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Installation

Plato uses uv as its package manager, which is a modern, fast Python package manager that provides significant performance improvements over conda environments. To install uv, refer to its official documentation, or simply run the following commands:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

To upgrade uv, run the command:

uv self update

To start working with Plato, first clone its git repository:

git clone git@github.com:TL-System/plato.git
cd plato

You can run Plato using uv run, using one of its configuration files:

uv run plato.py -c configs/MNIST/fedavg_lenet5.toml

In order to run any of the examples, first run the following command to include all global Python packages in a local Python environment:

uv sync

In case you need extra dependencies (including differential privacy, reinforcement learning, secure multi-party computation, and the MLX framework), you should run:

uv sync --all-extras

or:

uv sync --extra mlx

where mlx is the name of the dependency group.

Each example should be run in its own directory:

cd examples/server_aggregation/fedatt
uv run fedatt.py -c fedatt_FashionMNIST_lenet5.toml

This will make sure that any additional Python packages, specified in the local pyproject.toml configuration, will be installed first.

Optional: MLX Backend for Apple Silicon

To use MLX as a backend alternative to PyTorch on Apple Silicon devices, install the MLX dependencies:

uv sync --extra mlx

See the Quick Start guide for configuration details.

Building the plato-learn PyPi Package

The plato-learn PyPi package will be automatically built and published by a GitHub action workflow every time a release is created on GitHub. To build the package manually, follow these steps:

  1. Clean previous builds (optional): 

    rm -rf dist/ build/ *.egg-info

  2. Build the package: 

    uv build

  3. Publish to PyPI: 

    uv publish

    Or if you need to specify the PyPi token explicitly: 

    uv publish --token <your-pypi-token>

The uv tool will handle all the build process using the modern, PEP 517-compliant hatchling backend specified in pyproject.toml, making it much simpler than the old python setup.py sdist bdist_wheel approach.

Uninstalling Plato

Plato can be uninstalled by simply removing the local environment, residing within the top-level directory:

rm -rf .venv

Optionally, you may also clean uv’s cache:

uv cache clean

Optionally, you can also uninstall uv itself by following the official uv documentation.