def handler(event, context):
start_time = time.time()
bucket = event['data_bucket']
worker_index = event['rank']
num_workers = event['num_workers']
key = 'training_{}.pt'.format(worker_index)
print('data_bucket = {}\n worker_index:{}\n num_worker:{}\n key:{}'.format(bucket, worker_index, num_workers, key))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(constants.HOST, constants.PORT)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}"
.format(constants.HOST, constants.PORT))
# read file from s3
readS3_start = time.time()
s3.Bucket(bucket).download_file(key, os.path.join(local_dir, training_file))
s3.Bucket(bucket).download_file(test_file, os.path.join(local_dir, test_file))
print("read data cost {} s".format(time.time() - readS3_start))
# preprocess dataset
preprocess_start = time.time()
trainset = torch.load(os.path.join(local_dir, training_file))
testset = torch.load(os.path.join(local_dir, test_file))
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
print("preprocess data cost {} s".format(time.time() - preprocess_start))
device = 'cpu'
torch.manual_seed(1234)
#Model
print('==> Building model..')
# net = VGG('VGG19')
# net = ResNet18()
# net = ResNet50()
# net = PreActResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
net = MobileNet()
# net = MobileNetV2()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
# net = ShuffleNetV2(1)
# net = EfficientNetB0()
net = net.to(device)
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
# criterion = torch.nn.CrossEntropyLoss()
# optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
# register model
model_name = "dnn"
weight = [param.data.numpy() for param in net.parameters()]
weight_shape = [layer.shape for layer in weight]
weight_size = [layer.size for layer in weight]
weight_length = sum(weight_size)
ps_client.register_model(t_client, worker_index, model_name, weight_length, num_workers)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(model_name, weight_length))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(NUM_EPOCHS):
epoch_start = time.time()
net.train()
num_batch = 0
train_acc = Accuracy()
train_loss = Average()
for batch_idx, (inputs, targets) in enumerate(trainloader):
# print("------worker {} epoch {} batch {}------".format(worker_index, epoch+1, batch_idx+1))
batch_start = time.time()
# pull latest model
pull_start = time.time()
ps_client.can_pull(t_client, model_name, iter_counter, worker_index)
latest_model = ps_client.pull_model(t_client, model_name, iter_counter, worker_index)
latest_model = np.asarray(latest_model,dtype=np.float32)
pull_time = time.time() - pull_start
# update the model
offset = 0
for layer_index, param in enumerate(net.parameters()):
layer_value = latest_model[offset : offset + weight_size[layer_index]].reshape(weight_shape[layer_index])
param.data = torch.from_numpy(layer_value)
offset += weight_size[layer_index]
# Forward + Backward + Optimize
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = F.cross_entropy(outputs, targets)
optimizer.zero_grad()
loss.backward()
train_acc.update(outputs, targets)
train_loss.update(loss.item(), inputs.size(0))
# flatten and concat gradients of weight and bias
for index, param in enumerate(net.parameters()):
if index == 0:
flattened_grad = param.grad.data.numpy().flatten()
else:
flattened_grad = np.concatenate((flattened_grad, param.grad.data.numpy().flatten()))
flattened_grad = flattened_grad * -1
# push gradient to PS
push_start = time.time()
ps_client.can_push(t_client, model_name, iter_counter, worker_index)
ps_client.push_grad(t_client, model_name, flattened_grad, learning_rate, iter_counter, worker_index)
ps_client.can_pull(t_client, model_name, iter_counter+1, worker_index) # sync all workers
push_time = time.time() - push_start
iter_counter += 1
num_batch += 1
step_time = time.time() - batch_start
print("Epoch:[{}/{}], Step:[{}/{}];\n Training Loss:{}, Training accuracy:{};\n Step Time:{}, Calculation Time:{}, Communication Time:{}".format(
epoch, NUM_EPOCHS, num_batch, len(trainloader), train_loss, train_acc, step_time, step_time - (pull_time + push_time), pull_time + push_time))
# Test the Model
net.eval()
test_loss = Average()
test_acc = Accuracy()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = F.cross_entropy(outputs, targets)
test_loss.update(loss.item(), inputs.size(0))
test_acc.update(outputs, targets)
# correct = 0
# total = 0
# test_loss = 0
# for items, labels in validation_loader:
# items = Variable(items.view(-1, NUM_FEATURES))
# labels = Variable(labels)
# outputs = model(items)
# test_loss += criterion(outputs, labels).data
# _, predicted = torch.max(outputs.data, 1)
# total += labels.size(0)
# correct += (predicted == labels).sum()
print('Time = %.4f, accuracy of the model on test set: %f, loss = %f'
% (time.time() - train_start, test_acc, test_loss))
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
start_time = time.time()
bucket = event['bucket_name']
worker_index = event['rank']
num_workers = event['num_workers']
key = event['file']
host = event['host']
port = event['port']
print('bucket = {}'.format(bucket))
print('number of workers = {}'.format(num_workers))
print('worker index = {}'.format(worker_index))
print("file = {}".format(key))
print("host = {}".format(host))
print("port = {}".format(port))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(host, port)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
host, port))
# read file from s3
file = get_object(bucket, key).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - start_time))
# parse dataset
parse_start = time.time()
dataset = DenseDatasetWithLines(file, NUM_FEATURES)
print("parse data cost {} s".format(time.time() - parse_start))
# preprocess dataset
preprocess_start = time.time()
dataset_size = len(dataset)
indices = list(
range(dataset_size)) # indices for training and validation splits:
split = int(np.floor(VALIDATION_RATIO * dataset_size))
if SHUFFLE_DATASET:
np.random.seed(RANDOM_SEED)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=BATCH_SIZE,
sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset,
batch_size=BATCH_SIZE,
sampler=valid_sampler)
print("preprocess data cost {} s".format(time.time() - preprocess_start))
model = LogisticRegression(NUM_FEATURES, NUM_CLASSES)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
# register model
model_name = "w.b"
weight_shape = model.linear.weight.data.numpy().shape
weight_length = weight_shape[0] * weight_shape[1]
bias_shape = model.linear.bias.data.numpy().shape
bias_length = bias_shape[0]
model_length = weight_length + bias_length
ps_client.register_model(t_client, worker_index, model_name, model_length,
num_workers)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(
model_name, model_length))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(NUM_EPOCHS):
epoch_start = time.time()
for batch_index, (items, labels) in enumerate(train_loader):
print("------worker {} epoch {} batch {}------".format(
worker_index, epoch, batch_index))
batch_start = time.time()
# pull latest model
ps_client.can_pull(t_client, model_name, iter_counter,
worker_index)
latest_model = ps_client.pull_model(t_client, model_name,
iter_counter, worker_index)
model.linear.weight = Parameter(
torch.from_numpy(
np.asarray(latest_model[:weight_length],
dtype=np.double).reshape(weight_shape)))
model.linear.bias = Parameter(
torch.from_numpy(
np.asarray(latest_model[weight_length:],
dtype=np.double).reshape(bias_shape[0])))
items = Variable(items.view(-1, NUM_FEATURES))
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = model(items.double())
loss = criterion(outputs, labels)
loss.backward()
# flatten and concat gradients of weight and bias
w_b_grad = np.concatenate(
(model.linear.weight.grad.data.numpy().flatten(),
model.linear.bias.grad.data.numpy().flatten()))
cal_time = time.time() - batch_start
# push gradient to PS
sync_start = time.time()
ps_client.can_push(t_client, model_name, iter_counter,
worker_index)
ps_client.push_grad(t_client, model_name, w_b_grad, LEARNING_RATE,
iter_counter, worker_index)
ps_client.can_pull(t_client, model_name, iter_counter + 1,
worker_index) # sync all workers
sync_time = time.time() - sync_start
print(
'Epoch: [%d/%d], Step: [%d/%d] >>> Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'batch cost %.4f s: cal cost %.4f s and communication cost %.4f s'
% (epoch + 1, NUM_EPOCHS, batch_index + 1,
len(train_indices) / BATCH_SIZE, time.time() - train_start,
loss.data, time.time() - epoch_start,
time.time() - batch_start, cal_time, sync_time))
iter_counter += 1
# Test the Model
correct = 0
total = 0
test_loss = 0
for items, labels in validation_loader:
items = Variable(items.view(-1, NUM_FEATURES))
labels = Variable(labels)
outputs = model(items)
test_loss += criterion(outputs, labels).data
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print(
'Time = %.4f, accuracy of the model on the %d test samples: %d %%, loss = %f'
% (time.time() - train_start, len(val_indices),
100 * correct / total, test_loss))
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
# dataset
data_bucket = event['data_bucket']
file = event['file']
dataset_type = event["dataset_type"]
assert dataset_type == "sparse_libsvm"
n_features = event['n_features']
# ps setting
host = event['host']
port = event['port']
# hyper-parameter
n_clusters = event['n_clusters']
n_epochs = event["n_epochs"]
threshold = event["threshold"]
sync_mode = event["sync_mode"]
n_workers = event["n_workers"]
worker_index = event['worker_index']
assert sync_mode.lower() == Synchronization.Reduce
print('data bucket = {}'.format(data_bucket))
print("file = {}".format(file))
print('number of workers = {}'.format(n_workers))
print('worker index = {}'.format(worker_index))
print('num clusters = {}'.format(n_clusters))
print('host = {}'.format(host))
print('port = {}'.format(port))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(host, port)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(host, port))
# Reading data from S3
read_start = time.time()
storage = S3Storage()
lines = storage.load(file, data_bucket).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - read_start))
parse_start = time.time()
dataset = libsvm_dataset.from_lines(lines, n_features, dataset_type)
train_set = dataset.ins_list
np_dtype = train_set[0].to_dense().numpy().dtype
centroid_shape = (n_clusters, n_features)
print("parse data cost {} s".format(time.time() - parse_start))
print("dataset type: {}, data type: {}, centroids shape: {}"
.format(dataset_type, np_dtype, centroid_shape))
# register model
model_name = Prefix.KMeans_Cent
model_length = centroid_shape[0] * centroid_shape[1] + 1
ps_client.register_model(t_client, worker_index, model_name, model_length, n_workers)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(model_name, model_length))
init_centroids_start = time.time()
ps_client.can_pull(t_client, model_name, 0, worker_index)
ps_model = ps_client.pull_model(t_client, model_name, 0, worker_index)
if worker_index == 0:
centroids_np = sparse_centroid_to_numpy(train_set[0:n_clusters], n_clusters)
ps_client.can_push(t_client, model_name, 0, worker_index)
ps_client.push_grad(t_client, model_name,
np.append(centroids_np.flatten(), 1000.).astype(np.double) - np.asarray(ps_model).astype(np.double),
1., 0, worker_index)
else:
centroids_np = np.zeros(centroid_shape)
ps_client.can_push(t_client, model_name, 0, worker_index)
ps_client.push_grad(t_client, model_name,
np.append(centroids_np.flatten(), 0).astype(np.double),
0, 0, worker_index)
ps_client.can_pull(t_client, model_name, 1, worker_index)
ps_model = ps_client.pull_model(t_client, model_name, 1, worker_index)
cur_centroids = np.array(ps_model[0:-1]).astype(np.float32).reshape(centroid_shape)
cur_error = float(ps_model[-1])
print("initial centroids cost {} s".format(time.time() - init_centroids_start))
model = cluster_models.get_model(train_set, torch.from_numpy(cur_centroids), dataset_type,
n_features, n_clusters)
train_start = time.time()
for epoch in range(1, n_epochs + 1):
epoch_start = time.time()
# local computation
model.find_nearest_cluster()
local_cent = model.get_centroids("numpy").reshape(-1)
local_cent_error = np.concatenate((local_cent.astype(np.double).flatten(),
np.array([model.error], dtype=np.double)))
epoch_cal_time = time.time() - epoch_start
# push updates
epoch_comm_start = time.time()
last_cent_error = np.concatenate((cur_centroids.astype(np.double).flatten(),
np.array([cur_error], dtype=np.double)))
ps_model_inc = local_cent_error - last_cent_error
ps_client.can_push(t_client, model_name, epoch, worker_index)
ps_client.push_grad(t_client, model_name,
ps_model_inc, 1. / n_workers, epoch, worker_index)
# pull new model
ps_client.can_pull(t_client, model_name, epoch + 1, worker_index) # sync all workers
ps_model = ps_client.pull_model(t_client, model_name, epoch + 1, worker_index)
model.centroids = [torch.from_numpy(c).reshape(1, n_features).to_sparse()
for c in np.array(ps_model[0:-1]).astype(np.float32).reshape(centroid_shape)]
model.error = float(ps_model[-1])
cur_centroids = model.get_centroids("numpy")
cur_error = model.error
epoch_comm_time = time.time() - epoch_comm_start
print("Epoch[{}] Worker[{}], error = {}, cost {} s, cal cost {} s, sync cost {} s"
.format(epoch, worker_index, model.error,
time.time() - epoch_start, epoch_cal_time, epoch_comm_time))
if model.error < threshold:
break
print("Worker[{}] finishes training: Error = {}, cost {} s"
.format(worker_index, model.error, time.time() - train_start))
return
def handler(event, context):
start_time = time.time()
# dataset setting
train_file = event['train_file']
test_file = event['test_file']
data_bucket = event['data_bucket']
n_features = event['n_features']
n_classes = event['n_classes']
n_workers = event['n_workers']
worker_index = event['worker_index']
cp_bucket = event['cp_bucket']
# ps setting
host = event['host']
port = event['port']
# training setting
model_name = event['model']
optim = event['optim']
sync_mode = event['sync_mode']
assert model_name.lower() in MLModel.Deep_Models
assert optim.lower() in Optimization.Grad_Avg
assert sync_mode.lower() in Synchronization.Reduce
# hyper-parameter
learning_rate = event['lr']
batch_size = event['batch_size']
n_epochs = event['n_epochs']
start_epoch = event['start_epoch']
run_epochs = event['run_epochs']
function_name = event['function_name']
print('data bucket = {}'.format(data_bucket))
print("train file = {}".format(train_file))
print("test file = {}".format(test_file))
print('number of workers = {}'.format(n_workers))
print('worker index = {}'.format(worker_index))
print('model = {}'.format(model_name))
print('optimization = {}'.format(optim))
print('sync mode = {}'.format(sync_mode))
print('start epoch = {}'.format(start_epoch))
print('run epochs = {}'.format(run_epochs))
print('host = {}'.format(host))
print('port = {}'.format(port))
print("Run function {}, round: {}/{}, epoch: {}/{} to {}/{}".format(
function_name,
int(start_epoch / run_epochs) + 1, math.ceil(n_epochs / run_epochs),
start_epoch + 1, n_epochs, start_epoch + run_epochs, n_epochs))
# download file from s3
storage = S3Storage()
local_dir = "/tmp"
read_start = time.time()
storage.download(data_bucket, train_file,
os.path.join(local_dir, train_file))
storage.download(data_bucket, test_file,
os.path.join(local_dir, test_file))
print("download file from s3 cost {} s".format(time.time() - read_start))
train_set = torch.load(os.path.join(local_dir, train_file))
test_set = torch.load(os.path.join(local_dir, test_file))
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=True)
n_train_batch = len(train_loader)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=100,
shuffle=False)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
print("read data cost {} s".format(time.time() - read_start))
random_seed = 100
torch.manual_seed(random_seed)
device = 'cpu'
model = deep_models.get_models(model_name).to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# load checkpoint model if it is not the first round
if start_epoch != 0:
checked_file = 'checkpoint_{}.pt'.format(start_epoch - 1)
storage.download(cp_bucket, checked_file,
os.path.join(local_dir, checked_file))
checkpoint_model = torch.load(os.path.join(local_dir, checked_file))
model.load_state_dict(checkpoint_model['model_state_dict'])
optimizer.load_state_dict(checkpoint_model['optimizer_state_dict'])
print("load checkpoint model at epoch {}".format(start_epoch - 1))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(host, port)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
host, port))
# register model
parameter_shape = []
parameter_length = []
model_length = 0
for param in model.parameters():
tmp_shape = 1
parameter_shape.append(param.data.numpy().shape)
for w in param.data.numpy().shape:
tmp_shape *= w
parameter_length.append(tmp_shape)
model_length += tmp_shape
ps_client.register_model(t_client, worker_index, model_name, model_length,
n_workers)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(
model_name, model_length))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(start_epoch, min(start_epoch + run_epochs, n_epochs)):
model.train()
epoch_start = time.time()
train_acc = Accuracy()
train_loss = Average()
for batch_idx, (inputs, targets) in enumerate(train_loader):
batch_start = time.time()
batch_cal_time = 0
batch_comm_time = 0
# pull latest model
ps_client.can_pull(t_client, model_name, iter_counter,
worker_index)
latest_model = ps_client.pull_model(t_client, model_name,
iter_counter, worker_index)
pos = 0
for layer_index, param in enumerate(model.parameters()):
param.data = Variable(
torch.from_numpy(
np.asarray(latest_model[pos:pos +
parameter_length[layer_index]],
dtype=np.float32).reshape(
parameter_shape[layer_index])))
pos += parameter_length[layer_index]
batch_comm_time += time.time() - batch_start
batch_cal_start = time.time()
outputs = model(inputs)
loss = F.cross_entropy(outputs, targets)
optimizer.zero_grad()
loss.backward()
# flatten and concat gradients of weight and bias
param_grad = np.zeros((1))
for param in model.parameters():
# print("shape of layer = {}".format(param.data.numpy().flatten().shape))
param_grad = np.concatenate(
(param_grad, param.data.numpy().flatten()))
param_grad = np.delete(param_grad, 0)
#print("model_length = {}".format(param_grad.shape))
batch_cal_time += time.time() - batch_cal_start
# push gradient to PS
batch_push_start = time.time()
ps_client.can_push(t_client, model_name, iter_counter,
worker_index)
ps_client.push_grad(t_client, model_name, param_grad,
-1. * learning_rate / n_workers, iter_counter,
worker_index)
ps_client.can_pull(t_client, model_name, iter_counter + 1,
worker_index) # sync all workers
batch_comm_time += time.time() - batch_push_start
train_acc.update(outputs, targets)
train_loss.update(loss.item(), inputs.size(0))
optimizer.step()
iter_counter += 1
if batch_idx % 10 == 0:
print(
'Epoch: [%d/%d], Batch: [%d/%d], Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'batch cost %.4f s: cal cost %.4f s and communication cost %.4f s'
% (epoch + 1, n_epochs, batch_idx + 1, n_train_batch,
time.time() - train_start, loss.item(),
time.time() - epoch_start, time.time() - batch_start,
batch_cal_time, batch_comm_time))
test_loss, test_acc = test(epoch, model, test_loader)
print(
'Epoch: {}/{},'.format(epoch + 1, n_epochs),
'train loss: {},'.format(train_loss),
'train acc: {},'.format(train_acc),
'test loss: {},'.format(test_loss),
'test acc: {}.'.format(test_acc),
)
# training is not finished yet, invoke next round
if epoch < n_epochs - 1:
checkpoint_model = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss.average
}
checked_file = 'checkpoint_{}.pt'.format(epoch)
if worker_index == 0:
torch.save(checkpoint_model, os.path.join(local_dir, checked_file))
storage.upload(cp_bucket, checked_file,
os.path.join(local_dir, checked_file))
print("checkpoint model at epoch {} saved!".format(epoch))
print(
"Invoking the next round of functions. round: {}/{}, start epoch: {}, run epoch: {}"
.format(
int((epoch + 1) / run_epochs) + 1,
math.ceil(n_epochs / run_epochs), epoch + 1, run_epochs))
lambda_client = boto3.client('lambda')
payload = {
'train_file': event['train_file'],
'test_file': event['test_file'],
'data_bucket': event['data_bucket'],
'n_features': event['n_features'],
'n_classes': event['n_classes'],
'n_workers': event['n_workers'],
'worker_index': event['worker_index'],
'cp_bucket': event['cp_bucket'],
'host': event['host'],
'port': event['port'],
'model': event['model'],
'optim': event['optim'],
'sync_mode': event['sync_mode'],
'lr': event['lr'],
'batch_size': event['batch_size'],
'n_epochs': event['n_epochs'],
'start_epoch': epoch + 1,
'run_epochs': event['run_epochs'],
'function_name': event['function_name']
}
lambda_client.invoke(FunctionName=function_name,
InvocationType='Event',
Payload=json.dumps(payload))
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
start_time = time.time()
worker_index = event['rank']
num_workers = event['num_workers']
host = event['host']
port = event['port']
size = event['size']
print('number of workers = {}'.format(num_workers))
print('worker index = {}'.format(worker_index))
print("host = {}".format(host))
print("port = {}".format(port))
print("size = {}".format(size))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(host, port)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
host, port))
# register model
ps_client.register_model(t_client, worker_index, MODEL_NAME, size,
num_workers)
ps_client.exist_model(t_client, MODEL_NAME)
print("register and check model >>> name = {}, length = {}".format(
MODEL_NAME, size))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(NUM_EPOCHS):
epoch_start = time.time()
for batch_index in range(NUM_BATCHES):
print("------worker {} epoch {} batch {}------".format(
worker_index, epoch, batch_index))
batch_start = time.time()
loss = 0.0
# pull latest model
ps_client.can_pull(t_client, MODEL_NAME, iter_counter,
worker_index)
pull_start = time.time()
latest_model = ps_client.pull_model(t_client, MODEL_NAME,
iter_counter, worker_index)
pull_time = time.time() - pull_start
w_b_grad = np.random.rand(1, size).astype(np.double).flatten()
# push gradient to PS
ps_client.can_push(t_client, MODEL_NAME, iter_counter,
worker_index)
push_start = time.time()
ps_client.push_grad(t_client, MODEL_NAME, w_b_grad, LEARNING_RATE,
iter_counter, worker_index)
push_time = time.time() - push_start
ps_client.can_pull(t_client, MODEL_NAME, iter_counter + 1,
worker_index) # sync all workers
print(
'Epoch: [%d/%d], Step: [%d/%d] >>> Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'batch cost %.4f s: pull model cost %.4f s, push update cost %.4f s'
% (epoch + 1, NUM_EPOCHS, batch_index, NUM_BATCHES,
time.time() - train_start, loss, time.time() - epoch_start,
time.time() - batch_start, pull_time, push_time))
iter_counter += 1
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
startTs = time.time()
num_features = event['num_features']
learning_rate = event["learning_rate"]
batch_size = event["batch_size"]
num_epochs = event["num_epochs"]
validation_ratio = event["validation_ratio"]
# Reading data from S3
bucket_name = event['bucket_name']
key = urllib.parse.unquote_plus(event['key'], encoding='utf-8')
print(f"Reading training data from bucket = {bucket_name}, key = {key}")
key_splits = key.split("_")
worker_index = int(key_splits[0])
num_worker = int(key_splits[1])
# read file from s3
file = get_object(bucket_name, key).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - startTs))
parse_start = time.time()
dataset = SparseDatasetWithLines(file, num_features)
print("parse data cost {} s".format(time.time() - parse_start))
preprocess_start = time.time()
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_ratio * dataset_size))
np.random.seed(42)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
train_set = [dataset[i] for i in train_indices]
val_set = [dataset[i] for i in val_indices]
print("preprocess data cost {} s".format(time.time() - preprocess_start))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(constants.HOST, constants.PORT)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
constants.HOST, constants.PORT))
svm = SparseSVM(train_set, val_set, num_features, num_epochs,
learning_rate, batch_size)
# register model
model_name = "w.b"
model_length = num_features
ps_client.register_model(t_client, worker_index, model_name, model_length,
num_worker)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(
model_name, model_length))
# Training the Model
train_start = time.time()
iter_counter = 0
# Training the Model
for epoch in range(num_epochs):
epoch_start = time.time()
num_batches = math.floor(len(train_set) / batch_size)
print(f"worker {worker_index} epoch {epoch}")
for batch_idx in range(num_batches):
batch_start = time.time()
# pull latest model
ps_client.can_pull(t_client, model_name, iter_counter,
worker_index)
latest_model = ps_client.pull_model(t_client, model_name,
iter_counter, worker_index)
svm.weights = torch.from_numpy(latest_model).reshape(
num_features, 1)
batch_ins, batch_label = svm.next_batch(batch_idx)
acc = svm.one_epoch(batch_idx, epoch)
compute_end = time.time()
sync_start = time.time()
w_update = svm.weights - latest_model
ps_client.can_push(t_client, model_name, iter_counter,
worker_index)
ps_client.push_update(t_client, model_name, w_update,
learning_rate, iter_counter, worker_index)
ps_client.can_pull(t_client, model_name, iter_counter + 1,
worker_index) # sync all workers
sync_time = time.time() - sync_start
print(
'Epoch: [%d/%d], Step: [%d/%d] >>> Time: %.4f, train acc: %.4f, epoch cost %.4f, '
'batch cost %.4f s: cal cost %.4f s and communication cost %.4f s'
% (epoch + 1, NUM_EPOCHS, batch_idx + 1,
len(train_indices) / batch_size, time.time() - train_start,
acc, time.time() - epoch_start, time.time() - batch_start,
compute_end - batch_start, sync_time))
iter_counter += 1
val_acc = svm.evaluate()
print("Epoch takes {}s, validation accuracy: {}".format(
time.time() - epoch_start, val_acc))
def handler(event, context):
start_time = time.time()
bucket = event['data_bucket']
worker_index = event['rank']
num_worker = event['num_workers']
key = event['key']
print('bucket = {}'.format(bucket))
print('number of workers = {}'.format(num_worker))
print('worker index = {}'.format(worker_index))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(constants.HOST, constants.PORT)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
constants.HOST, constants.PORT))
#bucket = "cifar10dataset"
print('data_bucket = {}\n worker_index:{}\n num_worker:{}\n key:{}'.format(
bucket, worker_index, num_worker, key))
# read file from s3
readS3_start = time.time()
train_path = download_file(bucket, key)
trainset = torch.load(train_path)
test_path = download_file(bucket, test_file)
testset = torch.load(test_path)
print("read data cost {} s".format(time.time() - readS3_start))
preprocess_start = time.time()
batch_size = 200
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
device = 'cpu'
print("preprocess data cost {} s".format(time.time() - preprocess_start))
model = MobileNet()
model = model.to(device)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=5e-4)
# register model
model_name = "mobilenet"
parameter_shape = []
parameter_length = []
model_length = 0
for param in model.parameters():
tmp_shape = 1
parameter_shape.append(param.data.numpy().shape)
for w in param.data.numpy().shape:
tmp_shape *= w
parameter_length.append(tmp_shape)
model_length += tmp_shape
ps_client.register_model(t_client, worker_index, model_name, model_length,
num_worker)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(
model_name, model_length))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(num_epochs):
epoch_start = time.time()
model.train()
for batch_index, (inputs, targets) in enumerate(train_loader):
print("------worker {} epoch {} batch {}------".format(
worker_index, epoch, batch_index))
batch_start = time.time()
# pull latest model
ps_client.can_pull(t_client, model_name, iter_counter,
worker_index)
latest_model = ps_client.pull_model(t_client, model_name,
iter_counter, worker_index)
pos = 0
for layer_index, param in enumerate(model.parameters()):
param.data = Variable(
torch.from_numpy(
np.asarray(latest_model[pos:pos +
parameter_length[layer_index]],
dtype=np.float32).reshape(
parameter_shape[layer_index])))
pos += parameter_length[layer_index]
# Forward + Backward + Optimize
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
# flatten and concat gradients of weight and bias
param_grad = np.zeros((1))
for param in model.parameters():
#print("shape of layer = {}".format(param.data.numpy().flatten().shape))
param_grad = np.concatenate(
(param_grad, param.data.numpy().flatten()))
param_grad = np.delete(param_grad, 0)
print("model_length = {}".format(param_grad.shape))
# push gradient to PS
sync_start = time.time()
print(
ps_client.can_push(t_client, model_name, iter_counter,
worker_index))
print(
ps_client.push_grad(t_client, model_name, param_grad,
learning_rate, iter_counter, worker_index))
print(
ps_client.can_pull(t_client, model_name, iter_counter + 1,
worker_index)) # sync all workers
sync_time = time.time() - sync_start
print(
'Epoch: [%d/%d], Step: [%d/%d] >>> Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'batch cost %.4f s: cal cost %.4f s and communication cost %.4f s'
% (epoch + 1, num_epochs, batch_index + 1,
len(train_indices) / batch_size, time.time() - train_start,
loss.data, time.time() - epoch_start,
time.time() - batch_start, cal_time, sync_time))
iter_counter += 1
test(epoch, model, test_loader, criterion, device)
optimizer.step()
def main():
start_time = time.time()
parser = argparse.ArgumentParser()
parser.add_argument('--num-workers', type=int, default=1)
parser.add_argument('--rank', type=int, default=0)
parser.add_argument('--host', type=str, default=constants.HOST)
parser.add_argument('--port', type=int, default=constants.PORT)
parser.add_argument('--size', type=int, default=100)
args = parser.parse_args()
print(args)
print("host = {}".format(args.host))
print("port = {}".format(args.port))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(args.host, args.port)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(args.host, args.port))
# register model
ps_client.register_model(t_client, args.rank, MODEL_NAME, args.size, args.num_workers)
ps_client.exist_model(t_client, MODEL_NAME)
print("register and check model >>> name = {}, length = {}".format(MODEL_NAME, args.size))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(NUM_EPOCHS):
epoch_start = time.time()
for batch_index in range(NUM_BATCHES):
print("------worker {} epoch {} batch {}------"
.format(args.rank, epoch, batch_index))
batch_start = time.time()
loss = 0.0
# pull latest model
ps_client.can_pull(t_client, MODEL_NAME, iter_counter, args.rank)
pull_start = time.time()
latest_model = ps_client.pull_model(t_client, MODEL_NAME, iter_counter, args.rank)
pull_time = time.time() - pull_start
cal_start = time.time()
w_b_grad = np.random.rand(1, args.size).astype(np.double).flatten()
cal_time = time.time() - cal_start
# push gradient to PS
ps_client.can_push(t_client, MODEL_NAME, iter_counter, args.rank)
push_start = time.time()
ps_client.push_grad(t_client, MODEL_NAME, w_b_grad, LEARNING_RATE, iter_counter, args.rank)
push_time = time.time() - push_start
ps_client.can_pull(t_client, MODEL_NAME, iter_counter + 1, args.rank) # sync all workers
print('Epoch: [%d/%d], Step: [%d/%d] >>> Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'batch cost %.4f s: cal cost %.4f s, pull model cost %.4f s, push update cost %.4f s'
% (epoch + 1, NUM_EPOCHS, batch_index, NUM_BATCHES,
time.time() - train_start, loss, time.time() - epoch_start,
time.time() - batch_start, cal_time, pull_time, push_time))
iter_counter += 1
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
start_time = time.time()
# dataset setting
file = event['file']
data_bucket = event['data_bucket']
dataset_type = event['dataset_type']
assert dataset_type == "sparse_libsvm"
n_features = event['n_features']
n_classes = event['n_classes']
n_workers = event['n_workers']
worker_index = event['worker_index']
# ps setting
host = event['host']
port = event['port']
# training setting
model_name = event['model']
optim = event['optim']
sync_mode = event['sync_mode']
assert model_name.lower() in MLModel.Sparse_Linear_Models
assert optim.lower() == Optimization.Grad_Avg
assert sync_mode.lower() == Synchronization.Reduce
# hyper-parameter
learning_rate = event['lr']
batch_size = event['batch_size']
n_epochs = event['n_epochs']
valid_ratio = event['valid_ratio']
print('bucket = {}'.format(data_bucket))
print("file = {}".format(file))
print('number of workers = {}'.format(n_workers))
print('worker index = {}'.format(worker_index))
print('model = {}'.format(model_name))
print('host = {}'.format(host))
print('port = {}'.format(port))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(host, port)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
host, port))
# Read file from s3
read_start = time.time()
storage = S3Storage()
lines = storage.load(file, data_bucket).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - read_start))
parse_start = time.time()
dataset = libsvm_dataset.from_lines(lines, n_features, dataset_type)
print("parse data cost {} s".format(time.time() - parse_start))
preprocess_start = time.time()
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(valid_ratio * dataset_size))
shuffle_dataset = True
random_seed = 100
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# split train set and test set
train_set = [dataset[i] for i in train_indices]
n_train_batch = math.floor(len(train_set) / batch_size)
val_set = [dataset[i] for i in val_indices]
print("preprocess data cost {} s, dataset size = {}".format(
time.time() - preprocess_start, dataset_size))
model = linear_models.get_sparse_model(model_name, train_set, val_set,
n_features, n_epochs, learning_rate,
batch_size)
# register model
model_name = "w.b"
weight_length = n_features
bias_length = 1
model_length = weight_length + bias_length
ps_client.register_model(t_client, worker_index, model_name, model_length,
n_workers)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(
model_name, model_length))
# Training the Model
train_start = time.time()
iter_counter = 0
for epoch in range(n_epochs):
epoch_start = time.time()
epoch_cal_time = 0
epoch_comm_time = 0
epoch_loss = 0.
for batch_idx in range(n_train_batch):
batch_start = time.time()
batch_comm_time = 0
# pull latest model
ps_client.can_pull(t_client, model_name, iter_counter,
worker_index)
latest_model = ps_client.pull_model(t_client, model_name,
iter_counter, worker_index)
model.weight = torch.from_numpy(
np.asarray(latest_model[:weight_length]).astype(
np.float32).reshape(n_features, 1))
model.bias = float(latest_model[-1])
batch_comm_time += time.time() - batch_start
batch_loss, batch_acc = model.one_batch()
epoch_loss += batch_loss.average
w_b = np.concatenate((model.weight.double().numpy().flatten(),
np.array([model.bias]).astype(np.double)))
w_b_update = np.subtract(w_b, latest_model)
batch_cal_time = time.time() - batch_start
# push gradient to PS
batch_comm_start = time.time()
ps_client.can_push(t_client, model_name, iter_counter,
worker_index)
ps_client.push_grad(t_client, model_name, w_b_update,
1.0 / n_workers, iter_counter, worker_index)
ps_client.can_pull(t_client, model_name, iter_counter + 1,
worker_index) # sync all workers
batch_comm_time += time.time() - batch_comm_start
epoch_cal_time += batch_cal_time
epoch_comm_time += batch_comm_time
if batch_idx % 10 == 0:
print(
'Epoch: [%d/%d], Batch: [%d/%d], Time: %.4f, Loss: %.4f, Accuracy: %.4f,'
'batch cost %.4f s: cal cost %.4f s and communication cost %.4f s'
% (epoch + 1, n_epochs, batch_idx + 1, n_train_batch,
time.time() - train_start, batch_loss.average,
batch_acc.accuracy, time.time() - batch_start,
batch_cal_time, batch_comm_time))
iter_counter += 1
# Test the Model
test_start = time.time()
test_loss, test_acc = model.evaluate()
test_time = time.time() - test_start
print(
"Epoch: [{}/{}] finishes, Batch: [{}/{}], Time: {:.4f}, Loss: {:.4f}, epoch cost {:.4f} s, "
"calculation cost = {:.4f} s, synchronization cost {:.4f} s, test cost {:.4f} s, "
"accuracy of the model on the {} test samples: {}, loss = {}".
format(epoch + 1, n_epochs, batch_idx + 1, n_train_batch,
time.time() - train_start, epoch_loss,
time.time() - epoch_start, epoch_cal_time, epoch_comm_time,
test_time, len(val_set), test_acc.accuracy,
test_loss.average))
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
startTs = time.time()
num_features = event['num_features']
learning_rate = event["learning_rate"]
batch_size = event["batch_size"]
num_epochs = event["num_epochs"]
validation_ratio = event["validation_ratio"]
# Reading data from S3
bucket_name = event['bucket_name']
key = urllib.parse.unquote_plus(event['key'], encoding='utf-8')
print(f"Reading training data from bucket = {bucket_name}, key = {key}")
key_splits = key.split("_")
worker_index = int(key_splits[0])
num_worker = int(key_splits[1])
# read file from s3
file = get_object(bucket_name, key).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - startTs))
parse_start = time.time()
dataset = SparseDatasetWithLines(file, num_features)
print("parse data cost {} s".format(time.time() - parse_start))
preprocess_start = time.time()
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_ratio * dataset_size))
np.random.seed(42)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
train_set = [dataset[i] for i in train_indices]
val_set = [dataset[i] for i in val_indices]
print("preprocess data cost {} s".format(time.time() - preprocess_start))
# Set thrift connection
# Make socket
transport = TSocket.TSocket(constants.HOST, constants.PORT)
# Buffering is critical. Raw sockets are very slow
transport = TTransport.TBufferedTransport(transport)
# Wrap in a protocol
protocol = TBinaryProtocol.TBinaryProtocol(transport)
# Create a client to use the protocol encoder
t_client = ParameterServer.Client(protocol)
# Connect!
transport.open()
# test thrift connection
ps_client.ping(t_client)
print("create and ping thrift server >>> HOST = {}, PORT = {}".format(
constants.HOST, constants.PORT))
# register model
model_name = "w.b"
model_length = num_features + 1
ps_client.register_model(t_client, worker_index, model_name, model_length,
num_worker)
ps_client.exist_model(t_client, model_name)
print("register and check model >>> name = {}, length = {}".format(
model_name, model_length))
# Training the Model
train_start = time.time()
iter_counter = 0
lr = LogisticRegression(train_set, val_set, num_features, num_epochs,
learning_rate, batch_size)
# Training the Model
for epoch in range(num_epochs):
epoch_start = time.time()
num_batches = math.floor(len(train_set) / batch_size)
print(f"worker {worker_index} epoch {epoch}")
for batch_idx in range(num_batches):
batch_start = time.time()
# pull latest model
ps_client.can_pull(t_client, model_name, iter_counter,
worker_index)
latest_model = ps_client.pull_model(t_client, model_name,
iter_counter, worker_index)
lr.grad = torch.from_numpy(np.asarray(latest_model[:-1])).reshape(
num_features, 1)
lr.bias = float(latest_model[-1])
compute_start = time.time()
batch_ins, batch_label = lr.next_batch(batch_idx)
batch_grad = torch.zeros(lr.n_input, 1, requires_grad=False)
batch_bias = np.float(0)
train_loss = Loss()
train_acc = Accuracy()
for i in range(len(batch_ins)):
z = lr.forward(batch_ins[i])
h = lr.sigmoid(z)
loss = lr.loss(h, batch_label[i])
# print("z= {}, h= {}, loss = {}".format(z, h, loss))
train_loss.update(loss, 1)
train_acc.update(h, batch_label[i])
g = lr.backward(batch_ins[i], h.item(), batch_label[i])
batch_grad.add_(g)
batch_bias += np.sum(h.item() - batch_label[i])
batch_grad = batch_grad.div(len(batch_ins))
batch_bias = batch_bias / len(batch_ins)
batch_grad.mul_(-1.0 * learning_rate)
lr.grad.add_(batch_grad)
lr.bias = lr.bias - batch_bias * learning_rate
np_grad = lr.grad.numpy().flatten()
w_b_grad = np.append(np_grad, lr.bias)
compute_end = time.time()
sync_start = time.time()
ps_client.can_push(t_client, model_name, iter_counter,
worker_index)
ps_client.push_grad(t_client, model_name, w_b_grad, learning_rate,
iter_counter, worker_index)
ps_client.can_pull(t_client, model_name, iter_counter + 1,
worker_index) # sync all workers
sync_time = time.time() - sync_start
print(
'Epoch: [%d/%d], Step: [%d/%d] >>> Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'batch cost %.4f s: cal cost %.4f s and communication cost %.4f s'
% (epoch + 1, NUM_EPOCHS, batch_idx + 1, len(train_indices) /
batch_size, time.time() - train_start, train_loss,
time.time() - epoch_start, time.time() - batch_start,
compute_end - batch_start, sync_time))
iter_counter += 1
val_loss, val_acc = lr.evaluate()
print(f"Validation loss: {val_loss}, validation accuracy: {val_acc}")
print(f"Epoch takes {time.time() - epoch_start}s")
