tags	dataset	framework
quickstart llm nlp sentiment	IMDB	transformers

Federated Learning with HuggingFace Transformers and Flower (Quickstart Example)

This introductory example to using 🤗Transformers with Flower. The training script closely follows the HuggingFace course, so you are encouraged to check that out for a detailed explanation of the transformer pipeline.

In this example, we will federated the training of a DistilBERT modle on the IMDB dataset. The data will be downloaded and partitioned using Flower Datasets. This example runs best when a GPU is available.

Set up the project

Clone the project

Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:

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

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

quickstart-huggingface
├── huggingface_example
│   ├── __init__.py
│   ├── 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

Install dependencies and project

Install the dependencies defined in pyproject.toml as well as the huggingface_example package.

pip install -e .

Run the Example

You can run your Flower project in both simulation and deployment mode without making changes to the code. If you are starting with Flower, we recommend you using the simulation mode as it requires fewer components to be launched manually. By default, flwr run will make use of the Simulation Engine.

Run with the Simulation Engine

Tip

This example runs faster when the ClientApps have access to a GPU. If your system has one, you can make use of it by configuring the backend.client-resources component in pyproject.toml. If you want to try running the example with GPU right away, use the local-simulation-gpu federation as shown below.

# Run with the default federation (CPU only)
flwr run .

Run the project in the local-simulation-gpu federation that gives CPU and GPU resources to each ClientApp. By default, at most 1xClientApp (using ~12 GB of VRAM) will run in parallel in each available GPU. Note you can adjust the degree of paralellism but modifying the client-resources specification.

# Run with the `local-simulation-gpu` federation
flwr run . local-simulation-gpu

You can also override some of the settings for your ClientApp and ServerApp defined in pyproject.toml. For example

flwr run --run-config num-server-rounds=5

Tip

For a more detailed walk-through check our quickstart 🤗Transformers tutorial

Run with the Deployment Engine

Note

An update to this example will show how to run this Flower project with the Deployment Engine and TLS certificates, or with Docker.