Feature/SK-707 | Flower client example

.ci/tests/examples/run.sh

@@ -12,7 +12,7 @@ helper="$2"
 >&2 echo "Start FEDn"
 pushd "examples/$example"
 
-docker-compose \
+docker compose \
     -f ../../docker-compose.yaml \
     -f docker-compose.override.yaml \
     up -d --build
@@ -43,7 +43,7 @@ docker-compose \
 ".$example/bin/python" ../../.ci/tests/examples/api_test.py get_client_config --output ../../client.yaml
 
 # Redeploy clients with config
-docker-compose \
+docker compose \
     -f ../../docker-compose.yaml \
     -f docker-compose.override.yaml \
     -f ../../.ci/tests/examples/compose-client-settings.override.yaml \
@@ -56,4 +56,4 @@ docker-compose \
 ".$example/bin/python" ../../.ci/tests/examples/api_test.py test_api_get_methods
 
 popd
->&2 echo "Test completed successfully"
+>&2 echo "Test completed successfully"

.github/workflows/code-checks.yaml

@@ -43,6 +43,7 @@ jobs:
           --exclude-dir='.mnist-pytorch' 
           --exclude-dir='.mnist-keras' 
           --exclude-dir='docs'
+          --exclude-dir='flower-client'
           --exclude='tests.py'
           '^[ \t]+(import|from) ' -I . 
 

.vscode/settings.json

@@ -5,4 +5,5 @@
     },
     "python.linting.enabled": true,
     "python.linting.flake8Enabled": true,
+    "esbonio.sphinx.confDir": "",
 }

examples/flower-client/.dockerignore

@@ -0,0 +1,4 @@
+data
+seed.npz
+*.tgz
+*.tar.gz

examples/flower-client/.gitignore

@@ -0,0 +1,6 @@
+data
+*.npz
+*.tgz
+*.tar.gz
+.flower-client
+client.yaml

examples/flower-client/Dockerfile

@@ -0,0 +1,10 @@
+FROM ghcr.io/scaleoutsystems/fedn/fedn:0.8.0
+
+COPY requirements.txt /app/config/requirements.txt
+
+# Install requirements
+RUN python -m venv /venv \
+    && /venv/bin/pip install --upgrade pip \
+    && /venv/bin/pip install --no-cache-dir -r /app/config/requirements.txt \
+    # Clean up
+    && rm -r /app/config/requirements.txt

examples/flower-client/README.rst

@@ -0,0 +1,108 @@
+Using Flower ClientApps in FEDn
+============================
+
+This example demonstrates how to run a Flower 'ClientApp' on FEDn.
+
+The FEDn compute package 'client/entrypoint' 
+uses a built-in Flower compatibiltiy adapter for convenient wrapping of the Flower client.
+See `flwr_client.py` and `flwr_task.py` for the Flower client code (which is adapted from 
+https://github.com/adap/flower/tree/main/examples/app-pytorch). 
+
+
+Running the example
+-------------------
+
+See `https://fedn.readthedocs.io/en/stable/quickstart.html` for a general introduction to FEDn. 
+This example follows the same structure as the pytorch quickstart example. 
+
+Build a virtual environment (note that you might need to install the 'venv' package): 
+
+.. code-block::
+
+   bin/init_venv.sh
+
+Activate the virtual environment:
+
+.. code-block::
+
+   source .flower-client/bin/activate
+
+Make the compute package (to be uploaded to FEDn):
+
+.. code-block::
+
+   tar -czvf package.tgz client
+
+Create the seed model (to be uploaded to FEDn):
+.. code-block::
+
+   python client/entrypoint init_seed
+
+Next, you will upload the compute package and seed model to
+a FEDn network. Here you have two main options: using FEDn Studio 
+(recommended for new users), or a pseudo-local deployment
+on your own machine. 
+
+If you are using FEDn Studio (recommended):
+-----------------------------------------------------
+
+Follow instructions here to register for Studio and start a project: https://fedn.readthedocs.io/en/stable/studio.html.
+
+In your Studio project: 
+
+- From the "Sessions" menu, upload the compute package and seed model. 
+- Register a client and obtain the corresponding 'client.yaml'.  
+
+On your local machine / client (in the same virtual environment), start the FEDn client: 
+
+.. code-block::
+
+   CLIENT_NUMBER=0 FEDN_AUTH_SCHEME=Bearer fedn run client -in client.yaml --force-ssl --secure=True
+
+
+Or, if you prefer to use Docker, build an image (this might take a long time):
+
+.. code-block::
+
+   docker build -t flower-client .
+
+Then start the client usign Docker:
+
+.. code-block::
+
+   docker run \
+   -v $PWD/client.yaml:/app/client.yaml \
+   -e CLIENT_NUMBER=0 \
+   -e FEDN_AUTH_SCHEME=Bearer \
+   flower-client run client -in client.yaml --secure=True --force-ssl
+
+
+If you are running FEDn in pseudo-local mode:
+------------------------------------------------------------------
+
+Deploy a FEDn network on local host (see `https://fedn.readthedocs.io/en/stable/quickstart.html`). 
+
+Use the FEDn API Client to initalize FEDn with the compute package and seed model: 
+
+.. code-block::
+
+   python init_fedn.py
+
+Create a file 'client.yaml' with the following content: 
+
+.. code-block::
+
+   network_id: fedn-network
+   discover_host: api-server
+   discover_port: 8092
+   name: myclient
+
+Then start the client (using Docker)
+
+.. code-block::
+
+   docker run \
+   -v $PWD/client.yaml:/app/client.yaml \
+   --network=fedn_default \
+   -e CLIENT_NUMBER=0 \
+   flower-client run client -in client.yaml

examples/flower-client/bin/init_venv.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+set -e
+
+# Init venv
+python3 -m venv .flower-client
+
+# Pip deps
+.flower-client/bin/pip install --upgrade pip
+.flower-client/bin/pip install -e ../../fedn
+.flower-client/bin/pip install -r requirements.txt

examples/flower-client/client/entrypoint

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+import os
+
+import fire
+from flwr_client import app
+
+from fedn.utils.flowercompat.client_app_adapter import FlwrClientAppAdapter
+from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+
+HELPER_MODULE = "numpyhelper"
+helper = get_helper(HELPER_MODULE)
+
+flwr_adapter = FlwrClientAppAdapter(app)
+
+
+def _get_node_id():
+    """Get client number from environment variable."""
+
+    number = os.environ.get("CLIENT_NUMBER", "0")
+    return int(number)
+
+
+def save_parameters(out_path, parameters_np):
+    """Save model paramters to file.
+
+    :param model: The model to serialize.
+    :type model: torch.nn.Module
+    :param out_path: The path to save to.
+    :type out_path: str
+    """
+    helper.save(parameters_np, out_path)
+
+
+def init_seed(out_path="seed.npz"):
+    """Initialize seed model and save it to file.
+
+    :param out_path: The path to save the seed model to.
+    :type out_path: str
+    """
+    # Init and save
+    parameters_np = flwr_adapter.init_parameters(partition_id=_get_node_id())
+    save_parameters(out_path, parameters_np)
+
+
+def train(in_model_path, out_model_path):
+    """Complete a model update.
+
+    Load model paramters from in_model_path (managed by the FEDn client),
+    perform a model update through the flower client, and write updated paramters
+    to out_model_path (picked up by the FEDn client).
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_model_path: The path to save the output model to.
+    :type out_model_path: str
+    """
+    parameters_np = helper.load(in_model_path)
+
+    # Train on flower client
+    params, num_examples = flwr_adapter.train(
+        parameters=parameters_np, partition_id=_get_node_id()
+    )
+
+    # Metadata needed for aggregation server side
+    metadata = {
+        # num_examples are mandatory
+        "num_examples": num_examples,
+    }
+
+    # Save JSON metadata file (mandatory)
+    save_metadata(metadata, out_model_path)
+
+    # Save model update (mandatory)
+    save_parameters(out_model_path, params)
+
+
+def validate(in_model_path, out_json_path, data_path=None):
+    """Validate model on the clients test dataset.
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_json_path: The path to save the output JSON to.
+    :type out_json_path: str
+    :param data_path: The path to the data file.
+    :type data_path: str
+    """
+    parameters_np = helper.load(in_model_path)
+
+    loss, accuracy = flwr_adapter.evaluate(parameters_np, partition_id=_get_node_id())
+
+    # JSON schema
+    report = {
+        "test_loss": loss,
+        "test_accuracy": accuracy,
+    }
+    print(f"Loss: {loss}, accuracy: {accuracy}")
+    # Save JSON
+    save_metrics(report, out_json_path)
+
+
+if __name__ == "__main__":
+    fire.Fire(
+        {
+            "init_seed": init_seed,
+            "train": train,
+            "validate": validate,
+        }
+    )

examples/flower-client/client/fedn.yaml

@@ -0,0 +1,5 @@
+entry_points:
+  train:
+    command: python entrypoint train $ENTRYPOINT_OPTS
+  validate:
+    command: python entrypoint validate $ENTRYPOINT_OPTS

examples/flower-client/client/flwr_client.py

@@ -0,0 +1,39 @@
+"""Flower client code using the ClientApp abstraction.
+Code adapted from https://github.com/adap/flower/tree/main/examples/app-pytorch.
+"""
+
+from flwr.client import ClientApp, NumPyClient
+from flwr_task import (DEVICE, Net, get_weights, load_data, set_weights, test,
+                       train)
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+    def __init__(self, cid) -> None:
+        super().__init__()
+        print(f"STARTED CLIENT WITH CID {cid}")
+        self.net = Net().to(DEVICE)
+        self.trainloader, self.testloader = load_data(
+            partition_id=int(cid), num_clients=10
+        )
+
+    def fit(self, parameters, config):
+        set_weights(self.net, parameters)
+        train(self.net, self.trainloader, epochs=3)
+        return get_weights(self.net), len(self.trainloader.dataset), {}
+
+    def evaluate(self, parameters, config):
+        set_weights(self.net, parameters)
+        loss, accuracy = test(self.net, self.testloader)
+        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient(cid).to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+)