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options_bck.py
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options_bck.py
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# #!/usr/bin/env python
# # -*- coding: utf-8 -*-
# # Python version: 3.6
# import argparse
# '''
# hyper-parameter
# '''
# # def args_parser():
# # parser = argparse.ArgumentParser()
# # # data arguments
# # parser.add_argument('--dataset',
# # type=str,
# # default='cifar',
# # help="name of dataset")
# # parser.add_argument('--iid',
# # type=int,
# # default=1,
# # help='whether i.i.d or not')
# # parser.add_argument('--num_users',
# # type=int,
# # default=100,
# # help="number of users: K")
# # parser.add_argument('--frac',
# # type=float,
# # default=0.1,
# # help="the fraction of clients: C")
# # parser.add_argument('--num_data',
# # type=int,
# # default=500,
# # help="number of data per user: m")
# #
# # # model arguments
# # parser.add_argument('--method',
# # type=str,
# # default='fedavg',
# # help='method name')
# # parser.add_argument('--model', type=str, default='cnn', help='model name')
# # parser.add_argument('--local_solver',
# # type=str,
# # default='local_sgd',
# # help='local solver method')
# # parser.add_argument('--global_solver',
# # type=str,
# # default='avg',
# # help="aggregation rule")
# # ################################################# need to tune tau, batch_size, local_lr
# # # local solver hyperparameter
# # parser.add_argument('--tau',
# # type=int,
# # default=10,
# # help="num. of local epochs")
# # parser.add_argument('--batch_size',
# # type=int,
# # default=50,
# # help="local batch size")
# # parser.add_argument('--local_lr',
# # type=float,
# # default=0.125,
# # help="local learning rate")
# # parser.add_argument('--local_momentum',
# # type=float,
# # default=0.8,
# # help="SGD momentum (default: 0.5)")
# # parser.add_argument('--decay_weight',
# # type=float,
# # default=0.99,
# # help="learning rate decay weight (default: 0.5)")
# #
# # # global solver hyperparameter
# # parser.add_argument('--round',
# # type=int,
# # default=200,
# # help="rounds of training")
# # parser.add_argument('--clip',
# # type=float,
# # default=1.0,
# # help='clipping threshold')
# # # other
# # parser.add_argument('--gpu',
# # type=int,
# # default=1,
# # help="GPU ID, -1 for CPU")
# # parser.add_argument('--seed',
# # type=int,
# # default=1,
# # help="seed")
# # parser.add_argument('--repeat', type=int, default=1, help='repeat index')
# # parser.add_argument('--hyper_tune',
# # type=int,
# # default=0,
# # help=" tuning hyperparameter? ")
# #
# # args = parser.parse_args()
# # return args
# '''
# well-tuned hyper-parameter for svhn
# '''
# # def args_parser():
# # parser = argparse.ArgumentParser()
# # # data arguments
# # parser.add_argument('--dataset',
# # type=str,
# # default='svhn',
# # help="name of dataset")
# # parser.add_argument('--iid',
# # type=int,
# # default=1,
# # help='whether i.i.d or not')
# # parser.add_argument('--num_users',
# # type=int,
# # default=6000,
# # help="number of users: K")
# # parser.add_argument('--frac',
# # type=float,
# # default=0.0167,
# # help="the fraction of clients: C")
# # parser.add_argument('--num_data',
# # type=int,
# # default=100,
# # help="number of data per user: m")
# #
# # # model arguments
# # parser.add_argument('--method',
# # type=str,
# # default='fedavg',
# # help='method name')
# # parser.add_argument('--model', type=str, default='cnn', help='model name')
# # parser.add_argument('--local_solver',
# # type=str,
# # default='local_sgd',
# # help='local solver method')
# # parser.add_argument('--global_solver',
# # type=str,
# # default='avg',
# # help="aggregation rule")
# #
# # # local solver hyperparameter
# # parser.add_argument('--tau',
# # type=int,
# # default=5,
# # help="num. of local epochs")
# # parser.add_argument('--batch_size',
# # type=int,
# # default=50,
# # help="local batch size")
# # parser.add_argument('--local_lr',
# # type=float,
# # default=0.05,
# # help="local learning rate")
# # parser.add_argument('--local_momentum',
# # type=float,
# # default=0.8,
# # help="SGD momentum (default: 0.5)")
# # parser.add_argument('--decay_weight',
# # type=float,
# # default=0.99,
# # help="learning rate decay weight (default: 0.5)")
# #
# # # global solver hyperparameter
# # parser.add_argument('--round',
# # type=int,
# # default=200,
# # help="rounds of training")
# # parser.add_argument('--clip',
# # type=float,
# # default=1.0,
# # help='clipping threshold')
# # # other
# # parser.add_argument('--gpu',
# # type=int,
# # default=0,
# # help="GPU ID, -1 for CPU")
# # parser.add_argument('--seed',
# # type=int,
# # default=1,
# # help="seed")
# # parser.add_argument('--repeat', type=int, default=1, help='repeat index')
# # parser.add_argument('--hyper_tune',
# # type=int,
# # default=0,
# # help=" tuning hyperparameter? ")
# #
# # args = parser.parse_args()
# # return args
# '''
# well-tuned hyper-parameter for fmnist
# '''
# # def args_parser():
# # parser = argparse.ArgumentParser()
# # # data arguments
# # parser.add_argument('--dataset',
# # type=str,
# # default='fmnist',
# # help="name of dataset")
# # parser.add_argument('--iid',
# # type=int,
# # default=1,
# # help='whether i.i.d or not')
# # parser.add_argument('--num_users',
# # type=int,
# # default=6000,
# # help="number of users: K")
# # parser.add_argument('--frac',
# # type=float,
# # default=0.0167,
# # help="the fraction of clients: C")
# # parser.add_argument('--num_data',
# # type=int,
# # default=10,
# # help="number of data per user: m")
# #
# # # model arguments
# # parser.add_argument('--method',
# # type=str,
# # default='fedavg',
# # help='method name')
# # parser.add_argument('--model', type=str, default='cnn', help='model name')
# # parser.add_argument('--local_solver',
# # type=str,
# # default='local_sgd',
# # help='local solver method')
# # parser.add_argument('--global_solver',
# # type=str,
# # default='avg',
# # help="aggregation rule")
# #
# # # local solver hyperparameter
# # parser.add_argument('--tau',
# # type=int,
# # default=10,
# # help="num. of local epochs")
# # parser.add_argument('--batch_size',
# # type=int,
# # default=10,
# # help="local batch size")
# # parser.add_argument('--local_lr',
# # type=float,
# # default=0.125,
# # help="local learning rate")
# # parser.add_argument('--local_momentum',
# # type=float,
# # default=0.5,
# # help="SGD momentum (default: 0.5)")
# # parser.add_argument('--decay_weight',
# # type=float,
# # default=0.99,
# # help="learning rate decay weight (default: 0.5)")
# #
# # # global solver hyperparameter
# # parser.add_argument('--round',
# # type=int,
# # default=180,
# # help="rounds of training")
# # parser.add_argument('--clip',
# # type=float,
# # default=1.0,
# # help='clipping threshold')
# # # other
# # parser.add_argument('--gpu',
# # type=int,
# # default=1,
# # help="GPU ID, -1 for CPU")
# # parser.add_argument('--seed',
# # type=int,
# # default=1,
# # help="seed")
# # parser.add_argument('--repeat', type=int, default=1, help='repeat index')
# # parser.add_argument('--hyper_tune',
# # type=int,
# # default=0,
# # help=" tuning hyperparameter? ")
# #
# # args = parser.parse_args()
# # return args
# '''
# well-tuned hyper-parameter for cifar
# '''
# def args_parser():
# parser = argparse.ArgumentParser()
# # data arguments
# parser.add_argument('--dataset',
# type=str,
# default='cifar',
# help="name of dataset")
# parser.add_argument('--iid',
# type=int,
# default=1,
# help='whether i.i.d or not')
# parser.add_argument('--num_users',
# type=int,
# default=100,
# help="number of users: K")
# parser.add_argument('--frac',
# type=float,
# default=0.1,
# help="the fraction of clients: C")
# parser.add_argument('--num_data',
# type=int,
# default=500,
# help="number of data per user: m")
# # model arguments
# parser.add_argument('--method',
# type=str,
# default='fedavg',
# help='method name')
# parser.add_argument('--model', type=str, default='cnn', help='model name')
# parser.add_argument('--local_solver',
# type=str,
# default='local_sgd',
# help='local solver method')
# parser.add_argument('--global_solver',
# type=str,
# default='avg',
# help="aggregation rule")
# ################################################# may need to re-tune tau, batch_size, local_lr
# # local solver hyperparameter
# parser.add_argument('--tau',
# type=int,
# default=10,
# help="num. of local epochs")
# parser.add_argument('--batch_size',
# type=int,
# default=50,
# help="local batch size")
# parser.add_argument('--local_lr',
# type=float,
# default=0.125,
# help="local learning rate")
# parser.add_argument('--local_momentum',
# type=float,
# default=0.8,
# help="SGD momentum (default: 0.5)")
# parser.add_argument('--decay_weight',
# type=float,
# default=0.99,
# help="learning rate decay weight (default: 0.5)")
# # global solver hyperparameter
# parser.add_argument('--round',
# type=int,
# default=200,
# help="rounds of training")
# parser.add_argument('--clip',
# type=float,
# default=1.0,
# help='clipping threshold')
# # other
# parser.add_argument('--gpu',
# type=int,
# default=1,
# help="GPU ID, -1 for CPU")
# parser.add_argument('--seed',
# type=int,
# default=1,
# help="seed")
# parser.add_argument('--repeat', type=int, default=1, help='repeat index')
# parser.add_argument('--hyper_tune',
# type=int,
# default=0,
# help=" tuning hyperparameter? ")
# args = parser.parse_args()
# return args
# def call_parser():
# args = args_parser()
# return args
import argparse
def args_parser():
parser = argparse.ArgumentParser()
# data arguments
parser.add_argument('--dataset',
type=str,
default='fmnist',
help="name of dataset")
parser.add_argument('--iid',
type=int,
default=1,
help='whether i.i.d or not')
parser.add_argument('--num_users',
type=int,
default=10,
help="number of users: K")
parser.add_argument('--frac',
type=float,
default=0.5,
help="the fraction of clients: C")
parser.add_argument('--num_data',
type=int,
default=500,
help="number of data per user: m")
# model arguments
parser.add_argument('--method',
type=str,
default='fedavg',
help='method name')
parser.add_argument('--model', type=str, default='cnn', help='model name')
parser.add_argument('--local_solver',
type=str,
default='local_sgd',
help='local solver method')
parser.add_argument('--global_solver',
type=str,
default='avg',
help="aggregation rule")
# local solver hyperparameter
parser.add_argument('--tau',
type=int,
default=10,
help="num. of local epochs")
parser.add_argument('--batch_size',
type=int,
default=100,
help="local batch size")
parser.add_argument('--local_lr',
type=float,
default=0.01,
help="local learning rate")
parser.add_argument('--local_momentum',
type=float,
default=0.5,
help="SGD momentum (default: 0.5)")
parser.add_argument('--decay_weight',
type=float,
default=0.99,
help="learning rate decay weight (default: 0.5)")
# global solver hyperparameter
parser.add_argument('--round',
type=int,
default=20,
help="rounds of training")
parser.add_argument('--clip',
type=float,
default=1.0,
help='clipping threshold')
# other
parser.add_argument('--gpu',
type=int,
default=-1,
help="GPU ID, -1 for CPU")
parser.add_argument('--seed',
type=int,
default=1,
help="seed")
parser.add_argument('--repeat', type=int, default=1, help='repeat index')
parser.add_argument('--hyper_tune',
type=int,
default=0,
help=" tuning hyperparameter? ")
args = parser.parse_args()
return args
def call_parser():
args = args_parser()
return args