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multigpu_demo_v3.py
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multigpu_demo_v3.py
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import os
import time
import datetime
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data.distributed
from model import pyramidnet
import argparse
parser = argparse.ArgumentParser(description='cifar10 classification models, distributed train')
parser.add_argument('--lr', default=0.1, help='')
parser.add_argument('--batch_size', type=int, default=768, help='')
parser.add_argument('--max_epochs', type=int, default=4, help='')
parser.add_argument('--num_workers', type=int, default=4, help='')
parser.add_argument("--gpu_devices", type=int, nargs='+', required=True, default=None, help="")
parser.add_argument('--init_method', default='tcp://127.0.0.1:3456', type=str, help='')
parser.add_argument('--dist-backend', default='nccl', type=str, help='')
parser.add_argument('--rank', default=0, type=int, help='')
parser.add_argument('--world_size', default=1, type=int, help='')
parser.add_argument('--distributed', action='store_true', help='')
def main():
args = parser.parse_args()
#init the process group
dist.init_process_group(backend=args.dist_backend, init_method=args.init_method,
world_size=args.world_size, rank=args.rank)
#set cuda device for use
gpu_devices = ','.join([str(id) for id in args.gpu_devices])
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_devices
print("From Rank: {}, Use GPU: {} for training".format(args.rank, gpu_devices))
print('From Rank: {}, ==> Making model..'.format(args.rank))
net = pyramidnet()
net.cuda()
args.batch_size = int(args.batch_size / args.world_size)
args.num_workers = int(args.num_workers / args.world_size)
net = torch.nn.parallel.DistributedDataParallel(net, device_ids=args.gpu_devices, output_device=args.gpu_devices[0])
num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
print('From Rank: {}, The number of parameters of model is'.format(args.rank), num_params)
print('From Rank: {}, ==> Preparing data..'.format(args.rank))
transforms_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
dataset_train = CIFAR10(root='./data', train=True, download=True,
transform=transforms_train)
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
train_loader = DataLoader(dataset_train, batch_size=args.batch_size,
shuffle=(train_sampler is None), num_workers=args.num_workers,
sampler=train_sampler)
# there are 10 classes so the dataset name is cifar-10
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr,
momentum=0.9, weight_decay=1e-4)
scheduler = lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)
for epoch in range(args.max_epochs):
train(epoch, net, criterion, optimizer, train_loader, args.rank)
scheduler.step()
# if args.rank == 0:
torch.save(net.module.state_dict(), "final_model_rank_{}.pth".format(args.rank))
print("From Rank: {}, model saved.".format(args.rank))
def train(epoch, net, criterion, optimizer, train_loader, rank):
net.train()
train_loss = 0
correct = 0
total = 0
epoch_start = time.time()
for batch_idx, (inputs, targets) in enumerate(train_loader):
start = time.time()
inputs = inputs.cuda()
targets = targets.cuda()
outputs = net(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
acc = 100 * correct / total
batch_time = time.time() - start
if batch_idx % 20 == 0:
print('From Rank: {}, Epoch:[{}][{}/{}]| loss: {:.3f} | acc: {:.3f} | batch time: {:.3f}s '.format(rank,
epoch, batch_idx, len(train_loader), train_loss/(batch_idx+1), acc, batch_time), flush=True)
elapse_time = time.time() - epoch_start
elapse_time = datetime.timedelta(seconds=elapse_time)
print("From Rank: {}, Training time {}".format(rank, elapse_time))
if __name__=='__main__':
main()