laid. Now it's time to build robots! 🤖

Next Module: ROS 2 Fundamentals →

Keyboard Shortcuts for Productivity

VS Code:

  • Ctrl+Shift+P – Command Palette
  • `Ctrl+`` – Toggle Terminal
  • Ctrl+B – Toggle Sidebar
  • Ctrl+Shift+F – Search in all files

Terminal (Bash):

  • Ctrl+R – Search command history
  • Ctrl+A – Jump to beginning of line
  • Ctrl+E – Jump to end of line
  • Ctrl+L – Clear screen
Resource Management

Running multiple Docker containers + Isaac Sim can consume significant resources. Use htop to monitor CPU/RAM/GPU usage. If system slows down, close unnecessary applications or reduce simulation complexity.

Additional Resources​

Official Documentation​

Tutorials​

Community​

Environment Setup Complete! ✅

You now have a fully configured Ubuntu 22.04 workstation ready for Physical AI development. In the next module, we'll dive into ROS 2 and start controlling robots.