w_decay=weight_decay)
for n in range(num_w_per_cluster)
] for c in range(num_cl)]
#############
epochs = 300
period = 6
iter_ind = 1
#############
warm_up_epoch = 5
max_ind = 50000 * warm_up_epoch / (mini_batch * num_w_per_cluster * num_cl)
##############
old_nets = deepcopy(nets)
for c in range(num_cl):
for n in range(num_w_per_cluster):
ps_functions.grad_init(old_nets[c][n], x_init, y_init)
ps_functions.synch_weight(nets[c][n], ps_model)
ps_functions.warmup_lr(optimizers, num_cl, num_w_per_cluster, lr, iter_ind,
max_ind) #initialize lr for warmup phase
# Result vector
results = np.empty([1, 150])
res_ind = 0
# training
print('=======> training')
for e in tqdm(range(epochs)):
# user
i = 0
# cluster
c = 0
# period
per = 0
lr = 1e-1
momentum = 0.9
weight_decay = 1e-4
alpha = 0.45
criterions = [nn.CrossEntropyLoss() for n in range(N_w)]
optimizers = [
SGD_custom.define_optimizer(nets[n], lr, momentum, weight_decay)
for n in range(N_w)
]
avg_Optimizer = SGD_custom.define_optimizer(avg_model, lr, momentum,
weight_decay)
# initilize all weights equally
[ps_functions.synch_weight(nets[i], ps_model) for i in range(N_w)]
ps_functions.synch_weight(ps_model, avg_model)
for r in tqdm(range(runs)):
# index of the worker doing local SGD
w_index = w_index % N_w
for worker in range(N_w):
wcounter = 0
for data in trainloaders[worker]:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizers[worker].zero_grad()
preds = nets[worker](inputs)
loss = criterions[worker](preds, labels)
loss.backward()
optimizers[worker].step()
criterions = [nn.CrossEntropyLoss() for n in range(N_w)]
optimizers = [
SGD_custom.define_optimizer(nets[n], lr, momentum, weight_decay)
for n in range(N_w)
]
reserveOptimers = [
SGD_custom.define_optimizer(reserveNets[n], lr, momentum, weight_decay)
for n in range(N_w)
]
avg_Optimizer = SGD_custom.define_optimizer(avg_model, lr, momentum,
weight_decay)
# initilize all weights equally
[ps_functions.synch_weight(nets[i], ps_model) for i in range(N_w)]
[ps_functions.synch_weight(reserveNets[i], ps_model) for i in range(N_w)]
ps_functions.synch_weight(ps_model, avg_model)
runs = int(20000)
for r in tqdm(range(runs)):
# index of the worker doing local SGD
w_index = w_index % N_w
for worker in range(N_w):
for data in trainloaders[worker]:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizers[worker].zero_grad()
preds = nets[worker](inputs)
loss = criterions[worker](preds, labels)
loss.backward()
momentum,
w_decay=weight_decay)
for n in range(num_w_per_cluster)
] for c in range(num_cl)]
#############
epochs = 300
period = 6
iter_ind = 1
#############
warm_up_epoch = 5
max_ind = 50000 * warm_up_epoch / (mini_batch * num_w_per_cluster * num_cl)
##############
for c in range(num_cl):
for n in range(num_w_per_cluster):
ps_functions.synch_weight(weightsTilde[c][n], wref)
ps_functions.warmup_lr(optimizers, num_cl, num_w_per_cluster, lr, iter_ind,
max_ind) #initialize lr for warmup phase
# Result vector
results = np.empty([1, 150])
res_ind = 0
# training
print('=======> training')
for e in tqdm(range(epochs)):
# user
i = 0
# cluster
c = 0
# period
per = 0
lr = 1e-1
momentum = 0
weight_decay = 1e-4
criterions = [nn.CrossEntropyLoss() for n in range(N_w)]
optimizers = [
SGD_custom.define_optimizer(nets[n], lr, momentum, weight_decay)
for n in range(N_w)
]
avg_Optimizer = SGD_custom.define_optimizer(avg_model, lr, momentum,
weight_decay)
# initilize all weights equally
[ps_functions.synch_weight(nets[i], ps_model) for i in range(N_w)]
[ps_functions.synch_weight(netsCurrent[i], ps_model) for i in range(N_w)]
[ps_functions.synch_weight(netsOLD[i], ps_model) for i in range(N_w)]
[ps_functions.synch_weight(netsDif[i], ps_model) for i in range(N_w)]
[ps_functions.synch_weight(netsAvg[i], ps_model) for i in range(N_w)]
ps_functions.synch_weight(ps_model, avg_model)
runs = int(10000)
for r in tqdm(range(runs)): # 2
# index of the worker doing local SGD
w_index = w_index % N_w
for worker in range(N_w): #3
for data in trainloaders[worker]:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizers[worker].zero_grad()
# optimizers = [define_optimizer(nets[n], lr, momentum, w_decay=weight_decay) for n in range(num_workers)]
optimizers = [
SGD_custom2.define_optimizer(nets[n],
lr,
momentum,
w_decay=weight_decay)
for n in range(num_workers)
]
#######################
epochs = 300
warm_up_epoch = 5
max_ind = 50000 * warm_up_epoch / (mini_batch * num_workers)
iter_ind = 1
#######################
[ps_functions.synch_weight(nets[n], ps_model) for n in range(num_workers)]
#######################################
ps_functions.warmup_lr_nc(optimizers, num_workers, lr, iter_ind,
max_ind) #initialize lr for warmup phase
# Result vector
results = np.empty([1, 150])
res_ind = 0
#training
for e in tqdm(range(epochs)):
i = 0
for data in trainloader:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# index of the worker
index = i % num_workers
weight_decay = 1e-4
alpha = 0.45
criterions = [nn.CrossEntropyLoss() for n in range(N_w)]
optimizers = [
SGD_custom.define_optimizer(nets[n], lr, momentum, weight_decay)
for n in range(N_w)
]
avg_Optimizer = SGD_custom.define_optimizer(avg_model, lr, momentum,
weight_decay)
bcast_Optimizer = SGD_custom.define_optimizer(bcast_model, lr, momentum,
weight_decay)
# initilize all weights equally
[ps_functions.synch_weight(nets[i], ps_model) for i in range(N_w)]
ps_functions.synch_weight(ps_model, avg_model)
ps_functions.synch_weight(avg_model, bcast_model)
runs = int(20000)
for r in tqdm(range(runs)):
# index of the worker doing local SGD
w_index = w_index % N_w
ts = time.time()
for worker in range(N_w):
for data in trainloaders[worker]:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizers[worker].zero_grad()
preds = nets[worker](inputs)
loss = criterions[worker](preds, labels)
