add requirements

examples/monai-2D-mednist/README.rst

@@ -1,34 +1,31 @@
-FEDn Project: MonAI 2D Classification with the MedNIST Dataset (PyTorch)
-------------------------------------------------------------------------
+FEDn Project: MNIST (PyTorch)
+-----------------------------
 
-This is an example FEDn Project based on the  MonAI 2D Classification with the MedNIST Dataset. 
+This is an example FEDn Project based on the classic hand-written text recognition dataset MNIST.
 The example is intented as a minimalistic quickstart and automates the handling of training data
-by letting the client download and create its partition of the dataset as it starts up. 
+by letting the client download and create its partition of the dataset as it starts up.
 
-Links: 
-
--  MonAI: https://monai.io/
--  Base example notebook: https://github.com/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb
--  MedNIST dataset: https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/MedNIST.tar.gz 
+   **Note: These instructions are geared towards users seeking to learn how to work
+   with FEDn in local development mode using Docker/docker-compose. We recommend all new users
+   to start by following the Quickstart Tutorial: https://fedn.readthedocs.io/en/stable/quickstart.html**
 
 Prerequisites
 -------------
 
 Using FEDn Studio:
 
 -  `Python 3.8, 3.9, 3.10 or 3.11 <https://www.python.org/downloads>`__
--  `A FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__   
+-  `A FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__
 
 If using pseudo-distributed mode with docker-compose:
 
 -  `Docker <https://docs.docker.com/get-docker>`__
 -  `Docker Compose <https://docs.docker.com/compose/install>`__
 
-
 Creating the compute package and seed model
 -------------------------------------------
 
-Install fedn: 
+Install fedn:
 
 .. code-block::
 
@@ -39,7 +36,7 @@ Clone this repository, then locate into this directory:
 .. code-block::
 
    git clone https://github.com/scaleoutsystems/fedn.git
-   cd fedn/examples/monai-2D-mednist
+   cd fedn/examples/mnist-pytorch
 
 Create the compute package:
 
@@ -55,63 +52,47 @@ Next, generate a seed model (the first model in a global model trail):
 
    fedn run build --path client
 
-This will create a seed model called 'seed.npz' in the root of the project. This step will take a few minutes, depending on hardware and internet connection (builds a virtualenv).  
-
-Download and Prepare the data
--------------------------------------------
-
-Install monai
-
-.. code-block::
-
-   pip install monai
-
-Download and divide the data into parts. Set the number of
-data parts as an arguments python prepare_data.py NR-OF-DATAPARTS. In the
-below command we divide the dataset into 10 parts.
-.. code-block::
-
-    python prepare_data.py 10
-
-
+This will create a seed model called 'seed.npz' in the root of the project. This step will take a few minutes, depending on hardware and internet connection (builds a virtualenv).
 
 Using FEDn Studio
 -----------------
 
 Follow the guide here to set up your FEDn Studio project and learn how to connect clients (using token authentication): `Studio guide <https://fedn.readthedocs.io/en/stable/studio.html>`__.
-On the step "Upload Files", upload 'package.tgz' and 'seed.npz' created above. 
+On the step "Upload Files", upload 'package.tgz' and 'seed.npz' created above.
+
 
-Connecting clients:
-===================
+Modifing the data split:
+========================
 
-**NOTE: In case a different data path needs to be set, use the env variable FEDN_DATA_PATH.**
+The default traning and test data  for this example is downloaded and split direcly by the client when it starts up (see 'startup' entrypoint).
+The number of splits and which split used by a client can be controlled via the environment variables ``FEDN_NUM_DATA_SPLITS`` and ``FEDN_DATA_PATH``.
+For example, to split the data in 10 parts and start a client using the 8th partiton:
 
 .. code-block::
 
    export FEDN_PACKAGE_EXTRACT_DIR=package
-   export FEDN_DATA_PATH=./data/
-   export FEDN_CLIENT_SETTINGS_PATH=<full_path_to_the_dir>/client_settings.yaml
+   export FEDN_NUM_DATA_SPLITS=10
+   export FEDN_DATA_PATH=./data/clients/8/mnist.pt
    fedn client start -in client.yaml --secure=True --force-ssl
 
+The default is to split the data into 2 partitions and use the first partition.
+
+
 Connecting clients using Docker:
 ================================
 
-For convenience, there is a Docker image hosted on ghrc.io with fedn preinstalled. To start a client using Docker: 
+For convenience, there is a Docker image hosted on ghrc.io with fedn preinstalled. To start a client using Docker:
 
 .. code-block::
 
    docker run \
      -v $PWD/client.yaml:/app/client.yaml \
-     -v $PWD/client_settings.yaml:/app/client_settings.yaml \
      -e FEDN_PACKAGE_EXTRACT_DIR=package \
-     -e FEDN_DATA_PATH=./data/ \
-     -e FEDN_CLIENT_SETTINGS_PATH=/app/client_settings.yaml \
+     -e FEDN_NUM_DATA_SPLITS=2 \
+     -e FEDN_DATA_PATH=/app/package/data/clients/1/mnist.pt \
      ghcr.io/scaleoutsystems/fedn/fedn:0.9.0 run client -in client.yaml --force-ssl --secure=True
 
 
-**NOTE: The following instructions are only for SDK-based client communication and for local development environments using Docker.**
-
-
 Local development mode using Docker/docker compose
 --------------------------------------------------
 
@@ -126,8 +107,8 @@ Start a pseudo-distributed FEDn network using docker-compose:
     -f docker-compose.override.yaml \
     up
 
-This starts up local services for MongoDB, Minio, the API Server, one Combiner and two clients. 
-You can verify the deployment using these urls: 
+This starts up local services for MongoDB, Minio, the API Server, one Combiner and two clients.
+You can verify the deployment using these urls:
 
 - API Server: http://localhost:8092/get_controller_status
 - Minio: http://localhost:9000
@@ -142,18 +123,18 @@ Upload the package and seed model to FEDn controller using the APIClient. In Pyt
    client.set_active_package("package.tgz", helper="numpyhelper")
    client.set_active_model("seed.npz")
 
-You can now start a training session with 5 rounds (default): 
+You can now start a training session with 5 rounds (default):
 
 .. code-block::
 
    client.start_session()
 
-Automate experimentation with several clients  
+Automate experimentation with several clients
 =============================================
 
-If you want to scale the number of clients, you can do so by modifying ``docker-compose.override.yaml``. For example, 
-in order to run with 3 clients, change the environment variable ``FEDN_NUM_DATA_SPLITS`` to 3, and add one more client 
-by copying ``client1`` and setting ``FEDN_DATA_PATH`` to ``/app/package/data3/``
+If you want to scale the number of clients, you can do so by modifying ``docker-compose.override.yaml``. For example,
+in order to run with 3 clients, change the environment variable ``FEDN_NUM_DATA_SPLITS`` to 3, and add one more client
+by copying ``client1`` and setting ``FEDN_DATA_PATH`` to ``/app/package/data/clients/3/mnist.pt``
 
 
 Access message logs and validation data from MongoDB  

examples/monai-2D-mednist/requirements.txt

@@ -0,0 +1,3 @@
+monai
+PyYAML
+numpy==1.26.4