一、Code Sample
from __future__ import print_function
import os
import time
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
import torch.distributed as dist
def init_distributed_mode(args):
'''initilize DDP
'''
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
args.rank = int(os.environ["RANK"])
args.world_size = int(os.environ["WORLD_SIZE"])
args.gpu = int(os.environ["LOCAL_RANK"])
elif "SLURM_PROCID" in os.environ:
args.rank = int(os.environ["SLURM_PROCID"])
args.gpu = args.rank % torch.cuda.device_count()
elif hasattr(args, "rank"):
pass
else:
print("Not using distributed mode")
args.distributed = False
return
args.distributed = True
torch.cuda.set_device(args.gpu)
args.dist_backend = "nccl"
# args.dist_backend = "gloo"
print(f"| distributed init (rank {args.rank}): {args.dist_url}, local rank:{args.gpu}, world size:{args.world_size}", flush=True)
dist.init_process_group(
backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if args.distributed:
if dist.get_rank() == 0:
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, dist.get_world_size() * batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
else:
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
if args.dry_run:
break
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
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('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
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')
parser.add_argument('--local_rank', type=int, help='local rank, will passed by ddp')
parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
init_distributed_mode(args)
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs = {'batch_size': args.batch_size}
test_kwargs = {'batch_size': args.test_batch_size}
if use_cuda:
cuda_kwargs = {'num_workers': 1,
'pin_memory': True,
}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
dataset_train = datasets.MNIST('./data', train=True, download=True,
transform=transform)
dataset_val = datasets.MNIST('./data', train=False,
transform=transform)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset_train, shuffle=True)
else:
train_sampler = torch.utils.data.RandomSampler(dataset_train)
test_sampler = torch.utils.data.SequentialSampler(dataset_val)
train_loader = torch.utils.data.DataLoader(dataset_train, sampler = train_sampler, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset_val, sampler = test_sampler, **test_kwargs)
model = Net().to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
if args.distributed:
train_sampler.set_epoch(epoch)
train(args, model, device, train_loader, optimizer, epoch)
if args.distributed:
# Only run validation on GPU 0 process, for simplity, so we do not run validation on multi gpu.
if dist.get_rank() == 0:
test(model_without_ddp, device, test_loader)
else:
test(model, device, test_loader)
scheduler.step()
if args.save_model:
if args.distributed:
if dist.get_rank() == 0:
# only save model on GPU0 process.
torch.save(model.state_dict(), f"mnist_cnn.pt")
else:
torch.save(model.state_dict(), f"mnist_cnn_.pt")
if __name__ == '__main__':
start = time.time()
main()
print(f'Total cost time:{time.time() - start} ms')
二、将python文件上传的oss
如果有配置数据源nas等，也可以直接将文件上传到nas中
三、创建任务
wget https://********.oss-cn-hangzhou.aliyuncs.com/mnist-ddp.py && python -m torch.distributed.launch --master_addr=$MASTER_ADDR --master_port=$MASTER_PORT --nproc_per_node=1 --nnodes=$WORLD_SIZE --node_rank=$RANK mnist-ddp.py
四、察看运行结果
参考链接
提交分布式DLC任务
