#coding=utf-8
import torch
import ctypes
import dadt.pytorch as dadt
dadt.init(broad_cast_executor='nccl', all_reduce_executor='nccl')
device = torch.device('cuda:{}'.format(dadt.local_rank()))
# device = torch.device('cpu')
if 0 == dadt.rank():
x = torch.tensor([1, 2, 3, 4], device=device, dtype=torch.float)
else:
x = torch.tensor([1, 1, 1, 1], device=device, dtype=torch.float)
y = dadt.all_reduce(x, "x")
print(dadt.rank(), y)
y = dadt.all_reduce(x, "x")
print(dadt.rank(), y)
dadt.shutdown()
#coding=utf-8
import torch
import ctypes
import dadt.pytorch as dadt
dadt.init(broad_cast_executor='mpi')
device = torch.device('cuda:{}'.format(dadt.local_rank()))
if 0 == dadt.rank():
x = torch.tensor([1, 2, 3, 4], device=device)
else:
x = torch.tensor([0, 0, 0, 0], device=device)
y = dadt.broad_cast(x, "x")
print(dadt.rank(), y)
dadt.shutdown()
def train():
# init dadt
dadt.init(
cycle_duration_ms=5,
broad_cast_executor='nccl',
all_reduce_executor='nccl',
group_buffer_size=0)
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:{}".format(dadt.local_rank()))
model_ft = models.resnet101(pretrained=False)
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.002, momentum=0.9)
# init distribute optimizer
d_optimizer = dadt.DistributedOptimizer(optimizer=optimizer_ft)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft.train()
total_cost_time = 0.0
total_count = 0.0
for epoch in range(2500000):
running_loss = 0.0
running_corrects = 0
for inputs, labels in dataloaders['train']:
start_time = time.time()
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
d_optimizer.zero_grad()
outputs = model_ft(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
loss.backward()
d_optimizer.step()
cost_time = int(round((time.time() - start_time) * 1000))
total_cost_time += cost_time
total_count += 1
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
print('Rank:{}, cost time:{}, avg tiem:{} epoch:{}, loss:{}'.format(dadt.rank(), cost_time, total_cost_time/total_count, epoch, loss.item()))
print('--------------------------------------------------------------------------')
epoch_loss = running_loss / dataset_sizes['train']
epoch_acc = running_corrects.double() / dataset_sizes['train']
print('Rank:{}, {} Loss: {:.4f} Acc: {:.4f}'.format(dadt.rank(), 'train', epoch_loss, epoch_acc))
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size',
type=int,
default=64,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=1000,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=14,
metavar='N',
help='number of epochs to train (default: 14)')
parser.add_argument('--lr',
type=float,
default=1.0,
metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--gamma',
type=float,
default=0.7,
metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model',
action='store_true',
default=False,
help='For Saving the current Model')
args = parser.parse_args()
# initialize dadt
dadt.init(broad_cast_executor='nccl',
all_reduce_executor='nccl',
all_reduce_buffer_size=64 * 1024 * 1024)
torch.manual_seed(args.seed)
# get device by rank
device = torch.device("cuda:{}".format(dadt.local_rank()))
kwargs = {'batch_size': args.batch_size}
kwargs.update({'num_workers': 1, 'pin_memory': True, 'shuffle': True}, )
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
dataset1 = datasets.MNIST('./data{}'.format(dadt.local_rank()),
train=True,
download=True,
transform=transform)
dataset2 = datasets.MNIST('./data{}'.format(dadt.local_rank()),
train=False,
transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, **kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
distribte_optimizer = dadt.DistributedOptimizer(optimizer=optimizer)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, distribte_optimizer, epoch)
test(model, device, test_loader)
scheduler.step()
if args.save_model and 0 == dadt.rank():
torch.save(model.state_dict(), "mnist_cnn.pt")
# shut down background thread
dadt.shutdown()