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FrankJonasmoelle committed Aug 20, 2024
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# ASYNC CLIENTS
ASYNC CLIENTS
-------------

This example shows how to experiment with intermittent and asynchronous client workflows.

## Prerequisites
Prerequisites
-------------

- [Python 3.8, 3.9 or 3.10](https://www.python.org/downloads)
- [Docker](https://docs.docker.com/get-docker)
- [Docker Compose](https://docs.docker.com/compose/install)

## Running the example (pseudo-distributed, single host)
Running the example (pseudo-distributed, single host)
-----------------------------------------------------


First, make sure that FEDn is installed (we recommend using a virtual environment)

Clone FEDn
```sh

.. code-block::
git clone https://github.com/scaleoutsystems/fedn.git
```

Install FEDn and dependencies

``
.. code-block::
pip install fedn
```
Or from source, standing in the folder 'fedn/fedn'

```
pip install .
```

### Prepare the example environment, the compute package and seed model
Prepare the example environment, the compute package and seed model
-------------------------------------------------------------------

Standing in the folder fedn/examples/async-clients
```
pip install -r requirements.txt
```

.. code-block::
pip install -r requirements.txt
Create the compute package and seed model:
```
fedn package create --path client
```

```
fedn run build --path client
```
.. code-block::
fedn package create --path client
.. code-block::
fedn run build --path client
You will now have a file 'seed.npz' in the directory.

### Running a simulation
Running a simulation
--------------------

Deploy FEDn on localhost. Standing in the the FEDn root directory:

```
docker-compose up
```
.. code-block::
docker-compose up
Initialize FEDn with the compute package and seed model

```
python init_fedn.py
```
.. code-block::
python init_fedn.py
Start simulating clients
```
python run_clients.py
```

.. code-block::
python run_clients.py
Start the experiment / training sessions:

```
python run_experiment.py
```
.. code-block::
python run_experiment.py
Once global models start being produced, you can start analyzing results using API Client, refer to the notebook "Experiment.ipynb" for instructions.



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