Federated Learning with PyTorch and Flower (Quickstart Example)ΒΆ
This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses Flower Datasets to download, partition and preprocess the CIFAR-10 dataset.
Set up the projectΒΆ
Clone the projectΒΆ
Start by cloning the example project:
git clone --depth=1 https://github.com/adap/flower.git _tmp \
&& mv _tmp/examples/quickstart-pytorch . \
&& rm -rf _tmp \
&& cd quickstart-pytorch
This will create a new directory called quickstart-pytorch
with the following structure:
quickstart-pytorch
βββ pytorchexample
β βββ __init__.py
β βββ client_app.py # Defines your ClientApp
β βββ server_app.py # Defines your ServerApp
β βββ task.py # Defines your model, training and data loading
βββ pyproject.toml # Project metadata like dependencies and configs
βββ README.md
Install dependencies and projectΒΆ
Install the dependencies defined in pyproject.toml
as well as the pytorchexample
package.
pip install -e .
Run the projectΒΆ
You can run your Flower project 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 EngineΒΆ
flwr run .
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=5 learning-rate=0.05"
[!TIP] For a more detailed walk-through check our quickstart PyTorch tutorial
Run with the Deployment EngineΒΆ
[!NOTE] An update to this example will show how to run this Flower application with the Deployment Engine and TLS certificates, or with Docker.