def handler(event, context):
function_name = "lambda_core"
# dataset setting
dataset_name = 'higgs'
data_bucket = "higgs-10"
dataset_type = "dense_libsvm"
n_features = 30
tmp_table_name = "tmp-params"
merged_table_name = "merged-params"
key_col = "key"
# hyper-parameters
n_clusters = 10
n_epochs = 10
threshold = 0.0001
# training setting
sync_mode = "reduce" # reduce or reduce_scatter
n_workers = 10
# clear dynamodb table
dynamo_client = dynamo_operator.get_client()
tmp_tb = DynamoTable(dynamo_client, tmp_table_name)
merged_tb = DynamoTable(dynamo_client, tmp_table_name)
tmp_tb.clear(key_col)
merged_tb.clear(key_col)
# lambda payload
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['dataset_type'] = dataset_type
payload['n_features'] = n_features
payload['tmp_table_name'] = tmp_table_name
payload['merged_table_name'] = merged_table_name
payload['key_col'] = key_col
payload['n_clusters'] = n_clusters
payload['n_epochs'] = n_epochs
payload['threshold'] = threshold
payload['sync_mode'] = sync_mode
payload['n_workers'] = n_workers
# invoke functions
lambda_client = boto3.client('lambda')
for i in range(n_workers):
payload['worker_index'] = i
payload['file'] = '{}_{}'.format(i, n_workers)
lambda_client.invoke(FunctionName=function_name,
InvocationType='Event',
Payload=json.dumps(payload))
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 == "dense_libsvm"
n_features = event['n_features']
n_classes = event['n_classes']
n_workers = event['n_workers']
worker_index = event['worker_index']
tmp_table_name = event['tmp_table_name']
merged_table_name = event['merged_table_name']
key_col = event['key_col']
# training setting
model_name = event['model']
optim = event['optim']
sync_mode = event['sync_mode']
assert model_name.lower() in MLModel.Linear_Models
assert optim.lower() == Optimization.ADMM
assert sync_mode.lower() in [
Synchronization.Reduce, Synchronization.Reduce_Scatter
]
# hyper-parameter
learning_rate = event['lr']
batch_size = event['batch_size']
n_epochs = event['n_epochs']
valid_ratio = event['valid_ratio']
n_admm_epochs = event['n_admm_epochs']
lam = event['lambda']
rho = event['rho']
print('data 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('optimization = {}'.format(optim))
print('sync mode = {}'.format(sync_mode))
s3_storage = S3Storage()
dynamo_client = dynamo_operator.get_client()
tmp_table = DynamoTable(dynamo_client, tmp_table_name)
merged_table = DynamoTable(dynamo_client, merged_table_name)
communicator = DynamoCommunicator(dynamo_client, tmp_table, merged_table,
key_col, n_workers, worker_index)
# Read file from s3
read_start = time.time()
lines = s3_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]
# Creating 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)
n_train_batch = len(train_loader)
validation_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
sampler=valid_sampler)
print("preprocess data cost {} s, dataset size = {}".format(
time.time() - preprocess_start, dataset_size))
model = linear_models.get_model(model_name, n_features, n_classes)
z, u = initialize_z_and_u(model.linear.weight.data.size())
print("size of z = {}".format(z.shape))
print("size of u = {}".format(u.shape))
# 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)
# Training the Model
train_start = time.time()
for admm_epoch in range(n_admm_epochs):
print(">>> ADMM Epoch[{}]".format(admm_epoch))
admm_epoch_start = time.time()
admm_epoch_cal_time = 0
admm_epoch_comm_time = 0
admm_epoch_test_time = 0
for epoch in range(n_epochs):
epoch_start = time.time()
epoch_loss = 0.
for batch_index, (items, labels) in enumerate(train_loader):
batch_start = time.time()
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = model(items)
classify_loss = criterion(outputs, labels)
epoch_loss += classify_loss.item()
u_z = torch.from_numpy(u) - torch.from_numpy(z)
loss = classify_loss
for name, param in model.named_parameters():
if name.split('.')[-1] == "weight":
loss += rho / 2.0 * torch.norm(param + u_z, p=2)
# loss = classify_loss + rho / 2.0 * torch.norm(torch.sum(model.linear.weight, u_z))
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
epoch_cal_time = time.time() - epoch_start
admm_epoch_cal_time += epoch_cal_time
# Test the Model
test_start = time.time()
n_test_correct = 0
n_test = 0
test_loss = 0
for items, labels in validation_loader:
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
outputs = model(items)
test_loss += criterion(outputs, labels).item()
_, predicted = torch.max(outputs.data, 1)
n_test += labels.size(0)
n_test_correct += (predicted == labels).sum()
epoch_test_time = time.time() - test_start
admm_epoch_test_time += epoch_test_time
print(
'Epoch: [%d/%d], Step: [%d/%d], Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'cal cost %.4f s, test cost %.4f s, accuracy of the model on the %d test samples: %d %%, loss = %f'
% (epoch + 1, n_epochs, batch_index + 1, n_train_batch,
time.time() - train_start, epoch_loss,
time.time() - epoch_start, epoch_cal_time, epoch_test_time,
n_test, 100. * n_test_correct / n_test, test_loss / n_test))
sync_start = time.time()
w = model.linear.weight.data.numpy()
w_shape = w.shape
b = model.linear.bias.data.numpy()
b_shape = b.shape
u_shape = u.shape
w_b = np.concatenate((w.flatten(), b.flatten()))
u_w_b = np.concatenate((u.flatten(), w_b.flatten()))
# admm does not support async
if sync_mode == "reduce":
u_w_b_merge = communicator.reduce_epoch(u_w_b, admm_epoch)
elif sync_mode == "reduce_scatter":
u_w_b_merge = communicator.reduce_scatter_epoch(u_w_b, admm_epoch)
u_mean = u_w_b_merge[:u_shape[0] *
u_shape[1]].reshape(u_shape) / float(n_workers)
w_mean = u_w_b_merge[u_shape[0] * u_shape[1]: u_shape[0] * u_shape[1] + w_shape[0] * w_shape[1]]\
.reshape(w_shape) / float(n_workers)
b_mean = u_w_b_merge[u_shape[0] * u_shape[1] + w_shape[0] * w_shape[1]:]\
.reshape(b_shape[0]) / float(n_workers)
model.linear.weight.data = torch.from_numpy(w_mean)
model.linear.bias.data = torch.from_numpy(b_mean)
admm_epoch_comm_time += time.time() - sync_start
if worker_index == 0:
delete_start = time.time()
communicator.delete_expired_epoch(admm_epoch)
admm_epoch_comm_time += time.time() - delete_start
# z, u, r, s = update_z_u(w, z, u, rho, num_workers, lam)
# stop = check_stop(ep_abs, ep_rel, r, s, dataset_size, num_features, w, z, u, rho)
# print("stop = {}".format(stop))
# z = num_workers * rho / (2 * lam + num_workers * rho) * (w + u_mean)
z = update_z(w_mean, u_mean, rho, n_workers, lam)
u = u + model.linear.weight.data.numpy() - z
print(
"ADMM Epoch[{}] finishes, cost {} s, cal cost {} s, sync cost {} s, test cost {} s"
.format(admm_epoch,
time.time() - admm_epoch_start, admm_epoch_cal_time,
admm_epoch_comm_time, admm_epoch_test_time))
# Test the Model
n_test_correct = 0
n_test = 0
test_loss = 0
for items, labels in validation_loader:
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
outputs = model(items)
test_loss += criterion(outputs, labels).item()
_, predicted = torch.max(outputs.data, 1)
n_test += labels.size(0)
n_test_correct += (predicted == labels).sum()
print(
'Train finish, time = %.4f, accuracy of the model on the %d test samples: %d %%, loss = %f'
% (time.time() - train_start, n_test, 100. * n_test_correct / n_test,
test_loss / n_test))
if worker_index == 0:
s3_storage.clear(tmp_table_name)
s3_storage.clear(merged_table_name)
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
tuner_function_name = "lambda_tuner"
trial_function_name = "lambda_trial"
function_start = time.time()
function_duration = 14 * 60
n_submit_trial = event.get('n_submit_trial', 0)
# dataset setting
dataset_name = 'higgs'
data_bucket = "higgs-10"
dataset_type = "dense_libsvm" # dense_libsvm or sparse_libsvm
n_features = 30
n_classes = 2
tmp_bucket = "tmp-params"
merged_bucket = "merged-params"
# training setting
model = "lr" # lr, svm, sparse_lr, or sparse_svm
optim = "grad_avg" # grad_avg, model_avg, or admm
sync_mode = "reduce" # async, reduce or reduce_scatter
n_workers = 10
# tuner configs
tuner_strategy = "random_search"
tuner_concurrency = 5
lr_values = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10]
lr_disc = DiscHyper("lr_discrete", lr_values)
# hyper-parameters
lr = 0.01
batch_size = 100000
n_epochs = 2
valid_ratio = .2
n_admm_epochs = 2
lam = 0.01
rho = 0.01
# clear s3 bucket
s3_client = s3_operator.get_client()
s3_operator.clear_bucket(s3_client, tmp_bucket)
s3_operator.clear_bucket(s3_client, merged_bucket)
# set dynamodb table
recorder_table_name = "recoder"
dynamo_client = dynamo_operator.get_client()
recorder_tb = DynamoTable(dynamo_client, recorder_table_name)
items = recorder_tb.list()
print("{} items in the recorder".format(len(items)))
# lambda payload
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['dataset_type'] = dataset_type
payload['n_features'] = n_features
payload['n_classes'] = n_classes
payload['n_workers'] = n_workers
payload['tmp_bucket'] = tmp_bucket
payload['merged_bucket'] = merged_bucket
payload['model'] = model
payload['optim'] = optim
payload['sync_mode'] = sync_mode
payload['lr'] = lr
payload['batch_size'] = batch_size
payload['n_epochs'] = n_epochs
payload['valid_ratio'] = valid_ratio
payload['n_admm_epochs'] = n_admm_epochs
payload['lambda'] = lam
payload['rho'] = rho
# invoke functions
lambda_client = boto3.client('lambda')
n_trial = 10
trial_counter = n_submit_trial
for i in range(n_trial):
n_recorder_items = len(recorder_tb.list())
n_running_tail = trial_counter - n_recorder_items
while n_running_tail >= tuner_concurrency:
time.sleep(1)
n_recorder_items = len(recorder_tb.list())
n_running_tail = trial_counter - n_recorder_items
for j in range(n_workers):
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['dataset_type'] = dataset_type
payload['n_features'] = n_features
payload['n_classes'] = n_classes
payload['n_workers'] = n_workers
payload['tmp_bucket'] = tmp_bucket
payload['merged_bucket'] = merged_bucket
payload['model'] = model
payload['optim'] = optim
payload['sync_mode'] = sync_mode
payload['batch_size'] = batch_size
payload['n_epochs'] = n_epochs
payload['valid_ratio'] = valid_ratio
payload['n_admm_epochs'] = n_admm_epochs
payload['lambda'] = lam
payload['rho'] = rho
payload['function_name'] = trial_function_name
payload['tmp_bucket'] = tmp_bucket + "-i"
payload['merged_bucket'] = merged_bucket + "-i"
payload['lr'] = lr_disc.next() if tuner_strategy == "grid_search" else lr_disc.sample()
payload['worker_index'] = j
payload['train_file'] = 'training_{}.pt'.format(j)
payload['test_file'] = 'test.pt'
lambda_client.invoke(FunctionName=trial_function_name,
InvocationType='Event',
Payload=json.dumps(payload))
trial_counter += 1
if time.time() - function_start > function_duration:
# revoke itself
print("Invoking the next round of tuner functions, total trials {}, submitted trials {}"
.format(n_trial, trial_counter))
lambda_client = boto3.client('lambda')
payload = {
'n_submit_trial': n_submit_trial
}
lambda_client.invoke(FunctionName=tuner_function_name,
InvocationType='Event',
Payload=json.dumps(payload))
def handler(event, context):
# dataset
data_bucket = event['data_bucket']
file = event['file']
dataset_type = event["dataset_type"]
assert dataset_type == "dense_libsvm"
n_features = event['n_features']
n_workers = event["n_workers"]
worker_index = event['worker_index']
tmp_table_name = event['tmp_table_name']
merged_table_name = event['merged_table_name']
key_col = event['key_col']
# hyper-parameter
n_clusters = event['n_clusters']
n_epochs = event["n_epochs"]
threshold = event["threshold"]
sync_mode = event["sync_mode"]
assert sync_mode.lower() in [
Synchronization.Reduce, Synchronization.Reduce_Scatter
]
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('sync mode = {}'.format(sync_mode))
s3_storage = S3Storage()
dynamo_client = dynamo_operator.get_client()
tmp_table = DynamoTable(dynamo_client, tmp_table_name)
merged_table = DynamoTable(dynamo_client, merged_table_name)
communicator = DynamoCommunicator(dynamo_client, tmp_table, merged_table,
key_col, n_workers, worker_index)
# Reading data from S3
read_start = time.time()
lines = s3_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).ins_np
data_type = dataset.dtype
centroid_shape = (n_clusters, dataset.shape[1])
print("parse data cost {} s".format(time.time() - parse_start))
print("dataset type: {}, dtype: {}, Centroids shape: {}, num_features: {}".
format(dataset_type, data_type, centroid_shape, n_features))
init_centroids_start = time.time()
if worker_index == 0:
centroids = dataset[0:n_clusters]
merged_table.save(centroids.tobytes(), Prefix.KMeans_Init_Cent + "-1",
key_col)
else:
centroid_bytes = (merged_table.load_or_wait(
Prefix.KMeans_Init_Cent + "-1", key_col, 0.1))['value'].value
centroids = centroid_bytes2np(centroid_bytes, n_clusters, data_type)
if centroid_shape != centroids.shape:
raise Exception("The shape of centroids does not match.")
print("initialize centroids takes {} s".format(time.time() -
init_centroids_start))
model = cluster_models.get_model(dataset, centroids, dataset_type,
n_features, n_clusters)
train_start = time.time()
for epoch in range(n_epochs):
epoch_start = time.time()
# rearrange data points
model.find_nearest_cluster()
local_cent = model.get_centroids("numpy").reshape(-1)
local_cent_error = np.concatenate(
(local_cent.flatten(), np.array([model.error], dtype=np.float32)))
epoch_cal_time = time.time() - epoch_start
# sync local centroids and error
epoch_comm_start = time.time()
if sync_mode == "reduce":
cent_error_merge = communicator.reduce_epoch(
local_cent_error, epoch)
elif sync_mode == "reduce_scatter":
cent_error_merge = communicator.reduce_scatter_epoch(
local_cent_error, epoch)
cent_merge = cent_error_merge[:-1].reshape(centroid_shape) / float(
n_workers)
error_merge = cent_error_merge[-1] / float(n_workers)
model.centroids = cent_merge
model.error = error_merge
epoch_comm_time = time.time() - epoch_comm_start
print("one {} round cost {} s".format(sync_mode, epoch_comm_time))
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
if worker_index == 0:
tmp_table.clear(key_col)
merged_table.clear(key_col)
print("Worker[{}] finishes training: Error = {}, cost {} s".format(
worker_index, model.error,
time.time() - train_start))
return
def handler(event, context):
function_name = "lambda_core"
# dataset setting
dataset_name = 'cifar10'
data_bucket = "cifar10dataset"
n_features = 32 * 32
n_classes = 10
tmp_table_name = "tmp-params"
merged_table_name = "merged-params"
cp_bucket = "cp-model"
key_col = "key"
# training setting
model = "mobilenet" # mobilenet or resnet
optim = "grad_avg" # grad_avg or model_avg
sync_mode = "reduce" # async, reduce or reduce_scatter
n_workers = 10
# hyper-parameters
lr = 0.01
batch_size = 256
n_epochs = 5
start_epoch = 0
run_epochs = 3
# clear dynamodb table
s3_storage = S3Storage()
s3_storage.clear(cp_bucket)
dynamo_client = dynamo_operator.get_client()
tmp_tb = DynamoTable(dynamo_client, tmp_table_name)
merged_tb = DynamoTable(dynamo_client, tmp_table_name)
tmp_tb.clear(key_col)
merged_tb.clear(key_col)
# lambda payload
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['n_features'] = n_features
payload['n_classes'] = n_classes
payload['n_workers'] = n_workers
payload['tmp_table_name'] = tmp_table_name
payload['merged_table_name'] = merged_table_name
payload['key_col'] = key_col
payload['cp_bucket'] = cp_bucket
payload['model'] = model
payload['optim'] = optim
payload['sync_mode'] = sync_mode
payload['lr'] = lr
payload['batch_size'] = batch_size
payload['n_epochs'] = n_epochs
payload['start_epoch'] = start_epoch
payload['run_epochs'] = run_epochs
payload['function_name'] = function_name
# invoke functions
lambda_client = boto3.client('lambda')
for i in range(n_workers):
payload['worker_index'] = i
payload['train_file'] = 'training_{}.pt'.format(i)
payload['test_file'] = 'test.pt'
lambda_client.invoke(FunctionName=function_name,
InvocationType='Event',
Payload=json.dumps(payload))
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']
tmp_table_name = event['tmp_table_name']
merged_table_name = event['merged_table_name']
key_col = event['key_col']
cp_bucket = event['cp_bucket']
# 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, Optimization.Model_Avg]
assert sync_mode.lower() in Synchronization.All
# 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("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))
s3_storage = S3Storage()
dynamo_client = dynamo_operator.get_client()
tmp_table = DynamoTable(dynamo_client, tmp_table_name)
merged_table = DynamoTable(dynamo_client, merged_table_name)
communicator = DynamoCommunicator(dynamo_client, tmp_table, merged_table,
key_col, n_workers, worker_index)
# download file from s3
local_dir = "/tmp/"
read_start = time.time()
s3_storage.download(data_bucket, train_file,
os.path.join(local_dir, train_file))
s3_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)
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'
net = deep_models.get_models(model_name).to(device)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
optimizer = torch.optim.SGD(net.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)
s3_storage.download(cp_bucket, checked_file,
os.path.join(local_dir, checked_file))
checkpoint_model = torch.load(os.path.join(local_dir, checked_file))
net.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))
for epoch in range(start_epoch, min(start_epoch + run_epochs, n_epochs)):
train_loss, train_acc = train_one_epoch(epoch, net, train_loader,
optimizer, worker_index,
communicator, optim, sync_mode)
test_loss, test_acc = test(epoch, net, 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),
)
if worker_index == 0:
tmp_table.clear()
merged_table.clear()
# training is not finished yet, invoke next round
if epoch < n_epochs - 1:
checkpoint_model = {
'epoch': epoch,
'model_state_dict': net.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))
s3_storage.upload_file(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'],
'tmp_table_name': event['tmp_table_name'],
'merged_table_name': event['merged_table_name'],
'key_col': event['key_col'],
'cp_bucket': event['cp_bucket'],
'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):
function_name = "lambda_core"
# dataset setting
dataset_name = 'higgs'
data_bucket = "higgs-10"
dataset_type = "dense_libsvm" # dense_libsvm
n_features = 30
n_classes = 2
tmp_table_name = "tmp-params"
merged_table_name = "merged-params"
key_col = "key"
# training setting
model = "lr" # lr, svm
optim = "grad_avg" # grad_avg, model_avg, or admm
sync_mode = "reduce" # async, reduce or reduce_scatter
n_workers = 10
# hyper-parameters
lr = 0.01
batch_size = 100000
n_epochs = 2
valid_ratio = .2
n_admm_epochs = 2
lam = 0.01
rho = 0.01
# clear dynamodb table
dynamo_client = dynamo_operator.get_client()
tmp_tb = DynamoTable(dynamo_client, tmp_table_name)
merged_tb = DynamoTable(dynamo_client, tmp_table_name)
tmp_tb.clear(key_col)
merged_tb.clear(key_col)
# lambda payload
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['dataset_type'] = dataset_type
payload['n_features'] = n_features
payload['n_classes'] = n_classes
payload['n_workers'] = n_workers
payload['tmp_table_name'] = tmp_table_name
payload['merged_table_name'] = merged_table_name
payload['key_col'] = key_col
payload['model'] = model
payload['optim'] = optim
payload['sync_mode'] = sync_mode
payload['lr'] = lr
payload['batch_size'] = batch_size
payload['n_epochs'] = n_epochs
payload['valid_ratio'] = valid_ratio
payload['n_admm_epochs'] = n_admm_epochs
payload['lambda'] = lam
payload['rho'] = rho
# invoke functions
lambda_client = boto3.client('lambda')
for i in range(n_workers):
payload['worker_index'] = i
payload['file'] = '{}_{}'.format(i, n_workers)
lambda_client.invoke(FunctionName=function_name,
InvocationType='Event',
Payload=json.dumps(payload))
def handler(event, context):
tuner_function_name = "lambda_tuner"
trial_function_name = "lambda_trial"
function_start = time.time()
function_duration = 14 * 60
n_submit_trial = event.get('n_submit_trial', 0)
# dataset setting
dataset_name = 'cifar10'
data_bucket = "cifar10dataset"
n_features = 32 * 32
n_classes = 10
host = "127.0.0.1"
port = 11211
tmp_bucket = "tmp-params"
merged_bucket = "merged-params"
cp_bucket = "cp-model"
# training setting
model = "mobilenet" # mobilenet or resnet
optim = "grad_avg" # grad_avg or model_avg
sync_mode = "reduce" # async, reduce or reduce_scatter
n_workers = 10
# tuner configs
tuner_strategy = "random_search"
tuner_concurrency = 5
lr_values = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10]
lr_disc = DiscHyper("lr_discrete", lr_values)
# hyper-parameters
batch_size = 256
n_epochs = 5
start_epoch = 0
run_epochs = 3
# set dynamodb table
recorder_table_name = "recoder"
dynamo_client = dynamo_operator.get_client()
recorder_tb = DynamoTable(dynamo_client, recorder_table_name)
items = recorder_tb.list()
print("{} items in the recorder".format(len(items)))
# lambda payload
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['n_features'] = n_features
payload['n_classes'] = n_classes
payload['n_workers'] = n_workers
payload['host'] = host
payload['port'] = port
payload['tmp_bucket'] = tmp_bucket
payload['merged_bucket'] = merged_bucket
payload['cp_bucket'] = cp_bucket
payload['model'] = model
payload['optim'] = optim
payload['sync_mode'] = sync_mode
payload['lr'] = lr
payload['batch_size'] = batch_size
payload['n_epochs'] = n_epochs
payload['start_epoch'] = start_epoch
payload['run_epochs'] = run_epochs
payload['function_name'] = function_name
# invoke functions
lambda_client = boto3.client('lambda')
for i in range(n_workers):
payload['worker_index'] = i
payload['train_file'] = 'training_{}.pt'.format(i)
payload['test_file'] = 'test.pt'
lambda_client.invoke(FunctionName=function_name,
InvocationType='Event',
Payload=json.dumps(payload))
# invoke functions
lambda_client = boto3.client('lambda')
n_trial = 10
trial_counter = n_submit_trial
for i in range(n_trial):
n_recorder_items = len(recorder_tb.list())
n_running_tail = trial_counter - n_recorder_items
while n_running_tail >= tuner_concurrency:
time.sleep(1)
n_recorder_items = len(recorder_tb.list())
n_running_tail = trial_counter - n_recorder_items
for j in range(n_workers):
# lambda payload
payload = dict()
payload['dataset'] = dataset_name
payload['data_bucket'] = data_bucket
payload['n_features'] = n_features
payload['n_classes'] = n_classes
payload['n_workers'] = n_workers
payload['host'] = host
payload['port'] = port
payload['model'] = model
payload['optim'] = optim
payload['sync_mode'] = sync_mode
payload['lr'] = lr
payload['batch_size'] = batch_size
payload['n_epochs'] = n_epochs
payload['start_epoch'] = start_epoch
payload['run_epochs'] = run_epochs
payload['function_name'] = function_name
payload['tmp_bucket'] = tmp_bucket + "-i"
payload['merged_bucket'] = merged_bucket + "-i"
payload['cp_bucket'] = cp_bucket + "-i"
payload['lr'] = lr_disc.next(
) if tuner_strategy == "grid_search" else lr_disc.sample()
payload['worker_index'] = j
payload['train_file'] = 'training_{}.pt'.format(j)
payload['test_file'] = 'test.pt'
lambda_client.invoke(FunctionName=trial_function_name,
InvocationType='Event',
Payload=json.dumps(payload))
trial_counter += 1
if time.time() - function_start > function_duration:
# revoke itself
print(
"Invoking the next round of tuner functions, total trials {}, submitted trials {}"
.format(n_trial, trial_counter))
lambda_client = boto3.client('lambda')
payload = {'n_submit_trial': n_submit_trial}
lambda_client.invoke(FunctionName=tuner_function_name,
InvocationType='Event',
Payload=json.dumps(payload))