Quickstart fastai

In this federated learning tutorial we will learn how to train a SqueezeNet model on MNIST using Flower and fastai. It is recommended to create a virtual environment and run everything within a virtualenv.

Then, clone the code example directly from GitHub:

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

This will create a new directory called quickstart-fastai containing the following files:

quickstart-fastai
├── fastai_example
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
└── README.md

Next, activate your environment, then run:

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

# Install project and dependencies
$ pip install -e .

This example by default runs the Flower Simulation Engine, creating a federation of 10 nodes using FedAvg as the aggregation strategy. The dataset will be partitioned using Flower Dataset’s IidPartitioner. Let’s run the project:

# Run with default arguments
$ flwr run .

With default arguments you will see an output like this one:

Loading project configuration...
Success
INFO :      Starting Flower ServerApp, config: num_rounds=3, no round_timeout
INFO :
INFO :      [INIT]
INFO :      Using initial global parameters provided by strategy
INFO :      Starting evaluation of initial global parameters
INFO :      Evaluation returned no results (`None`)
INFO :
INFO :      [ROUND 1]
INFO :      configure_fit: strategy sampled 5 clients (out of 10)
INFO :      aggregate_fit: received 5 results and 0 failures
WARNING :   No fit_metrics_aggregation_fn provided
INFO :      configure_evaluate: strategy sampled 5 clients (out of 10)
INFO :      aggregate_evaluate: received 5 results and 0 failures
INFO :
INFO :      [ROUND 2]
INFO :      configure_fit: strategy sampled 5 clients (out of 10)
INFO :      aggregate_fit: received 5 results and 0 failures
INFO :      configure_evaluate: strategy sampled 5 clients (out of 10)
INFO :      aggregate_evaluate: received 5 results and 0 failures
INFO :
INFO :      [ROUND 3]
INFO :      configure_fit: strategy sampled 5 clients (out of 10)
INFO :      aggregate_fit: received 5 results and 0 failures
INFO :      configure_evaluate: strategy sampled 5 clients (out of 10)
INFO :      aggregate_evaluate: received 5 results and 0 failures
INFO :
INFO :      [SUMMARY]
INFO :      Run finished 3 round(s) in 143.02s
INFO :          History (loss, distributed):
INFO :                  round 1: 2.699497365951538
INFO :                  round 2: 0.9549586296081543
INFO :                  round 3: 0.6627192616462707
INFO :          History (metrics, distributed, evaluate):
INFO :          {'accuracy': [(1, 0.09766666889190674),
INFO :                        (2, 0.6948333323001862),
INFO :                        (3, 0.7721666693687439)]}
INFO :

You can also override the parameters defined in the [tool.flwr.app.config] section in pyproject.toml like this:

# Override some arguments
$ flwr run . --run-config num-server-rounds=5

참고

Check the source code of this tutorial in examples/quickstart-fasai in the Flower GitHub repository.