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sweeper_keypressemg_sgd_dp_no_gp.py
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import argparse
import logging
from pathlib import Path
import torch
import trainer_keypressemg_sgd_dp_no_gp
from keypressemg_utils import get_num_users
from sweep_utils import sweep
from utils import set_logger, str2bool
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Sweep SGD_DP Federated Learning Toronto Surface EMG Typing Database")
num_users = get_num_users()
##################################
# Network args #
##################################
parser.add_argument("--depth_power", type=int, default=1)
parser.add_argument("--num-classes", type=int, default=26, help="Number of unique labels")
parser.add_argument("--num-features", type=int, default=320, help="Number of extracted features (model input size)")
##################################
# Optimization args #
##################################
parser.add_argument("--num-steps", type=int, default=200)
parser.add_argument("--optimizer", type=str, default='sgd',
choices=['adam', 'sgd'], help="optimizer type")
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--inner-steps", type=int, default=10, help="number of inner steps")
parser.add_argument("--num-client-agg", type=int, default=5, help="number of clients per step")
parser.add_argument("--lr", type=float, default=1e-2, help="learning rate")
parser.add_argument("--global_lr", type=float, default=0.9, help="server learning rate")
parser.add_argument("--wd", type=float, default=1e-4, help="weight decay")
parser.add_argument("--clip", type=float, default=1.0, help="gradient clip")
parser.add_argument("--noise_multiplier", type=float, default=0.1, help="dp noise factor "
"to be multiplied by clip")
#############################
# General args #
#############################
parser.add_argument("--num-workers", type=int, default=0, help="number of workers")
parser.add_argument("--gpus", type=str, default='0', help="gpu device ID")
parser.add_argument("--exp-name", type=str, default='Sweep_GEP_public_keypressemg', help="suffix for exp name")
parser.add_argument("--save-path", type=str, default=(Path.home() / 'saved_models').as_posix(),
help="dir path for saved models")
parser.add_argument("--seed", type=int, default=52, help="seed value")
parser.add_argument('--wandb', type=str2bool, default=True)
##################################
# GEP args #
##################################
parser.add_argument("--gradients-history-size", type=int,
default=20, help="amount of past gradients participating in embedding subspace computation")
parser.add_argument("--basis-size", type=int, default=19, help="number of basis vectors")
#############################
# Dataset Args #
#############################
parser.add_argument(
"--data-name", type=str, default="keypressemg",
choices=['cifar10', 'cifar100', 'putEMG', 'keypressemg'], help="Name of the dataset"
)
parser.add_argument("--data-path", type=str,
default='./data/EMG/keypressemg/CleanData/valid_features_long_npy',
# default=(Path.cwd() / 'data/keypressemg_toronto/valid_user_features').as_posix(),
# default=(Path.home() / 'datasets/EMG/putEMG/Data-HDF5-Features-Small').as_posix(),
help="dir path for dataset")
parser.add_argument("--num-clients", type=int, default=num_users, help="total number of clients")
parser.add_argument("--num-private-clients", type=int, default=num_users, help="number of private clients")
parser.add_argument("--num_public_clients", type=int, default=0, help="number of public clients")
parser.add_argument("--classes-per-client", type=int, default=26, help="number of classes each client experience")
#############################
# General args #
#############################
parser.add_argument("--gpu", type=int, default=0, help="gpu device ID")
parser.add_argument("--eval-every", type=int, default=5, help="eval every X selected epochs")
parser.add_argument("--eval-after", type=int, default=10, help="eval only after X selected epochs")
parser.add_argument("--log-every", type=int, default=5, help="log every X selected epochs")
parser.add_argument("--log-dir", type=str, default="./log", help="dir path for logger file")
parser.add_argument("--log-name", type=str, default="sweep_keypressemg_sgd_dp", help="dir path for logger file")
parser.add_argument("--log-level", type=int, default=logging.INFO, help="logger filter")
parser.add_argument("--csv-path", type=str, default="./csv", help="dir path for csv file")
parser.add_argument("--csv-name", type=str, default="keypressemg_sgd_dp.csv", help="dir path for csv file")
args = parser.parse_args()
assert args.gpu <= torch.cuda.device_count(), f"--gpu flag should be in range [0,{torch.cuda.device_count() - 1}]"
logger = set_logger(args)
logger.info(f"Args: {args}")
sweep_configuration = {
"name": f"sgd_dp_keypressemg_{args.num_features}_{args.seed}",
"method": "grid",
"metric": {"goal": "maximize", "name": "test_avg_acc"},
"parameters": {
"lr": {"values": [0.1]},
"global_lr": {"values": [0.999, 0.5]},
"seed": {"values": [args.seed]},
"clip": {"values": [10.0, 1.0, 0.1, 0.01]},
"noise_multiplier": {"values": [0.0, 0.1, 1.0, 10.0]},
"inner_steps": {"values": [1]},
"num_steps": {"values": [100]},
"wd": {"values": [0.0001, 0.001]},
"num_client_agg": {"values": [5]},
"depth_power": {"values": [1]}
},
}
sweep(sweep_config=sweep_configuration, args=args,
train_fn=trainer_keypressemg_sgd_dp_no_gp.train)