---
tags: [quickstart, vision, fds]
dataset: [CIFAR-10]
framework: [torch, torchvision]
---

# Federated Learning with PyTorch and Flower (Quickstart Example)

[<img src="_static/view-gh.png" alt="View on GitHub" width="200"/>](https://github.com/adap/flower/blob/main/examples/quickstart-pytorch)

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](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.

## Set up the project

### Clone the project

Start by cloning the example project:

```shell
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:

```shell
quickstart-pytorch
├── pytorchexample
│   ├── __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
└── README.md
```

### Install dependencies and project

Install the dependencies defined in `pyproject.toml` as well as the `pytorchexample` package.

```bash
pip install -e .
```

## Run the project

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 Engine

> [!TIP]
> This example might run faster when the `ClientApp`s have access to a GPU. If your system has one, you can make use of it by configuring the `backend.client-resources` component in `pyproject.toml`. If you want to try running the example with GPU right away, use the `local-simulation-gpu` federation as shown below. Check the [Simulation Engine documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) to learn more.

```bash
# Run with the default federation (CPU only)
flwr run .
```

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

```bash
flwr run . --run-config "num-server-rounds=5 learning-rate=0.05"
```

Run the project in the `local-simulation-gpu` federation that gives CPU and GPU resources to each `ClientApp`. By default, at most 5x`ClientApp` will run in parallel in the available GPU. You can tweak the degree of parallelism by adjusting the settings of this federation in the `pyproject.toml`.

```bash
# Run with the `local-simulation-gpu` federation
flwr run . local-simulation-gpu
```

> [!TIP]
> For a more detailed walk-through check our [quickstart PyTorch tutorial](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html)

### Run with the Deployment Engine

Follow this [how-to guide](https://flower.ai/docs/framework/how-to-run-flower-with-deployment-engine.html) to run the same app in this example but with Flower's Deployment Engine. After that, you might be intersted in setting up [secure TLS-enabled communications](https://flower.ai/docs/framework/how-to-enable-tls-connections.html) and [SuperNode authentication](https://flower.ai/docs/framework/how-to-authenticate-supernodes.html) in your federation.

If you are already familiar with how the Deployment Engine works, you may want to learn how to run it using Docker. Check out the [Flower with Docker](https://flower.ai/docs/framework/docker/index.html) documentation.