logging.basicConfig(
format="%(asctime)s %(levelname)-8s %(message)s",
level=logging.INFO,
datefmt="%Y-%m-%d %H:%M:%S",
)
B = 10 # 600
is_iid = True
C = 0.1
lr = 0.1
for s in range(20):
name = f"Seed value: {s}"
logging.info(name)
experiment = Experiment(workspace="federated-learning",
project_name="Reproducability seed")
experiment.set_name(name)
# TODO a paraméterek helytelen nevére nem adott hibát
config = TorchFederatedLearnerMNISTConfig(
LEARNING_RATE=lr,
IS_IID_DATA=is_iid,
BATCH_SIZE=B,
CLIENT_FRACTION=C,
N_CLIENTS=100,
N_EPOCH_PER_CLIENT=5,
MAX_ROUNDS=300,
SEED=s,
)
learner = TorchFederatedLearnerMNIST(experiment, config)
learner.train()
format="%(asctime)s %(levelname)-8s %(message)s",
level=logging.INFO,
datefmt="%Y-%m-%d %H:%M:%S",
)
C = 1
NC = 2
E = 5
B = 50
is_iid = False
dist = "IID" if is_iid else "non IID"
name = f"torch - {dist} - glorot"
logging.info(name)
experiment = Experiment(workspace="federated-learning",
project_name="compare-frameworks")
experiment.set_name(name)
# TODO a paraméterek helytelen nevére nem adott hibát
config = TorchFederatedLearnerMNISTConfig(
LEARNING_RATE=0.1,
IS_IID_DATA=is_iid,
BATCH_SIZE=B,
CLIENT_FRACTION=C,
N_CLIENTS=NC,
N_EPOCH_PER_CLIENT=E,
MAX_ROUNDS=1500,
)
learner = TorchFederatedLearnerMNIST(experiment, config)
learner.train()
from comet_ml import Experiment
import logging
from FLF.TorchFederatedLearnerMNIST import TorchFederatedLearnerMNIST, TorchFederatedLearnerMNISTConfig
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S')
name = "TorchClientBatchIter"
logging.info(name)
experiment = Experiment(
workspace="federated-learning", project_name="simple_runs"
)
experiment.set_name(name)
config = TorchFederatedLearnerMNISTConfig(N_CLIENTS=100, CLIENT_FRACTION=0.1, N_ROUNDS=10, BATCH_SIZE=64)
learner = TorchFederatedLearnerMNIST(experiment, config)
learner.train()
# TODO n workers for data loading is broken