Upgrade to Flower Next#

Welcome to the migration guide for updating Flower to Flower Next! Whether you’re a seasoned user or just getting started, this guide will help you smoothly transition your existing setup to take advantage of the latest features and improvements in Flower Next, starting from version 1.8.

Note

This guide shows how to reuse pre-1.8 Flower code with minimum code changes by using the compatibility layer in Flower Next. In another guide, we will show how to run Flower Next end-to-end with pure Flower Next APIs.

Let’s dive in!

Installer la mise à jour#

Using pip#

Here’s how to update an existing installation of Flower to Flower Next with pip:

$ python -m pip install -U flwr

or if you need Flower Next with simulation:

$ python -m pip install -U flwr[simulation]

Ensure you set the following version constraint in your requirements.txt

# Without simulation support
flwr>=1.8,<2.0

# With simulation support
flwr[simulation]>=1.8, <2.0

or pyproject.toml:

# Without simulation support
dependencies = ["flwr>=1.8,2.0"]

# With simulation support
dependencies = ["flwr[simulation]>=1.8,2.0"]

Using Poetry#

Update the flwr dependency in pyproject.toml and then reinstall (don’t forget to delete poetry.lock via rm poetry.lock before running poetry install).

Ensure you set the following version constraint in your pyproject.toml:

[tool.poetry.dependencies]
python = "^3.8"

# Without simulation support
flwr = ">=1.8,<2.0"

# With simulation support
flwr = { version = ">=1.8,<2.0", extras = ["simulation"] }

Changements nécessaires#

In Flower Next, the infrastructure and application layers have been decoupled. Instead of starting a client in code via start_client(), you create a ClientApp() and start it via the command line. Instead of starting a server in code via start_server(), you create a ServerApp() and start it via the command line. The long-running components of server and client are called SuperLink and SuperNode. The following non-breaking changes that require manual updates and allow you to run your project both in the traditional way and in the Flower Next way:

`ClientApp()`#

Wrap your existing client with ClientApp() instead of launching it via start_client(). Here’s an example:

# Flower 1.8
def client_fn(cid: str):
    return flwr.client.FlowerClient().to_client()

app = flwr.client.ClientApp(
   client_fn=client_fn,
)

# Flower 1.7
if __name__ == "__main__":
    flwr.client.start_client(
       server_address="127.0.0.1:8080",
       client=flwr.client.FlowerClient().to_client(),
    )

`ServerApp()`#

Wrap your existing strategy with ServerApp() instead of starting the server via start_server(). Here’s an example:

# Flower 1.8
app = flwr.server.ServerApp(
    config=config,
    strategy=strategy,
)

# Flower 1.7
if __name__ == "__main__":
    flwr.server.start_server(
        server_address="0.0.0.0:8080",
        config=config,
        strategy=strategy,
    )

Deployment#

Run the SuperLink using flower-superlink before running, in sequence, flower-client-app (2x) and flower-server-app. There is no need to execute client.py and server.py as Python scripts.
Here’s an example to start the server without HTTPS (only for prototyping):

# Start a Superlink
$ flower-superlink --insecure

# In a new terminal window, start a long-running SuperNode
$ flower-client-app client:app --insecure

# In another terminal window, start another long-running SuperNode (at least 2 SuperNodes are required)
$ flower-client-app client:app --insecure

# In yet another terminal window, run the ServerApp (this starts the actual training run)
$ flower-server-app server:app --insecure

Here’s another example to start with HTTPS. Use the --certificates command line argument to pass paths to (CA certificate, server certificate, and server private key).

# Start a secure Superlink
$ flower-superlink --certificates \
    <your-ca-cert-filepath> \
    <your-server-cert-filepath> \
    <your-privatekey-filepath>

# In a new terminal window, start a long-running secure SuperNode
$ flower-client-app client:app \
    --root-certificates <your-ca-cert-filepath> \
    --server 127.0.0.1:9092

# In another terminal window, start another long-running secure SuperNode (at least 2 SuperNodes are required)
$ flower-client-app client:app \
    --root-certificates <your-ca-cert-filepath> \
    --server 127.0.0.1:9092

# In yet another terminal window, run the ServerApp (this starts the actual training run)
$ flower-server-app server:app \
    --root-certificates <your-ca-cert-filepath> \
    --server 127.0.0.1:9091

Simulation in CLI#

Wrap your existing client and strategy with ClientApp() and ServerApp(), respectively. There is no need to use start_simulation() anymore. Here’s an example:

# Regular Flower client implementation
class FlowerClient(NumPyClient):
    # ...

# Flower 1.8
def client_fn(cid: str):
    return FlowerClient().to_client()

client_app = flwr.client.ClientApp(
   client_fn=client_fn,
)

server_app = flwr.server.ServerApp(
    config=config,
    strategy=strategy,
)

# Flower 1.7
if __name__ == "__main__":
    hist = flwr.simulation.start_simulation(
        num_clients=100,
        ...
    )

Run flower-simulation in CLI and point to the server_app / client_app object in the code instead of executing the Python script. Here’s an example (assuming the server_app and client_app objects are in a sim.py module):

# Flower 1.8
$ flower-simulation \
    --server-app=sim:server_app \
    --client-app=sim:client_app \
    --num-supernodes=100

# Flower 1.7
$ python sim.py

Set default resources for each ClientApp() using the --backend-config command line argument instead of setting the client_resources argument in start_simulation(). Here’s an example:

# Flower 1.8
$ flower-simulation \
    --client-app=sim:client_app \
    --server-app=sim:server_app \
    --num-supernodes=100 \
    --backend-config='{"client_resources": {"num_cpus": 2, "num_gpus": 0.25}}'

# Flower 1.7 (in `sim.py`)
if __name__ == "__main__":
    hist = flwr.simulation.start_simulation(
        num_clients=100,
        client_resources = {'num_cpus': 2, "num_gpus": 0.25},
        ...
    )

Simulation in a Notebook#

Run run_simulation() in your notebook instead of start_simulation(). Here’s an example:

NUM_CLIENTS = <specify-an-integer>

def client_fn(cid: str):
    # ...
    return FlowerClient().to_client()

client_app = flwr.client.ClientApp(
   client_fn=client_fn,
)

server_app = flwr.server.ServerApp(
    config=config,
    strategy=strategy,
)

backend_config = {"client_resources": {"num_cpus": 2, "num_gpus": 0.25}}

# Flower 1.8
flwr.simulation.run_simulation(
    server_app=server_app,
    client_app=client_app,
    num_supernodes=NUM_CLIENTS,
    backend_config=backend_config,
)

# Flower 1.7
flwr.simulation.start_simulation(
    client_fn=client_fn,
    num_clients=NUM_CLIENTS,
    config=config,
    strategy=strategy,
    client_resources=backend_config["client_resources"],
)

Aide supplémentaire#

Some official Flower code examples are already updated to Flower Next so they can serve as a reference for using the Flower Next API. If there are further questions, join the Flower Slack and use the channel #questions. You can also participate in Flower Discuss where you can find us answering questions, or share and learn from others about migrating to Flower Next.

Important

As we continuously enhance Flower Next at a rapid pace, we’ll be periodically updating this guide. Please feel free to share any feedback with us!

Happy migrating! 🚀

Upgrade to Flower Next#

Installer la mise à jour#

Using pip#

Using Poetry#

Changements nécessaires#

ClientApp()#

ServerApp()#

Deployment#

Simulation in CLI#

Simulation in a Notebook#

Aide supplémentaire#

`ClientApp()`#

`ServerApp()`#