FedCast/pyproject.toml at main · NKDataConv/FedCast · GitHub

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[tool.poetry]
name = "fedcast"
version = "0.1.1b1"
description = "A modular framework for time series forecasting using federated learning, built on top of the Flower framework"
authors = ["FedCast Team <nk@data-convolution.de>"]
license = "Apache-2.0"
readme = "README.md"
homepage = "https://github.com/NKDataConv/FedCast"
repository = "https://github.com/NKDataConv/FedCast"
documentation = "https://github.com/NKDataConv/FedCast#readme"
keywords = ["federated-learning", "time-series", "forecasting", "machine-learning", "flower", "privacy"]
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: Apache Software License",
    "Operating System :: OS Independent",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.9",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Software Development :: Libraries :: Python Modules",
]
packages = [{include = "fedcast"}]

[tool.poetry.dependencies]
python = "<=3.13.2,>=3.9.2"
flwr = {version = "^1.18.0", extras = ["simulation"]}
flwr-datasets = "^0.5.0"
pandas = "^2.2.3"
torch = "^2.3"
torchvision = "*"
datasets = "^3.1.0"
wfdb = "^4.1.2"
yfinance = "^0.2.40"
mlflow = "^2.15"

tensorflow = "^2.15"
tensorflow-metal = {version = "^1.1.0", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'"}

[tool.poetry.group.dev.dependencies]
pytest = "^8.2.2"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"