---
tags: [mods, monitoring]
dataset: [CIFAR-10]
framework: [wandb, tensorboard]
---
# Using custom mods ๐งช
[
](https://github.com/adap/flower/blob/main/examples/custom-mods)
> ๐งช = This example covers experimental features that might change in future versions of Flower
> Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch.
The following steps describe how to write custom Flower Mods and use them in a simple example.
## Writing custom Flower Mods
As described [in the documentation](https://flower.ai/docs/framework/how-to-use-built-in-mods.html#what-are-mods), Flower Mods in their simplest form can be described as:
```python
def basic_mod(msg: Message, context: Context, app: ClientApp) -> Message:
# Do something with incoming Message (or Context)
# before passing to the inner ``ClientApp``
reply = app(msg, context)
# Do something with outgoing Message (or Context)
# before returning
return reply
```
and used when defining the `ClientApp` as:
```python
app = ClientApp(
client_fn=client_fn,
mods=[basic_mod],
)
```
The mods in this example do not modify the `Message` object that the `ClientApp` is communicating to the `ServerApp`. Instead, the mods only log the _metrics_ returned by the client's `fit()` method to Weight & Biases or into TensorBoard .
## 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/custom-mods . \
&& rm -rf _tmp \
&& cd custom-mods
```
This will create a new directory called `custom-mods` with the following structure:
```shell
custom-mods
โโโ README.md
โโโ custom_mods
โ โโโ __init__.py
โ โโโ client_app.py # Defines your ClientApp
โ โโโ mods.py # Defines a Weights & Biases and TensorBoard mod
โ โโโ server_app.py # Defines your ServerApp
โ โโโ task.py # Defines your model, training and data loading
โโโ pyproject.toml # Project metadata like dependencies and configs
```
### Install dependencies and project
Install the dependencies defined in `pyproject.toml` as well as the `custom_mods` package.
```bash
pip install -e .
```
## Run the project
> \[!TIP\]
> By default the `ClientApp` uses the TensorBoard mod, if you would like to enable the Weight & Biases mod, please edit the line at the bottom of `custom_mods/client_app.py` and pass the `get_wandb_mod` mod to the constructor of your `ClientApp`.
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
> \[!NOTE\]
> Check the [Simulation Engine documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) to learn more about Flower simulations and how to optimize them.
```bash
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"
```
### Check the results
For W&B, you will need to login to the [website](https://wandb.ai).
For TensorBoard, you will need to run the following command in your terminal:
```bash
tensorboard --logdir
```
Where `` needs to be replaced by the directory passed as an argument to the wrapper function (`.runs_history/` by default).
### 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.