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PumaGuard

Build and Test Webpage

Test and package code

Open in GitHub Codespaces

Open in Colab

Introduction

Please visit http://pumaguard.rtfd.io/ for more information.

Get PumaGuard

PyPI - Version

GitHub Codespaces

If you do not want to install any new software on your computer you can use GitHub Codespaces, which provide a development environment in your browser.

Open in GitHub Codespaces

Local Development Environment

You can set up a local development environment using either uv (recommended for speed) or poetry.

uv is an extremely fast Python package installer and resolver.

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Or on Windows:

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Create a virtual environment and install dependencies:

uv venv
source .venv/bin/activate  # On Linux/macOS
# or
.venv\Scripts\activate  # On Windows

# Install with development dependencies
uv pip install -e ".[dev,extra-dev]"

Or use uv sync for automatic environment management:

uv sync --extra dev --extra extra-dev

Using Poetry

Alternatively, you can use poetry:

sudo apt install python3-poetry
poetry install

Running the scripts on colab.research.google.com

Google Colab offers runtimes with GPUs and TPUs, which make training a model much faster. In order to run the training script in Google Colab, do the following from the terminal:

git clone https://github.com/PEEC-Nature-Youth-Group/pumaguard.git
cd pumaguard
scripts/train.py --help

For example, if you want to train the model from row 1 in the notebook,

scripts/train.py --notebook 1

Web UI

PumaGuard includes a modern Flutter-based web interface for monitoring and configuration.

Starting the Web UI

Using uv:

uv run pumaguard-webui --host 0.0.0.0 --port 5000

Using poetry:

poetry run pumaguard-webui --host 0.0.0.0 --port 5000

The web interface will be accessible at http://your-server-ip:5000 or http://pumaguard.local:5000 (if mDNS is enabled).

mDNS/Zeroconf Support

PumaGuard supports automatic server discovery via mDNS (also known as Bonjour or Zeroconf). This allows clients to connect using a friendly hostname like pumaguard.local instead of needing to know the IP address.

Setup mDNS on the server:

  • Linux: Install Avahi

    sudo apt install avahi-daemon avahi-utils
    sudo systemctl enable avahi-daemon
    sudo systemctl start avahi-daemon
    
  • macOS: Built-in, no setup needed

  • Windows: Install Bonjour Print Services

Using mDNS:

Once mDNS is set up, your server will be automatically discoverable at:

http://pumaguard.local:5000

You can customize the hostname:

pumaguard-webui --mdns-name my-server
# Accessible at: http://my-server.local:5000

Or disable mDNS:

pumaguard-webui --no-mdns

For detailed mDNS setup instructions including Docker/container configurations, see docs/MDNS_SETUP.md.

Running the server

The pumaguard-server watches a folder and classifies new files as they are added to that folder. Run with

Using uv:

uv run pumaguard-server FOLDER

Using poetry:

poetry run pumaguard-server FOLDER

Where FOLDER is the folder to watch.

Server Demo Session

Training new models

For reproducibility, training new models should be done via the train script and all necessary data, i.e. images, and the resulting weights and history should be committed to the repository.

  1. Get a TPU instance on Colab or run the script on your local machine.

  2. Open a terminal and run

    git clone https://github.com/PEEC-Nature-Youth-Group/pumaguard.git
    cd pumaguard
    
  3. Get help on how to use the script

    On Colab, run

    ./scripts/pumaguard --help
    ./scripts/pumaguard train --help
    

    On your local machine with uv:

    sudo apt install nvidia-cudnn
    uv sync --extra dev --extra extra-dev
    uv run pumaguard --help
    uv run pumaguard train --help
    

    Or with poetry:

    sudo apt install nvidia-cudnn
    poetry install
    poetry run pumaguard --help
    poetry run pumaguard train --help
    
  4. Train the model from scratch

    ./scripts/pumaguard train --no-load --settings pumaguard-models/model_settings_6_pre-trained_512_512.yaml
    

Metadata

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