Introduction

What is this repository?

This is a template docker-based dev environment. It currently supports NVIDIA GPUs but with slight modifications it can target for x86 CPUs and Apple silicon chips.

It currently includes the following tools:

• a assignments directory with an empty notebook where you need to populate with your code. The notebook can optionally use the artagents library.
• a project directory for your project source code. The documentation for the project is stored separately in the docs directory.
• a docs directory that contains the source code of quarto markdown (qmd) and ipynb notebooks content. You use the docs folder to publish your project work.

How to Launch the Development Container in VS Code

This repository includes a VS Code development container configuration that can be launched with either CPU or GPU support.

Prerequisites

1. Install VS Code with the "Dev Containers" extension
2. Install Docker and ensure it's running
3. For GPU support: Install NVIDIA Container Toolkit (for Linux) or Docker Desktop with GPU support

After the container is launched you can install femtotransformer, run the following command:

make start

Dont forget to source the environment after the make command:

source .venv/bin/activate

Port Customization

You can customize the exposed ports by modifying the .env file. Each service has its own set of port variables:

Dev Service Ports:

• DEV_QUARTO_PORT: Quarto preview server (default: 4199)
• DEV_JUPYTER_PORT: Jupyter notebook server (default: 8890)
• DEV_PORT: Additional development server (default: 8088)

ROS Service Ports:

• ROS_PORT: ROS master port (default: 11311)
• ROS_QUARTO_PORT: Quarto preview server (default: 4180)
• ROS_JUPYTER_PORT: Jupyter notebook server (default: 8880)
• ROS_DEV_PORT: Additional development server (default: 8078)

Note: The actual ports exposed will be the values from your .env file.

Service Selection

The repository supports multiple container configurations:

• dev: Standard PyTorch development environment
• ros: ROS development environment with GPU support

Using Shell Script (Recommended)

Switch between services using the provided shell script:

# Configure for PyTorch development
./devcontainer.sh dev

# Configure for ROS development  
./devcontainer.sh ros

The script will:

1. Update the .devcontainer/devcontainer.json service configuration
2. Update environment files (.env and .devcontainer/devcontainer.env)
3. Optionally launch VS Code for you

Manual Configuration

To manually switch services, modify the service field in .devcontainer/devcontainer.json:

{
  "service": "torch.dev.gpu"  // or "ros.dev.gpu"
}

After changing the service configuration, close and reopen VS Code, then select "Reopen in Container" when prompted.

What should I do with it?

• Follow all instructions under resources in the class website as you will need it to submit your work.
• Familiarize yourself with the uv package manager as you will use it to build and manage all your dependencies.
• Follow the instructions in the course web site under resources to submit your github repo to the course's LLM system (Canvas/Brightspace).

Additional Notes for ROS Development

You can connect to the foxglove app using the following command:

ros2 launch foxglove_bridge foxglove_bridge_launch.xml