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hyperparameters.txt
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- train fl
- train-fl non-uniform is a different file as of now
dataset='mnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=False,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=True,
alpha=9e-1,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
-. Comparison of decay of lr
++++ set decay param to -0.01 when nestrov = True
dataset='fmnist',
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=50,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=1,
radius=0.6,
use_same_graphs=True,
graph='multi',
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=32,
var_theta=False,
true_eps=False,
alpha=9e-1,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
++++ mnist set decay param to -0.1
dataset='mnist',
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=50,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=1,
radius=0.6,
use_same_graphs=True,
graph='multi',
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=32,
var_theta=False,
true_eps=False,
alpha=9e-1,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
0. Comparison consensus rounds
+++ mnist svm non iid
dataset='mnist',
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=50,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=30,
radius=0.6,
use_same_graphs=True,
graph='multi',
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=2,
var_theta=False,
true_eps=False,
alpha=9e-1,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
+++ fmnist svm non iid
dataset='fmnist',
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=50,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=1,
radius=0.6,
use_same_graphs=True,
graph='multi',
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=2,
var_theta=False,
true_eps=False,
alpha=9e-1,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
1. Experiment to understand the effect of changing sigmas
Sigmas are calculated using eqn 26 inequality with different percentages of bound
Done on iid and non-iid dataset
with same or different connectivities on different layers
dataset='mnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=True,
alpha=1e-2,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
++++ fmnist for non iid and iid
dataset='fmnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=10,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=True,
alpha=1e-2,
dynamic_alpha=False,
alpha_multiplier=False,
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.5,
F_0=6.250795,
F_optim=0.753122,
eps_multiplier=1.000001,
kappa=15,
2. Check the effect of delta calculation
Settings:
- Alpha constant
- Alpha variable
- noniid
- iid
- same degree
- varying degree
3. Comparison across different delta values.
++++ mnist non iid 1
dataset='mnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=4,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# 1 from 2.5 to 2
alpha_multiplier=[1e13]*3,
topology='rgg',
delta_multiplier=0.8,
delta_or_psi = 'delta',
psi=1e-6,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ mnist iid 10
num_train=60000,
num_test=10000,
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=10,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
# graphs='topology_rgg_degree_3.2_rho_0.7500.pkl',
graphs=[
'topology_rgg_degree_2.0_rho_0.8750.pkl',
'topology_rgg_degree_3.2_rho_0.7500.pkl',
'topology_rgg_degree_4.0_rho_0.3750.pkl',
],
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=4,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# 1 from 2.5 to 2
alpha_multiplier=[1e7]*3,
topology='rgg',
delta_multiplier=0.8,
delta_or_psi = 'delta',
psi=1e-6,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ fmnist non iid 1
dataset='fmnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=4,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
alpha_multiplier=[1e12]*3,
topology='rgg',
delta_multiplier=0.99,
delta_or_psi = 'delta',
psi=1e-6,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ fmnist iid 10
dataset='fmnist',
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=10,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=4,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# 1 from 2.5 to 2
alpha_multiplier=[1e8]*3,
topology='rgg',
delta_multiplier=0.99,
delta_or_psi = 'delta',
psi=1e-6,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
4. comparison across psi values
++++ iid after fixing the consensus bug
dataset='mnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=10,
repeat=1,
rounds=2,
radius=0,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# multiplier = 1 for factor 2 and iid 10 and psi expt
alpha_multiplier=[1e1, 1e1, 1e1],
delta_or_psi='psi',
psi=1e5,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ mnist non iid
dataset='mnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# multiplier = 1 for factor 2 and iid 10 and psi expt
alpha_multiplier=[1e4, 1e4, 1e4],
delta_or_psi='psi',
psi=1e5,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ fmnist iid 10
dataset='fmnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=10,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# multiplier = 1 for factor 2 and iid 10 and psi expt
alpha_multiplier=[1e1, 1e1, 1e1],
delta_or_psi='psi',
psi=1e5,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ fmnist non-iid 1
dataset='fmnist',
num_workers=125,
num_clusters=[25, 5, 1],
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.01,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=2,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
alpha_multiplier=[1e4, 1e4, 1e4],
delta_or_psi='psi',
psi=1e5,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
5. kappa experiment for comparing the number of iterations taken vs planned
num_train=60000,
num_test=10000,
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.004,
nesterov=False,
eta=10,
decay=1e-1, # try values 0.6, 0.4, 0.2 for kappa expt
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
# graphs='topology_rgg_degree_3.2_rho_0.7500.pkl',
graphs=[
'topology_rgg_degree_2.0_rho_0.8750.pkl',
'topology_rgg_degree_3.2_rho_0.7500.pkl',
'topology_rgg_degree_4.0_rho_0.3750.pkl',
],
# radius=[0.6, 0.7, 0.9],
d2d=1.0,
factor=4,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# 1 from 2.5 to 2
alpha_multiplier=[2e6, 1e12, 1e2],
delta_or_psi = 'delta',
psi=1e-6,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,
++++ fmnist non iid 1
dataset='fmnist',
# num_workers=200,
# num_clusters=[10, 5, 2, 1],
# num_workers=500,
# num_clusters=[100, 20, 4, 1],
num_workers=125,
num_clusters=[25, 5, 1],
# for more gap among consensus
# more workers
# higher degree
uniform_clusters=True,
shuffle_workers=False,
batch_size=False,
test_batch_size=64,
epochs=25,
lr=0.006,
nesterov=False,
eta=10,
decay=1e-1,
no_cuda=False,
seed=1,
log_interval=1,
save_model=True,
stratify=True,
uniform_data=True,
shuffle_data=True,
non_iid=1,
repeat=1,
rounds=2,
radius=0.6,
use_same_graphs=True,
graph='multi',
d2d=1.0,
factor=4,
var_theta=True,
true_eps=False,
alpha=9e-1,
dynamic_alpha=True,
# multiplier = 1 for factor 2 and iid 10 and psi expt
alpha_multiplier=[1e4, 1e4, 1e4],
delta_or_psi='psi',
psi=1e-1,
topology='rgg',
delta_multiplier=0.99,
dynamic_delta=False,
omega=1.1,
F_0=0.0776,
F_optim=0,
eps_multiplier=1.0001,
kappa=1,