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 FedAvg strategy:
INFO :          ├── Number of rounds: 3
INFO :          ├── ArrayRecord (4.72 MB)
INFO :          ├── ConfigRecord (train): (empty!)
INFO :          ├── ConfigRecord (evaluate): (empty!)
INFO :          ├──> Sampling:
INFO :          │       ├──Fraction: train (0.50) | evaluate ( 1.00)
INFO :          │       ├──Minimum nodes: train (2) | evaluate (2)
INFO :          │       └──Minimum available nodes: 2
INFO :          └──> Keys in records:
INFO :                  ├── Weighted by: 'num-examples'
INFO :                  ├── ArrayRecord key: 'arrays'
INFO :                  └── ConfigRecord key: 'config'
INFO :
INFO :
INFO :      [ROUND 1/3]
INFO :      configure_train: Sampled 5 nodes (out of 10)
INFO :      aggregate_train: Received 5 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {}
INFO :      configure_evaluate: Sampled 10 nodes (out of 10)
INFO :      aggregate_evaluate: Received 10 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'eval_loss': 3.1197, 'eval_acc': 0.14874}
INFO :
INFO :      [ROUND 2/3]
INFO :      configure_train: Sampled 5 nodes (out of 10)
INFO :      aggregate_train: Received 5 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {}
INFO :      configure_evaluate: Sampled 10 nodes (out of 10)
INFO :      aggregate_evaluate: Received 10 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'eval_loss': 0.8071, 'eval_acc': 0.7488}
INFO :
INFO :      [ROUND 3/3]
INFO :      configure_train: Sampled 5 nodes (out of 10)
INFO :      aggregate_train: Received 5 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {}
INFO :      configure_evaluate: Sampled 10 nodes (out of 10)
INFO :      aggregate_evaluate: Received 10 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'eval_loss': 0.5015, 'eval_acc': 0.8547}
INFO :
INFO :      Strategy execution finished in 72.84s
INFO :
INFO :      Final results:
INFO :
INFO :          Global Arrays:
INFO :                  ArrayRecord (4.719 MB)
INFO :
INFO :          Aggregated ClientApp-side Train Metrics:
INFO :          {1: {}, 2: {}, 3: {}}
INFO :
INFO :          Aggregated ClientApp-side Evaluate Metrics:
INFO :          { 1: {'eval_acc': '1.4875e-01', 'eval_loss': '3.1197e+00'},
INFO :            2: {'eval_acc': '7.4883e-01', 'eval_loss': '8.0705e-01'},
INFO :            3: {'eval_acc': '8.5467e-01', 'eval_loss': '5.0145e-01'}}
INFO :
INFO :          ServerApp-side Evaluate Metrics:
INFO :          {}
INFO :

Saving final model to disk...

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

Note

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