### Clone a Flower quickstart example

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/run-quickstart-examples-docker-compose.rst

Downloads the specified quickstart example repository and prepares the directory for deployment.

```bash
$ git clone --depth=1 https://github.com/flwrlabs/flower.git \
     && mv flower/examples/quickstart-pytorch . \
     && rm -rf flower && cd quickstart-pytorch
```

--------------------------------

### Install Flower and Quickstart Project

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-series-customize-the-client-pytorch.rst

Installs the Flower package and sets up a new PyTorch quickstart project. This is a prerequisite for running the tutorial examples.

```shell
pip install -U "flwr[simulation]"
```

```shell
flwr new @flwrlabs/quickstart-pytorch
```

```shell
cd quickstart-pytorch
pip install -e .
```

--------------------------------

### Install and Run Locally

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/nextjs-web-chats/README.md

Commands to install dependencies and start the development server.

```bash
npm install
npm run dev
```

--------------------------------

### Install Flower and Initialize Project

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-quickstart-pytorch.rst

Install the necessary Flower simulation dependencies and generate a new project structure using the quickstart template.

```shell
# In a new Python environment
$ pip install flwr[simulation]
```

```shell
$ flwr new @flwrlabs/quickstart-pytorch
```

--------------------------------

### Run Quickstart Example

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/tutorial-quickstart-docker-compose.rst

Executes the quickstart Flower application, connecting to the configured local SuperLink. The --stream flag is used to monitor the progress and output.

```bash
$ flwr run quickstart-pytorch local-deployment --stream
```

--------------------------------

### Install Project and Dependencies

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-quickstart-pytorch-lightning.rst

Navigate to the cloned example directory and install the project along with its dependencies using pip. Ensure your virtual environment is activated before running this command.

```shell
# Navigate to the example directory
$ cd path/to/quickstart-pytorch-lightning

# Install project and dependencies
$ pip install -e .
```

--------------------------------

### Clone and Run Hello World Example

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/README.md

Steps to clone the Flower repository, set up the hello-world-ts example project, install dependencies, build, and run it.

```sh
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp && \
mv _tmp/intelligence/ts/examples/hello-world-ts . && \
rm -rf _tmp && \
cd hello-world-ts

npm i
npm run build
npm run start
```

--------------------------------

### Deploy Services with State and TLS

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/tutorial-quickstart-docker-compose.rst

Commands to start the full deployment stack and run the quickstart project.

```bash
$ docker compose -f compose.yml -f with-tls.yml -f with-state.yml up --build -d
```

```bash
$ flwr run quickstart-pytorch local-deployment-tls --stream
```

--------------------------------

### Setup Python Environment

Source: https://github.com/flwrlabs/flower/blob/main/baselines/floco/README.md

Commands to create and activate a virtual environment and install project dependencies.

```bash
# Create a python env
pyenv virtualenv 3.10.14 floco

# Activate it
pyenv activate floco

# Install project including packages
pip install .
```

--------------------------------

### Install System Prerequisites (macOS, RHEL, Debian)

Source: https://github.com/flwrlabs/flower/blob/main/examples/fedrag/README.md

Installs necessary packages like wget and git-lfs for downloading files and large repositories. Ensure these are installed before proceeding with the example.

```shell
# wget is used to download .tar files from the Web
# git-lfs is used to download large files from the Hugging Face respository

# macOS
brew install wget   
brew install git-lfs

# RHEL
yum install wget   
yum install git-lfs

# Ubuntu/Debian
apt install wget
apt install git-lfs
 
# Windows
# If you are on Windows, it is highly recommended to make use of WSL with Ubuntu to run your Flower apps. 
# Then, you can install the packages using the above Ubuntu commands.
# Extra tip: with WSL you can also make use of the NVIDIA GPU in your Windows host. 
 
# enable Git LFS in your Git environment
# (holds for all systems)
git lfs install
```

--------------------------------

### Run Development Server

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/web-chat/README.md

Start the development server for the web chat example. This command will typically output a URL to access the application in your browser.

```bash
npm run dev
```

--------------------------------

### Install FjORD via pip

Source: https://github.com/flwrlabs/flower/blob/main/baselines/fjord/README.md

Standard installation method using pip and the provided setup script.

```bash
pip install -r requirements.txt
python setup.py install
```

--------------------------------

### Clone Example Project

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/streaming/README.md

Download the example project from GitHub. This command clones the repository, extracts the streaming example, and navigates into the example directory.

```bash
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp && mv _tmp/intelligence/ts/examples/streaming . && rm -rf _tmp && cd streaming
```

--------------------------------

### Flower Docker Setup Guide

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/ref-changelog.md

This section outlines the steps to set up and run Flower components using Docker. It covers Docker images for Flower SuperLink, SuperNode, and ServerApp.

```bash
# Example Docker command (conceptual, actual commands may vary)
docker run -p 8080:8080 flwr/serverapp

```

--------------------------------

### Install Flower and Initialize Project

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-numpy/README.md

Commands to install the Flower framework and scaffold a new project.

```shell
pip install flwr
```

```shell
flwr new @flwrlabs/quickstart-numpy
```

--------------------------------

### Install Google Cloud SDK (Windows)

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-run-flower-on-gcp.rst

Provides instructions for installing the Google Cloud SDK on Windows by downloading and running the installer. Refer to the official page for the latest installer link.

```bash
# Windows
# Download the Windows installer from the Google Cloud SDK page
# https://dl.google.com/dl/cloudsdk/channels/rapid/GoogleCloudSDKInstaller.exe
# Run the .exe installer and follow the on-screen instructions
```

--------------------------------

### Generate a new PyTorch quickstart app

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-run-simulations.rst

Use the `flwr new` command to generate a new Flower application. This example specifically generates the PyTorch quickstart app. You can also run `flwr new` without arguments to see a list of recommended apps.

```shell
flwr new @flwrlabs/quickstart-pytorch
```

--------------------------------

### Create a new Flower App with PyTorch Quickstart

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-run-flower-with-deployment-engine.rst

Use the 'flwr new' command to create a new Flower app based on the PyTorch quickstart template. This command downloads and unpacks the template, and provides instructions for installing dependencies and running the app.

```bash
$ flwr new @flwrlabs/quickstart-pytorch

❇ Requesting download link for @flwrlabs/quickstart-pytorch...
❖ Downloading ZIP into memory...
✶ Unpacking into /Users/alice/quickstart-pytorch...
✦ Flower App creation successful.

To run your Flower App, first install its dependencies:

        cd quickstart-pytorch && pip install -e .

then, run the app:

        flwr run .

ℹ] Check the README in your app directory to learn how to
customize it and how to run it using the Deployment Runtime.
```

--------------------------------

### Clone the fastai example repository

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-quickstart-fastai.rst

Downloads the quickstart-fastai example project from the Flower GitHub repository.

```shell
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp \
             && mv _tmp/examples/quickstart-fastai . \
             && rm -rf _tmp && cd quickstart-fastai
```

--------------------------------

### Fetch Flower App Template

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-xgboost/README.md

Fetch a new Flower application template for the quickstart XGBoost example. This command creates a new directory with the project structure.

```shell
flwr new @flwrlabs/quickstart-xgboost
```

--------------------------------

### Start Server (Flower 1.7)

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-upgrade-to-flower-1.13.rst

Legacy method for starting a server using start_server.

```python
if __name__ == "__main__":
    start_server(
        server_address="0.0.0.0:8080",
        config=config,
        strategy=strategy,
    )
```

--------------------------------

### Example Updates with requirements.txt

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/ref-changelog.md

Various examples have been updated, including simplified advanced examples and improved macOS compatibility. All examples now include a `requirements.txt` file alongside `pyproject.toml`.

```python
# Example requirements.txt content (typical structure)
# package_name==version
```

--------------------------------

### Clone Web Chat Example

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/web-chat/README.md

Use this command to download the web chat example project. It clones the repository, moves the example to the current directory, cleans up temporary files, and navigates into the example's directory.

```bash
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp && mv _tmp/intelligence/ts/examples/web-chat . && rm -rf _tmp && cd web-chat
```

--------------------------------

### Install Flower pre-release

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-install-flower.rst

Installation of alpha, beta, or release candidate versions of Flower.

```bash
python -m pip install -U --pre flwr
```

```bash
python -m pip install -U --pre 'flwr[simulation]'
```

--------------------------------

### Install project dependencies

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-quickstart-fastai.rst

Navigates to the project directory and installs the package in editable mode.

```shell
# Navigate to the example directory
$ cd path/to/quickstart-fastai

# Install project and dependencies
$ pip install -e .
```

--------------------------------

### Install Flower Datasets with extras

Source: https://github.com/flwrlabs/flower/blob/main/baselines/docs/source/how-to-install-flwr-datasets.md

Install with specific extras for vision or audio dataset support.

```bash
python -m pip install "flwr-datasets[vision]"
```

```bash
python -m pip install "flwr-datasets[audio]"
```

--------------------------------

### Flower Authentication Example

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/ref-changelog.md

This example demonstrates how to get started with the new built-in client node authentication system in Flower 1.9. It requires no external authentication systems.

```python
import flwr as fl

# Load the Flower authentication example
app = flwr.FlowerAuthenticationApp()

# Run the application
app.run()

```

--------------------------------

### Add Print Statement to Get Weights Function

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/tutorial-quickstart-docker-compose.rst

Modifies the get_weights function in the PyTorch quickstart example to include a print statement. This demonstrates how to update application code.

```python
# ...
def get_weights(net):
    print("Get weights")
    return [val.cpu().numpy() for _, val in net.state_dict().items()]


# ...
```

--------------------------------

### Wrap Client with ClientApp

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-upgrade-to-flower-1.13.rst

Example showing the transition from start_client to the ClientApp architecture.

```python
from flwr.app import Context
from flwr.client import start_client
from flwr.clientapp import ClientApp


# Flower 1.10 and later (recommended)
def client_fn(context: Context):
    return FlowerClient().to_client()


app = ClientApp(client_fn=client_fn)


# # Flower 1.8 - 1.9 (deprecated, no longer supported)
# def client_fn(cid: str):
#     return FlowerClient().to_client()
#
#
# app = ClientApp(client_fn=client_fn)


# Flower 1.7 (deprecated, only for backwards-compatibility)
if __name__ == "__main__":
    start_client(
        server_address="127.0.0.1:8080",
        client=FlowerClient().to_client(),
    )
```

--------------------------------

### Setup development environment

Source: https://github.com/flwrlabs/flower/blob/main/baselines/docs/source/contributor-how-to-develop-flwr-datasets.md

Initializes the project environment using uv.

```bash
cd datasets
uv sync --all-extras
```

--------------------------------

### Example Output of Version Check

Source: https://github.com/flwrlabs/flower/blob/main/datasets/docs/source/how-to-install-flwr-datasets.md

This is the expected output when the Flower Datasets installation is verified successfully, showing the installed version number.

```text
0.6.0
```

--------------------------------

### Wrap Strategy with ServerApp

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-upgrade-to-flower-1.13.rst

Example showing the transition from start_server to the ServerApp architecture.

```python
from flwr.app import Context
from flwr.server import ServerAppComponents, ServerConfig, start_server
from flwr.server.strategy import FedAvg
from flwr.serverapp import ServerApp


# Flower 1.10 and later (recommended)
def server_fn(context: Context):
    strategy = FedAvg()
    config = ServerConfig()
    return ServerAppComponents(config=config, strategy=strategy)
```

--------------------------------

### Audit Log Examples

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-configure-audit-logging.rst

Example log outputs for various system interactions including starting runs, listing runs, and SuperNode communication.

```shell
INFO :      [AUDIT] {"timestamp": "2025-07-12T10:24:21Z", "actor": {"actor_id": "...", "description": "...", "ip_address": "..."}, "event": {"action": "ControlServicer.StartRun", "run_id": "...", "fab_hash": "..."}, "status": "started"}
INFO :      ControlServicer.StartRun
INFO :      [AUDIT] {"timestamp": "2025-07-12T10:24:21Z", "actor": {"actor_id": "...", "description": "...", "ip_address": "..."}, "event": {"action": "ControlServicer.StartRun", "run_id": "...", "fab_hash": "..."}, "status": "completed"}
```

```shell
INFO :      [AUDIT] {"timestamp": "2025-07-12T10:26:35Z", "actor": {"actor_id": "...", "description": "...", "ip_address": "..."}, "event": {"action": "ControlServicer.ListRuns", "run_id": null, "fab_hash": null}, "status": "started"}
INFO :      ControlServicer.ListRuns
INFO :      [AUDIT] {"timestamp": "2025-07-12T10:26:35Z", "actor": {"actor_id": "...", "description": "...", "ip_address": "..."}, "event": {"action": "ControlServicer.ListRuns", "run_id": null, "fab_hash": null}, "status": "completed"}
```

```shell
INFO :      [AUDIT] {"timestamp": "2025-07-14T10:27:02Z", "actor": {"actor_id": "...", "description": "SuperNode", "ip_address": "..."}, "event": {"action": "FleetServicer.PullMessages", "run_id": null, "fab_hash": null}, "status": "started"}
INFO :      [Fleet.PullMessages] node_id=...
```

--------------------------------

### Clone Example Project

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/browser-extension/README.md

Use this command to download the example project. Ensure you are in the correct directory after cloning.

```bash
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp && mv _tmp/intelligence/ts/examples/encrypted . && rm -rf _tmp && cd encrypted
```

--------------------------------

### Clone Example Project

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/hello-world-ts/README.md

Download the example project using git clone. This command fetches the project and sets up the necessary files.

```bash
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp && mv _tmp/intelligence/ts/examples/hello-world-ts . && rm -rf _tmp && cd hello-world-ts
```

--------------------------------

### Install PyTorch SuperNode Helm Chart

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/helm/how-to-deploy-supernode-using-helm.md

Command to install the Flower SuperNode Helm chart for a PyTorch setup using a specified values.yaml file.

```sh
helm install pytorch . --values values.yaml
```

--------------------------------

### Clone the Hello World project

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/ts/examples/hello-world-js/README.md

Downloads the specific example directory from the Flower repository.

```bash
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp && mv _tmp/intelligence/ts/examples/hello-world-js . && rm -rf _tmp && cd hello-world-js
```

--------------------------------

### Install Flower and Initialize Project

Source: https://github.com/flwrlabs/flower/blob/main/examples/fl-dp-sa/README.md

Commands to install the Flower framework and scaffold a new project directory.

```shell
pip install flwr
```

```shell
flwr new @flwrlabs/fl-dp-sa
```

--------------------------------

### View ServerApp Logs

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/tutorial-quickstart-docker-compose.rst

Example output from the ServerApp logs showing the initialization process.

```text
INFO :      Starting logstream for run_id `10386255862566726253`
INFO :      Starting Flower ServerApp
WARNING :   Option `--insecure` was set. Starting insecure HTTP channel to superlink:9091.
🎊 Successfully installed quickstart-pytorch to /app/.flwr/apps/flower.quickstart-pytorch.1.0.0.35361a47.
Get weights
INFO :      Starting Flower ServerApp, config: num_rounds=3, no round_timeout
```

--------------------------------

### Environment Setup for FedRep Baseline

Source: https://github.com/flwrlabs/flower/blob/main/baselines/fedrep/README.md

Commands to create and activate a Python virtual environment and install the project dependencies.

```bash
# Create the environment
pyenv virtualenv 3.10.12 fedrep-env

# Activate it
pyenv activate fedrep-env

# Then install the project
pip install -e .
```

--------------------------------

### Install Flower and Fetch Project

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-monai/README.md

Commands to install the Flower framework and initialize a new project template.

```shell
pip install flwr
```

```shell
flwr new @flwrlabs/quickstart-monai
```

--------------------------------

### Setup Python Environment with Poetry

Source: https://github.com/flwrlabs/flower/blob/main/baselines/dasha/README.md

Commands to configure a Python 3.10 environment and install dependencies using Poetry.

```bash
# Set Python 3.10
pyenv local 3.10.6
# Tell poetry to use python 3.10
poetry env use 3.10.6
# Install the base Poetry environment
# By default, Poetry installs the PyTorch package with Python 3.10 and CUDA 11.8.
# If you have a different setup, then change the "torch" and "torchvision" lines in [tool.poetry.dependencies].
poetry install

# Activate the environment
poetry shell
```

--------------------------------

### Install Flower and Fetch Project

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-fastai/README.md

Commands to install the Flower framework and initialize a new project template.

```shell
pip install flwr
```

```shell
flwr new @flwrlabs/quickstart-fastai
```

--------------------------------

### Run Flower Quickstart Project

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/tutorial-quickstart-docker-compose.rst

Executes the quickstart-pytorch project in a local deployment environment.

```bash
$ flwr run quickstart-pytorch local-deployment --stream
```

```bash
$ flwr run quickstart-pytorch local-deployment-tls --stream
```

--------------------------------

### Clone the project repository

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-lerobot/README.md

Downloads the example project and navigates into the directory.

```shell
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp \
		&& mv _tmp/examples/quickstart-lerobot . \
		&& rm -rf _tmp && cd quickstart-lerobot
```

--------------------------------

### Compare OpenFL and Flower project structures

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-migrate-from-openfl.rst

Visual representation of the directory structure differences between a typical OpenFL example and a Flower PyTorch quickstart project.

```text
openfl-example
├── requirements.txt
├── .workspace
├── plan
│   ├── plan.yaml
│   ├── cols.yaml
│   ├── defaults
│   └── data.yaml
├── logs
├── cert
├── save
├── data
└── src
    ├── __init__.py
    ├── taskrunner.py
    ├── utils.py
    └── dataloader.py
```

```text
quickstart-pytorch
├── pytorchexample
│   ├── __init__.py
│   ├── client_app.py
│   ├── server_app.py
│   └── task.py
├── pyproject.toml
└── README.md
```

--------------------------------

### Environment Setup for FedNova Baseline

Source: https://github.com/flwrlabs/flower/blob/main/baselines/fednova/README.md

These commands guide you through setting up the Python environment for the FedNova baseline using pyenv and Poetry. Ensure you are in the correct directory before executing.

```bash
# Navigate to baselines/fednova
cd baselines/fednova

# Set python version
pyenv install 3.10.11
pyenv local 3.10.11

# Tell poetry to use python 3.10
poetry env use 3.10.11

# install the base Poetry environment, make sure there is no existing poetry.lock file and pyproject.toml file is located in the current directory
poetry install

# activate the environment
poetry shell
```

--------------------------------

### Initialize start_simulation with Client IDs

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/ref-changelog.md

Demonstrates initializing `start_simulation` with a list of client IDs. This allows passing custom IDs to `client_fn` for easier data partition loading.

```python
start_simulation(client_fn=client_fn, num_clients=num_clients, client_ids=client_ids)
```

--------------------------------

### Configure SuperLink Connection in Flower Config

Source: https://github.com/flwrlabs/flower/blob/main/examples/whisper-federated-finetuning/README.md

Define the SuperLink connection in your Flower Configuration file (e.g., `flwr config list`). This example assumes a local SuperLink and disables SSL for simplicity; consult documentation for secure setups.

```toml
[superlink.remote]
address = '127.0.0.1:9093' # IP:9093 of your superlink (assumed localhost superlink)
insecure = true # Check the documentation to setup with SSL
```

--------------------------------

### Example Flower configuration output

Source: https://github.com/flwrlabs/flower/blob/main/examples/embedded-devices/README.md

Sample output showing the location of the configuration file and existing connections.

```console
# Example output:
Flower Config file: /path/to/your/.flwr/config.toml
SuperLink connections:
 supergrid
 local (default)
```

--------------------------------

### Initialize Flower Server and Clients

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-run-flower-on-azure.rst

Run these commands to start the Flower long-running processes. The server (SuperLink) and clients (SuperNode) require specific configurations, including the SuperLink's public IP and port. Use the --insecure flag for initial setup.

```bash
# Server VM (SuperLink)
flower-superlink --insecure
```

```bash
# Client-1 VM (SuperNode-1)
flower-supernode \
  --insecure \
  --superlink="SUPERLINK_PUBLIC_IP:9092"  # SuperLink public ip and port
```

```bash
# Client-2 VM (SuperNode-2)
flower-supernode \
  --insecure \
  --superlink="SUPERLINK_PUBLIC_IP:9092"  # SuperLink public ip and port
```

--------------------------------

### Install Flower and Initialize Project

Source: https://github.com/flwrlabs/flower/blob/main/examples/fl-tabular/README.md

Commands to install the Flower framework and scaffold a new tabular federated learning project.

```shell
pip install flwr
```

```shell
flwr new @flwrlabs/fl-tabular
```

--------------------------------

### Install Flower 1.0 with pip

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-upgrade-to-flower-1.0.rst

Use the -U flag with pip to upgrade an existing Flower installation to version 1.0. This applies to both core installation and installation with simulation extras.

```bash
python -m pip install -U flwr
```

```bash
python -m pip install -U 'flwr[simulation]'
```

--------------------------------

### Fetch Flower App

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-mlx/README.md

Initialize a new Flower project using the quickstart template.

```shell
flwr new @flwrlabs/quickstart-mlx
```

--------------------------------

### Initialize and execute a FedAvg strategy

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-use-strategies.rst

Demonstrates the basic instantiation of a ServerApp and the execution of the FedAvg strategy using the start method.

```python
# Create ServerApp
app = ServerApp()


@app.main()
def main(grid: Grid, context: Context) -> None:
    """Main entry point for the ServerApp."""

    # Load global model
    global_model = Net()
    arrays = ArrayRecord(global_model.state_dict())

    # Initialize FedAvg strategy with default settings
    strategy = FedAvg()

    # Start strategy, run FedAvg for `num_rounds`
    result = strategy.start(
        grid=grid,
        initial_arrays=arrays,
    )
```

--------------------------------

### Install pnpm Globally

Source: https://github.com/flwrlabs/flower/blob/main/baselines/docs/source/contributor-how-to-build-from-source.md

Use this command to install pnpm globally via npm. Ensure npm is installed and configured.

```bash
npm install -g pnpm
```

--------------------------------

### Initialize Environment Paths

Source: https://github.com/flwrlabs/flower/blob/main/baselines/fedbn/docs/multirun_plot.ipynb

Sets up project directory and paths for saving figures and reading results.

```python
import pickle, yaml
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import os

PROJECT_DIRECTORY = Path(os.path.abspath("")).parent
SAVE_FIG_PATH = PROJECT_DIRECTORY / "_static"
RESULTS_PATH = PROJECT_DIRECTORY / "results"
```

--------------------------------

### Install Baseline Project

Source: https://github.com/flwrlabs/flower/blob/main/baselines/dev/baseline/README.md

Install the baseline project in the activated virtual environment using pip. This command installs the project and its dependencies.

```bash
pip install -e .
```

--------------------------------

### Initialize Poetry Environment

Source: https://github.com/flwrlabs/flower/blob/main/baselines/fedmlb/README.md

Install dependencies and activate the project environment.

```bash
# cd to your FedMLB directory (i.e. where the `pyproject.toml` is)

# install the base Poetry environment
poetry install

# activate the environment
poetry shell
```

--------------------------------

### Example ServerApp Console Output

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-series-customize-the-client-pytorch.rst

Sample output showing logged training metadata from clients.

```console
INFO :      [ROUND 1/3]
INFO :      configure_train: Sampled 25 nodes (out of 1000)
{'training_time': 123.45, 'converged': True, 'training_losses': {'epoch1': 0.56, 'epoch2': 0.34}}
{'training_time': 130.67, 'converged': False, 'training_losses': {'epoch1': 0.60, 'epoch2': 0.40}}
...
```

--------------------------------

### Verify installation

Source: https://github.com/flwrlabs/flower/blob/main/baselines/docs/source/how-to-install-flwr-datasets.md

Check the installed version of the library.

```bash
python -c "import flwr_datasets;print(flwr_datasets.__version__)"
```

--------------------------------

### Initialize a new Flower project

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-migrate-from-openfl.rst

Create a new Flower project using the PyTorch quickstart template.

```shell
$ flwr new @flwrlabs/quickstart-pytorch
```

--------------------------------

### Install Dependencies with pnpm

Source: https://github.com/flwrlabs/flower/blob/main/intelligence/docs/source/contributor-how-to-build-from-source.md

Navigate to the TypeScript SDK directory and install project dependencies using pnpm. Ensure you have pnpm installed and the repository cloned.

```bash
cd intelligence/ts
pnpm install
```

--------------------------------

### Install Flower with REST Extra

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/ref-exit-codes/601.rst

Install the 'flwr' package with the 'rest' extra to enable the REST-based Fleet API. Ensure the installation completes successfully before retrying.

```bash
pip install "flwr[rest]"
```

--------------------------------

### Install Flower Pre-release with pip

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/contributor-how-to-install-development-versions.rst

Use this to install a pre-release version of Flower from PyPI using pip. The -U flag ensures the latest pre-release is installed.

```bash
pip install -U --pre flwr
```

```bash
pip install -U --pre 'flwr[simulation]'
```

--------------------------------

### Execute Strategy Start Method

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-use-strategies.rst

Demonstrates the minimal and customized execution of the federated learning process using the start method.

```python
# Start strategy
result = strategy.start(
    grid=grid,
    initial_arrays=ArrayRecord(...),
)
```

```python
# Define configs to send to ClientApp
train_cfg = ConfigRecord({"lr": 0.1, "optim": "adam"})
eval_cfg = ConfigRecord({"max-steps": 500, "local-checkpoint": True})
```

--------------------------------

### Execute the Flower example

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/docker/run-quickstart-examples-docker-compose.rst

Runs the example using the specified SuperLink connection with streaming logs enabled.

```bash
$ flwr run . local-deployment --stream
```

--------------------------------

### Install and Verify Flower 1.28

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-upgrade-to-flower-1.28.rst

Install the latest Flower version and verify the installation by checking the version number. Ensure you include the 'simulation' extra for full functionality.

```shell
$ pip install -U "flwr[simulation]"
$ flwr --version # should show 1.28.x
```

--------------------------------

### Verify kubectl installation

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/how-to-run-flower-on-gcp.rst

Check the installed version of the kubectl client.

```bash
kubectl version --client  # this will show the installed versions of the Client and Kustomize
```

--------------------------------

### Install App Dependencies

Source: https://github.com/flwrlabs/flower/blob/main/hub/docs/source/how-to-use-app-from-hub.md

Navigate to the downloaded app's directory and install its dependencies using pip. This prepares the app for local execution.

```bash
cd <app_name>
pip install -e .
```

--------------------------------

### Install Flower Datasets via pip

Source: https://github.com/flwrlabs/flower/blob/main/baselines/docs/source/how-to-install-flwr-datasets.md

Standard installation from PyPI.

```bash
python -m pip install flwr-datasets
```

--------------------------------

### Fetch Flower Project

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-pennylane/README.md

Initialize a new Flower project using the quickstart-pennylane template.

```shell
flwr new @flwrlabs/quickstart-pennylane
```

--------------------------------

### Clone Flower PyTorch Lightning Example

Source: https://github.com/flwrlabs/flower/blob/main/framework/docs/source/tutorial-quickstart-pytorch-lightning.rst

Clone the Flower PyTorch Lightning example from GitHub to your local machine. This command downloads the example code and sets up the necessary directory structure.

```shell
git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp \
                 && mv _tmp/examples/quickstart-pytorch-lightning . \
                 && rm -rf _tmp && cd quickstart-pytorch-lightning
```

--------------------------------

### Fetch Flower App

Source: https://github.com/flwrlabs/flower/blob/main/examples/quickstart-catboost/README.md

Create a new Flower project using the quickstart template.

```shell
flwr new @flwrlabs/quickstart-catboost
```

--------------------------------

### Install Dependencies with uv

Source: https://github.com/flwrlabs/flower/blob/main/framework/uv.md

Installs dependencies for a Poetry environment with Python 3.10.19, including all extras and dependency groups. Use `--locked` to install from `uv.lock` and fail if the lockfile is out-of-date.

```bash
uv sync --python=3.10.19 --locked --all-extras --all-groups
```