slim down

.vscode/settings.json

@@ -1,7 +1,7 @@
 {
     "editor.formatOnSave": true,
     "editor.codeActionsOnSave": {
-        "source.organizeImports": true
+        "source.organizeImports": "explicit"
     },
     "python.linting.enabled": true,
     "python.linting.flake8Enabled": true,

docs/distributed.rst

@@ -2,8 +2,8 @@ Distributed Deployment
 ===================================
 
 This tutorial outlines the steps for deploying the FEDn framework on a local network, using a workstation as 
-the host and Raspberry Pi 5 devices as clients. We'll demonstrate this setup using the PyTorch MNIST example model 
-and compute package.
+the host and different devices as clients. For general steps on how to run FEDn, see one of the quickstart tutorials. 
+
 
 .. note::
    For deployment over a public network, ensure you have:
@@ -25,97 +25,16 @@ Launch a distributed FEDn Network
 -------------
 
 
-Note the local IP address of your host device; it will be needed later. Clone the FEDn repository and navigate to the cloned directory.
-Start the FEDn network using Docker Compose:
-
-.. code-block::
-
-   docker-compose up 
-
-Install the FEDn SDK
--------------
-
-.. note::
-    To programmatically interact with the FEDn network use the APIClient.
-    Install the FEDn via pip:
-
-    .. code-block:: bash
-       
-       $ pip install fedn
-       # or from source
-       $ cd fedn/fedn
-       $ pip install . 
-
-
-Prepare the compute package and seed for the FEDn network
--------------
-
-From your host device, prepare the compute package and model seed. In this tutorial we will use the 
-``examples/mnist-pytorch`` example. 
-
-Locate into ``examples/mnist-pytorch`` folder and initialize a virtual environment.
-
-.. code-block:: python
-
-   bin/init_venv.sh
-
-Now create the compute package and an initial model:
-
-.. code-block::
-
-   bin/build.sh
-
-
-Upload the compute package and seed model to FEDn:
-
-.. code:: python
-
-   >>> from fedn import APIClient
-   >>> client = APIClient(host="localhost", port=8092)
-   >>> client.set_package("package.tgz", helper="numpyhelper")
-   >>> client.set_initial_model("seed.npz")
-
-Building a Custom Image for Client Devices (Optional)
--------------
+Note the local IP address of your host device; it will be needed later. Follow the standard procedure 
+to initiate a FEDn network. Once the network is active, upload your compute package and seed (for comprehensive details, 
+see the quickstart tutorials).
 
-The prebuilt Docker images may not be compatible with Raspberry Pi due to system architecture differences. To 
-build a custom Docker image:
-
-#. **Clone the Repository on Your Client Device**: Ensure you're in the root directory of the repository.
-
-#. **Build Your Custom Image**: Substitute <custom image name> with your desired image name:
-
-.. code-block::
-
-   docker build —build-arg REQUIREMENTS=examples/mnist-pytorch/requirements.txt -t <custom image name> .
 
 Configuring and Attaching Clients
 -------------
 
-The next step is to configure and attach clients. Begin with navigating to ``examples/mnist-pytorch`` and 
-execute the initializion script:
-
-.. code-block:: python
-
-   bin/init_venv.sh
-
-Download the data:
-
-.. code-block::
-
-   bin/get_data
-
-Split the data in 2 parts for the clients:
-
-.. code-block::
-
-   bin/split_data
-
-Data partitions will be generated in the folder 'data/clients'.  
-
-
-FEDn relies on a configuration file for the client to connect to the server. Create a file called 'client.yaml' with 
-the follwing content:
+On your client device, continue with initializing your client. To connect to the host machine we need to ensure we are 
+routing the correct DNS to our hosts local IP adress. We can do this using the standard FEDn `client.yaml`:
 
 .. code-block::
 
@@ -124,35 +43,25 @@ the follwing content:
    discover_port: 8092
 
 
-Make sure to move the file ``client.yaml`` to the root of the examples/mnist-pytorch folder and connect the client to 
-the network. Note that we are routing `api-server` and `combiner` to our hosts local ip adress:
+We can then run using docker by adding the hosts to the docker run command:
 
 .. code-block::
 
    docker run \
-   -v $PWD/client.yaml:/app/client.yaml \
-   -v $PWD/data/clients/1:/var/data \
-   -e ENTRYPOINT_OPTS=--data_path=/var/data/mnist.pt \
    —add-host=api-server:<host local ip> \
    —add-host=combiner:<host local ip> \
-   <custom image name> run client -in client.yaml --name client1
-
+   <image name> run client -in client.yaml --name client1
 
-Start a training session
--------------
 
-On the host device, observe the API Server logs and combiner logs, you should see the client connecting.
-You are now ready to start training the model. In the python enviroment you installed FEDn:
+Alternatively updating the `/etc/hosts` file, appending the following lines for running naitively:
 
-.. code:: python
+.. code-block::
 
-   >>> ...
-   >>> client.start_session(session_id="test-session", rounds=3)
+   <host local ip>      api-server
+   <host local ip>      combiner
 
-Clean up
---------
-You can clean up by running 
 
-.. code-block::
+Start a training session
+-------------
 
-   docker-compose down
+After connecting with your clients, you are ready to start training sessions from the host machine.