Create new example flower-via-docker-compose (#2626)

Co-authored-by: jafermarq <[email protected]>

README.md

@@ -135,6 +135,7 @@ Other [examples](https://github.com/adap/flower/tree/main/examples):
 - [Advanced Flower with TensorFlow/Keras](https://github.com/adap/flower/tree/main/examples/advanced-tensorflow)
 - [Advanced Flower with PyTorch](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)
 - Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation_pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation_tensorflow))
+- [Flower through Docker Compose and with Grafana dashboard](https://github.com/adap/flower/tree/main/examples/flower-via-docker-compose)
 
 ## Community
 

examples/flower-via-docker-compose/.gitignore

@@ -0,0 +1,17 @@
+# ignore __pycache__ directories
+__pycache__/
+
+# ignore .pyc files
+*.pyc
+
+# ignore .vscode directory
+.vscode/
+
+# ignore .npz files
+*.npz
+
+# ignore .csv files
+*.csv
+
+# ignore docker-compose.yaml file
+docker-compose.yml

examples/flower-via-docker-compose/Dockerfile

@@ -0,0 +1,19 @@
+# Use an official Python runtime as a parent image
+FROM python:3.10-slim-buster
+
+# Set the working directory in the container to /app
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY ./requirements.txt /app/requirements.txt
+
+# Install gcc and other dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    python3-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install any needed packages specified in requirements.txt
+RUN pip install -r requirements.txt
+
+

examples/flower-via-docker-compose/client.py

@@ -0,0 +1,110 @@
+import os
+import argparse
+import flwr as fl
+import tensorflow as tf
+import logging
+from helpers.load_data import load_data
+import os
+from model.model import Model
+
+logging.basicConfig(level=logging.INFO)  # Configure logging
+logger = logging.getLogger(__name__)  # Create logger for the module
+
+# Make TensorFlow log less verbose
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Parse command line arguments
+parser = argparse.ArgumentParser(description="Flower client")
+
+parser.add_argument(
+    "--server_address", type=str, default="server:8080", help="Address of the server"
+)
+parser.add_argument(
+    "--batch_size", type=int, default=32, help="Batch size for training"
+)
+parser.add_argument(
+    "--learning_rate", type=float, default=0.1, help="Learning rate for the optimizer"
+)
+parser.add_argument("--client_id", type=int, default=1, help="Unique ID for the client")
+parser.add_argument(
+    "--total_clients", type=int, default=2, help="Total number of clients"
+)
+parser.add_argument(
+    "--data_percentage", type=float, default=0.5, help="Portion of client data to use"
+)
+
+args = parser.parse_args()
+
+# Create an instance of the model and pass the learning rate as an argument
+model = Model(learning_rate=args.learning_rate)
+
+# Compile the model
+model.compile()
+
+
+class Client(fl.client.NumPyClient):
+    def __init__(self, args):
+        self.args = args
+
+        logger.info("Preparing data...")
+        (x_train, y_train), (x_test, y_test) = load_data(
+            data_sampling_percentage=self.args.data_percentage,
+            client_id=self.args.client_id,
+            total_clients=self.args.total_clients,
+        )
+
+        self.x_train = x_train
+        self.y_train = y_train
+        self.x_test = x_test
+        self.y_test = y_test
+
+    def get_parameters(self, config):
+        # Return the parameters of the model
+        return model.get_model().get_weights()
+
+    def fit(self, parameters, config):
+        # Set the weights of the model
+        model.get_model().set_weights(parameters)
+
+        # Train the model
+        history = model.get_model().fit(
+            self.x_train, self.y_train, batch_size=self.args.batch_size
+        )
+
+        # Calculate evaluation metric
+        results = {
+            "accuracy": float(history.history["accuracy"][-1]),
+        }
+
+        # Get the parameters after training
+        parameters_prime = model.get_model().get_weights()
+
+        # Directly return the parameters and the number of examples trained on
+        return parameters_prime, len(self.x_train), results
+
+    def evaluate(self, parameters, config):
+        # Set the weights of the model
+        model.get_model().set_weights(parameters)
+
+        # Evaluate the model and get the loss and accuracy
+        loss, accuracy = model.get_model().evaluate(
+            self.x_test, self.y_test, batch_size=self.args.batch_size
+        )
+
+        # Return the loss, the number of examples evaluated on and the accuracy
+        return float(loss), len(self.x_test), {"accuracy": float(accuracy)}
+
+
+# Function to Start the Client
+def start_fl_client():
+    try:
+        client = Client(args).to_client()
+        fl.client.start_client(server_address=args.server_address, client=client)
+    except Exception as e:
+        logger.error("Error starting FL client: %s", e)
+        return {"status": "error", "message": str(e)}
+
+
+if __name__ == "__main__":
+    # Call the function to start the client
+    start_fl_client()

examples/flower-via-docker-compose/config/grafana.ini

@@ -0,0 +1,12 @@
+[security]
+allow_embedding = true
+admin_user = admin
+admin_password = admin
+
+[dashboards]
+default_home_dashboard_path = /etc/grafana/provisioning/dashboards/dashboard_index.json
+
+[auth.anonymous]
+enabled = true
+org_name = Main Org.
+org_role = Admin

examples/flower-via-docker-compose/config/prometheus.yml

@@ -0,0 +1,19 @@
+
+global:
+  scrape_interval:     1s
+  evaluation_interval: 1s
+
+rule_files:
+scrape_configs:
+  - job_name: 'cadvisor'
+    scrape_interval: 1s 
+    metrics_path: '/metrics'
+    static_configs:
+      - targets: ['cadvisor:8080']
+        labels:
+          group: 'cadvisor'
+  - job_name: 'server_metrics'
+    scrape_interval: 1s
+    metrics_path: '/metrics'
+    static_configs:
+      - targets: ['server:8000']