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/flwrlabs/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 uses a local simulation profile that flwr run submits to a managed local SuperLink, which then executes the run with the Flower Simulation Runtime, 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 and stream logs $ flwr run . --stream 

Plain flwr run . submits the run, prints the run ID, and returns without streaming logs. For the full local workflow, see Run Flower Locally with a Managed SuperLink.

With default arguments you will see streamed output like this:

Starting local SuperLink on 127.0.0.1:39093... Successfully started run 1859953118041441032 INFO : Starting FedAvg strategy: INFO : ├── Number of rounds: 3 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 : [ROUND 2/3] INFO : ... INFO : [ROUND 3/3] INFO : ... INFO : Strategy execution finished in 72.84s INFO : Final results: INFO : ServerApp-side Evaluate Metrics: 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 

Tip

Check the Run simulations documentation to learn more about how to configure and run Flower simulations.

Note

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