pyproject.toml

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "federated-elsa-robotics"
version = "1.0.0"
description = ""
license = "Apache-2.0"
dependencies = [
    "flwr[simulation]>=1.15.1",
    "flwr-datasets[vision]>=0.5.0",
    "torch==2.5.1",
    "torchvision==0.20.1",
    "torchaudio",
    "matplotlib",
    "wandb",
    "tqdm",
    "moviepy==1.0.3",
    "opencv-python-headless",
]


[tool.hatch.build.targets.wheel]
packages = ["."]

[tool.flwr.app]
publisher = "santibb"

[tool.flwr.app.components]
serverapp = "federated_elsa_robotics.server_app:app"
clientapp = "federated_elsa_robotics.client_app:app"

[tool.flwr.app.config]
# Number of rounds in the federated learning process (server epochs)
num-server-rounds = 100

# Number of local epochs on each client
local-epochs = 50

# Fraction of clients that participate in each round (defines the subset from num-supernodes)
fraction-fit = 0.05

# Fraction of clients that are evaluated in each round
fraction-eval = 0.0025

# server-device = "cpu"
server-device = "cuda:0"
use-wandb = true

# DatasetOptions
dataset-config-path="dataset_config.yaml"

# Task to be learned by the model
dataset-task= "close_box"

# Samples per client
train-split= 0.9

[tool.flwr.federations]
default = "local-simulation"

[tool.flwr.federations.local-simulation]
# Number of clients in the federation (a subset will be selected for each round based on the fraction-fit parameter)
options.num-supernodes = 400

# Number of CPU cores available to each client
options.backend.client-resources.num-cpus = 2
# Define the number of GPUs available to the clients (If we have 20 clients and 1 GPU, we can set this to 0.05)
options.backend.client-resources.num-gpus = 0.05