Flower AI Summit 2026·April 15–16·London

@johannes/forest-monitoring-example

0
0
flwr new @johannes/forest-monitoring-example

forest-monitoring-example: A Flower / PyTorch app

This Flower example uses PyTorch for a regression task, modelling forest timber volme using satellite time series data. The example is easy to run and helps illustrate how Flower can be adapted to your own use case. It uses a dummy demo datasets for example runs using the simulation engine and deployment mode.

Fetch the App

Install Flower:

pip install flwr

This will create a new directory called forest-monitoring-example with the following structure:

forest-monitoring-example
├── data  # the data is not included in the FlowerHub repositroy but can be downloaded from https://zenodo.org/records/18929115; see below for further info
│   ├── deployment
│   │   ├── client_1_demo_data.npz
│   │   └── client_2_demo_data.npz
│   ├── simulation
│   │   └── demo_data.npz
├── forest_monitoring_example
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
├── .gitignore
└── README.md

Install dependencies and project

The dependencies are listed in the pyproject.toml and you can install them as follows:

pip install -e .

Tip: Your pyproject.toml file can define more than just the dependencies of your Flower app. You can also use it to specify hyperparameters for your runs and control which Flower Runtime is used. By default, it uses the Simulation Runtime, but you can switch to the Deployment Runtime when needed. Learn more in the TOML configuration guide.

Demo data sets

Demo data sets for simulation and deployment mode can be downloaded from the Zenodo repository: https://zenodo.org/records/18929115 Save the demo_data.npz in the folder: /forest-monitoring-example/data/simulation/ for running the app with the simulation engine.

Run the App

Once you downloaded the data from Zenodo and saved them, you can run your Flower App in both simulation and deployment mode without making changes to the code. If you are starting with Flower, we recommend you using the simulation mode as it requires fewer components to be launched manually. By default, flwr run will make use of the Simulation Engine.

Run with the Simulation Engine

In the forest-monitoring-example directory, use flwr run to run a local simulation:

flwr run --stream

You can also override some of the settings for your ClientApp and ServerApp defined in pyproject.toml. For example:

flwr run . --run-config "num-server-rounds=3"

Refer to the How to Run Simulations guide in the documentation for advice on how to optimize your simulations.

Run with the Deployment Engine

Follow this how-to guide to run the same app in this example but with Flower's Deployment Engine. After that, you might be interested in setting up secure TLS-enabled communications and SuperNode authentication in your federation.

flower-supernode \
    --insecure \
    --superlink <SUPERLINK-FLEET-API> \
    --node-config=’client-id=”CID_1” data-path=”/path/to/demo_data/client_1_demo_data.npz”’

Launch the run via flwr run but pointing to a SuperLink connection that specifies the SuperLink your SuperNode is connected to:

flwr run . <SUPERLINK-CONNECTION> --stream

You can run Flower on Docker too! Check out the Flower with Docker documentation.

Resources