correct readme

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

@@ -1,13 +1,15 @@
-FEDn Project: MNIST (PyTorch)
------------------------------
+FEDn Project: MonAI 2D Classification with the MedNIST Dataset (PyTorch)
+------------------------------------------------------------------------
 
-This is an example FEDn Project based on the classic hand-written text recognition dataset MNIST.
+This is an example FEDn Project based on the  MonAI 2D Classification with the MedNIST Dataset.
 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.
 
-   **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**
+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
 
 Prerequisites
 -------------
@@ -22,6 +24,7 @@ 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
 -------------------------------------------
 
@@ -36,7 +39,7 @@ Clone this repository, then locate into this directory:
 .. code-block::
 
    git clone https://github.com/scaleoutsystems/fedn.git
-   cd fedn/examples/mnist-pytorch
+   cd fedn/examples/monai-2D-mednist
 
 Create the compute package:
 
@@ -54,29 +57,43 @@ Next, generate a seed model (the first model in a global model trail):
 
 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 -r requirements.txt
+
+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
+
+
+
 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.
 
+Connecting clients:
+===================
 
-Modifing the data split:
-========================
-
-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:
+**NOTE: In case a different data path needs to be set, use the env variable FEDN_DATA_PATH.**
 
 .. code-block::
 
    export FEDN_PACKAGE_EXTRACT_DIR=package
-   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.
+   export FEDN_DATA_PATH=<full_path_to_the_dir>/data/
+   export FEDN_CLIENT_SETTINGS_PATH=<full_path_to_the_dir>/client_settings.yaml
+   export export FEDN_DATA_SPLIT_INDEX=0
 
+   fedn client start -in client.yaml --secure=True --force-ssl
 
 Connecting clients using Docker:
 ================================
@@ -87,12 +104,16 @@ For convenience, there is a Docker image hosted on ghrc.io with fedn preinstalle
 
    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_NUM_DATA_SPLITS=2 \
-     -e FEDN_DATA_PATH=/app/package/data/clients/1/mnist.pt \
+     -e FEDN_DATA_PATH=./data/ \
+     -e FEDN_CLIENT_SETTINGS_PATH=/app/client_settings.yaml \
      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
 --------------------------------------------------
 
@@ -134,7 +155,7 @@ 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/data/clients/3/mnist.pt``
+by copying ``client1`` and setting ``FEDN_DATA_PATH`` to ``/app/package/data3/``
 
 
 Access message logs and validation data from MongoDB