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ddp_example.py
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ddp_example.py
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import os
import argparse
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
def train_epoch(train_loader, optimizer, criterion, lr_scheduler, model, world_size):
model.train()
train_running_loss = 0.0
train_running_acc = 0.0
for batch_idx, (data, target) in enumerate(train_loader):
data = data.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
output = model(data)
preds = torch.max(output, 1)[1]
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_running_loss += loss.item()
train_running_acc += torch.eq(preds, target).sum().item()
lr_scheduler.step()
train_loss_value = train_running_loss/ (len(train_dataset) / world_size)
train_acc_value = train_running_acc/ (len(train_dataset) / world_size)
return train_loss_value, train_acc_value
def valid_epoch(valid_loader, criterion, model, world_size):
model.eval()
valid_running_loss = 0.0
valid_running_acc = 0.0
with torch.no_grad():
for batch_idx, (data, target) in enumerate(valid_loader):
data = data.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
outputs = model(data)
preds = torch.max(outputs, 1)[1]
loss = criterion(outputs, target)
valid_running_loss += loss.item()
valid_running_acc += torch.eq(preds, target).sum().item()
valid_loss_value = valid_running_loss/ (len(valid_dataset) / world_size)
valid_acc_value = valid_running_acc/ (len(valid_dataset) / world_size)
return valid_loss_value, valid_acc_value
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", default=0, type=int)
args = parser.parse_args()
dist.init_process_group(backend='nccl')
dist.barrier()
# rank = dist.get_rank()
world_size = dist.get_world_size()
train_transform = transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
valid_transform = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform)
valid_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=valid_transform)
train_sampler = DistributedSampler(train_dataset)
valid_sampler = DistributedSampler(valid_dataset)
train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=256,
pin_memory=False, prefetch_factor=2, num_workers=4)
valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=256,
pin_memory=False, prefetch_factor=2, num_workers=4)
if torch.cuda.is_available():
device = torch.device("cuda", args.local_rank)
else:
device = torch.device("cpu")
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18')
model.fc = nn.Sequential(nn.Linear(in_features=512, out_features=128), nn.LeakyReLU(),
nn.Dropout(0.5), nn.Linear(128, 10))
model = model.to(device)
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.05)
lr_scheduler_values = lr_scheduler.StepLR(optimizer, step_size = 30, gamma = 0.1)
criterion = nn.CrossEntropyLoss().to(device)
num_epochs = 100
for epoch in range(num_epochs):
train_sampler.set_epoch(epoch)
valid_sampler.set_epoch(epoch)
train_loss_value, train_acc_value = train_epoch(train_loader, optimizer, criterion, lr_scheduler_values, model, world_size)
valid_loss_value, valid_acc_value = valid_epoch(valid_loader, criterion, model, world_size)
print("Train_local_rank: {} Train_Epoch: {}/{} Training_Loss: {} Training_acc: {:.2f}\
".format(args.local_rank, epoch, num_epochs-1, train_loss_value, train_acc_value))
print("Valid_local_rank: {} Valid_Epoch: {}/{} Valid_Loss: {} Valid_acc: {:.2f}\
".format(args.local_rank, epoch, num_epochs-1, valid_loss_value, valid_acc_value))
print('--------------------------------')
print("finished.")