Quickstart with Docker#

This quickstart aims to guide you through the process of containerizing a Flower project and running it end to end using Docker on your local machine.

This tutorial does not use production-ready settings, so you can focus on understanding the basic workflow that uses the minimum configurations.

Prerequisites#

Before you start, make sure that:

  • The flwr CLI is installed locally.

  • The Docker daemon is running.

Step 1: Set Up#

  1. Create a new Flower project (PyTorch):

    $ flwr new quickstart-docker --framework PyTorch --username flower

🔨 Creating Flower project quickstart-docker...
🎊 Project creation successful.

Use the following command to run your project:

      cd quickstart-docker
      pip install -e .
      flwr run

$ cd quickstart-docker
$ pip install -e .

  2. Create a new Docker bridge network called flwr-network:

    $ docker network create --driver bridge flwr-network

    User-defined networks, such as flwr-network, enable IP resolution of container names, a feature absent in the default bridge network. This simplifies quickstart example by avoiding the need to determine host IP first.

Step 3: Start the SuperNode#

Start two SuperNode containers.

  1. Start the first container:

    $ docker run --rm \
    -p 9094:9094 \
    --network flwr-network \
    --name supernode-1 \
    --detach \
    flwr/supernode:1.11.1  \
    --insecure \
    --superlink superlink:9092 \
    --node-config "partition-id=0 num-partitions=2" \
    --supernode-address 0.0.0.0:9094 \
    --isolation process
    Understand the command

    • docker run: This tells Docker to run a container from an image.

    • --rm: Remove the container once it is stopped or the command exits.

    • -p 9094:9094: Map port 9094 of the container to the same port of
      the host machine, allowing other services to access the SuperNode API on
      http://localhost:9094.

    • --network flwr-network: Make the container join the network named flwr-network.

    • --name supernode-1: Assign the name supernode-1 to the container.

    • --detach: Run the container in the background, freeing up the terminal.

    • flwr/supernode:|stable_flwr_version|: This is the name of the image to be run and the specific tag
      of the image.
    • --insecure: This flag tells the container to operate in an insecure mode, allowing
      unencrypted communication.
    • --superlink superlink:9092: Connect to the SuperLink’s Fleet API at the address
      superlink:9092.
    • --node-config "partition-id=0 num-partitions=2": Set the partition ID to 0 and the
      number of partitions to 2 for the SuperNode configuration.
    • --supernode-address 0.0.0.0:9094: Set the address and port number that the SuperNode
      is listening on.
    • --isolation process: Tells the SuperNode that the ClientApp is created by separate
      independent process. The SuperNode does not attempt to create it.

  2. Start the second container:

    $ docker run --rm \
    -p 9095:9095 \
    --network flwr-network \
    --name supernode-2 \
    --detach \
    flwr/supernode:1.11.1  \
    --insecure \
    --superlink superlink:9092 \
    --node-config "partition-id=1 num-partitions=2" \
    --supernode-address 0.0.0.0:9095 \
    --isolation process

Step 4: Start the ClientApp#

The ClientApp Docker image comes with a pre-installed version of Flower and serves as a base for building your own ClientApp image. In order to install the FAB dependencies, you will need to create a Dockerfile that extends the ClientApp image and installs the required dependencies.

  1. Create a ClientApp Dockerfile called Dockerfile.clientapp and paste the following code into it:

    Dockerfile.clientapp#
    1FROM flwr/clientapp:1.11.1
2
3WORKDIR /app
4COPY pyproject.toml .
5RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \
6    && python -m pip install -U --no-cache-dir .
7
8ENTRYPOINT ["flwr-clientapp"]
    Understand the Dockerfile
    • FROM flwr/clientapp:1.11.1: This line specifies that the Docker image
      to be built from is the flwr/clientapp image, version 1.11.1.
    • WORKDIR /app: Set the working directory for the container to /app.
      Any subsequent commands that reference a directory will be relative to this directory.
    • COPY pyproject.toml .: Copy the pyproject.toml file
      from the current working directory into the container’s /app directory.
    • RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml: Remove the flwr dependency
      from the pyproject.toml.
    • python -m pip install -U --no-cache-dir .: Run the pip install command to
      install the dependencies defined in the pyproject.toml file

      The -U flag indicates that any existing packages should be upgraded, and
      --no-cache-dir prevents pip from using the cache to speed up the installation.
    • ENTRYPOINT ["flwr-clientapp"]: Set the command flwr-clientapp to be
      the default command run when the container is started.

    Important

    Note that flwr is already installed in the flwr/clientapp base image, so only other package dependencies such as flwr-datasets, torch, etc., need to be installed. As a result, the flwr dependency is removed from the pyproject.toml after it has been copied into the Docker image (see line 5).

  2. Next, build the ClientApp Docker image by running the following command in the directory where the Dockerfile is located:

    $ docker build -f Dockerfile.clientapp -t flwr_clientapp:0.0.1 .

    Note

    The image name was set as flwr_clientapp with the tag 0.0.1. Remember that these values are merely examples, and you can customize them according to your requirements.

  3. Start the first ClientApp container:

    $ docker run --rm \
    --network flwr-network \
    --detach \
    flwr_clientapp:0.0.1  \
    --supernode supernode-1:9094
    Understand the command

    • docker run: This tells Docker to run a container from an image.

    • --rm: Remove the container once it is stopped or the command exits.

    • --network flwr-network: Make the container join the network named flwr-network.

    • --detach: Run the container in the background, freeing up the terminal.

    • flwr_clientapp:0.0.1: This is the name of the image to be run and the specific tag
      of the image.
    • --supernode supernode-1:9094: Connect to the SuperNode’s Fleet API at the address
      supernode-1:9094.

  4. Start the second ClientApp container:

    $ docker run --rm \
    --network flwr-network \
    --detach \
    flwr_clientapp:0.0.1 \
    --supernode supernode-2:9095

Step 5: Start the SuperExec#

The procedure for building and running a SuperExec image is almost identical to the ClientApp image.

Similar to the ClientApp image, you will need to create a Dockerfile that extends the SuperExec image and installs the required FAB dependencies.

  1. Create a SuperExec Dockerfile called Dockerfile.superexec and paste the following code in:

    Dockerfile.superexec#
    FROM flwr/superexec:1.11.1

WORKDIR /app

COPY pyproject.toml .
RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \
   && python -m pip install -U --no-cache-dir .

ENTRYPOINT ["flower-superexec", "--executor", "flwr.superexec.deployment:executor"]
    Understand the Dockerfile
    • FROM flwr/superexec:1.11.1: This line specifies that the Docker image
      to be built from is the flwr/superexec image, version 1.11.1.
    • WORKDIR /app: Set the working directory for the container to /app.
      Any subsequent commands that reference a directory will be relative to this directory.
    • COPY pyproject.toml .: Copy the pyproject.toml file
      from the current working directory into the container’s /app directory.
    • RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml: Remove the flwr dependency
      from the pyproject.toml.
    • python -m pip install -U --no-cache-dir .: Run the pip install command to
      install the dependencies defined in the pyproject.toml file

      The -U flag indicates that any existing packages should be upgraded, and
      --no-cache-dir prevents pip from using the cache to speed up the installation.
    • ENTRYPOINT ["flower-superexec": Set the command flower-superexec to be
      the default command run when the container is started.

      "--executor", "flwr.superexec.deployment:executor"] Use the
      flwr.superexec.deployment:executor executor to run the ServerApps.

  2. Afterward, in the directory that holds the Dockerfile, execute this Docker command to build the SuperExec image:

    $ docker build -f Dockerfile.superexec -t flwr_superexec:0.0.1 .

  3. Start the SuperExec container:

    $ docker run --rm \
   -p 9093:9093 \
    --network flwr-network \
    --name superexec \
    --detach \
    flwr_superexec:0.0.1 \
    --insecure \
    --executor-config superlink=\"superlink:9091\"
    Understand the command

    • docker run: This tells Docker to run a container from an image.

    • --rm: Remove the container once it is stopped or the command exits.

    • -p 9093:9093: Map port 9093 of the container to the same port of
      the host machine, allowing you to access the SuperExec API on http://localhost:9093.

    • --network flwr-network: Make the container join the network named flwr-network.

    • --name superexec: Assign the name superexec to the container.

    • --detach: Run the container in the background, freeing up the terminal.

    • flwr_superexec:0.0.1: This is the name of the image to be run and the specific tag
      of the image.
    • --insecure: This flag tells the container to operate in an insecure mode, allowing
      unencrypted communication.
    • --executor-config superlink=\"superlink:9091\": Configure the SuperExec executor to
      connect to the SuperLink running on port 9091.

Step 6: Run the Quickstart Project#

  1. Add the following lines to the pyproject.toml:

    pyproject.toml#
    [tool.flwr.federations.docker]
address = "127.0.0.1:9093"
insecure = true

  2. Run the quickstart-docker project by executing the command:

    $ flwr run . docker

  3. Follow the SuperExec logs to track the execution of the run:

    $ docker logs -f superexec

Step 7: Update the Application#

  1. Change the application code. For example, change the seed in quickstart_docker/task.py to 43 and save it:

    quickstart_docker/task.py#
    # ...
partition_train_test = partition.train_test_split(test_size=0.2, seed=43)
# ...

  2. Stop the current ClientApp containers:

    $ docker stop $(docker ps -a -q  --filter ancestor=flwr_clientapp:0.0.1)

  3. Rebuild the FAB and ClientApp image:

    $ docker build -f Dockerfile.clientapp -t flwr_clientapp:0.0.1 .

  4. Launch two new ClientApp containers based on the newly built image:

    $ docker run --rm \
    --network flwr-network \
    --detach \
    flwr_clientapp:0.0.1  \
    --supernode supernode-1:9094
$ docker run --rm \
    --network flwr-network \
    --detach \
    flwr_clientapp:0.0.1 \
    --supernode supernode-2:9095

  5. Run the updated project:

    $ flwr run . docker

Step 8: Clean Up#

Remove the containers and the bridge network:

$ docker stop $(docker ps -a -q  --filter ancestor=flwr_clientapp:0.0.1) \
   supernode-1 \
   supernode-2 \
   superexec \
   superlink
$ docker network rm flwr-network

Where to Go Next#