Přidat „pages/students/2016/lukas_pokryvka/dp2021/mnist/mnist-dist.py“

pages/students/2016/lukas_pokryvka/dp2021/mnist/mnist-dist.py

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+import os
+from datetime import datetime
+import argparse
+import torch.multiprocessing as mp
+import torchvision
+import torchvision.transforms as transforms
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from apex.parallel import DistributedDataParallel as DDP
+from apex import amp
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N',
+                        help='number of data loading workers (default: 4)')
+    parser.add_argument('-g', '--gpus', default=1, type=int,
+                        help='number of gpus per node')
+    parser.add_argument('-nr', '--nr', default=0, type=int,
+                        help='ranking within the nodes')
+    parser.add_argument('--epochs', default=2, type=int, metavar='N',
+                        help='number of total epochs to run')
+    args = parser.parse_args()
+    args.world_size = args.gpus * args.nodes
+    os.environ['MASTER_ADDR'] = '147.232.47.114'
+    os.environ['MASTER_PORT'] = '8888'
+    mp.spawn(train, nprocs=args.gpus, args=(args,))
+
+
+class ConvNet(nn.Module):
+    def __init__(self, num_classes=10):
+        super(ConvNet, self).__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.fc = nn.Linear(7*7*32, num_classes)
+
+    def forward(self, x):
+        out = self.layer1(x)
+        out = self.layer2(out)
+        out = out.reshape(out.size(0), -1)
+        out = self.fc(out)
+        return out
+
+
+def train(gpu, args):
+    rank = args.nr * args.gpus + gpu
+    dist.init_process_group(backend='nccl', init_method='env://', world_size=args.world_size, rank=rank)
+    torch.manual_seed(0)
+    model = ConvNet()
+    torch.cuda.set_device(gpu)
+    model.cuda(gpu)
+    batch_size = 10
+    # define loss function (criterion) and optimizer
+    criterion = nn.CrossEntropyLoss().cuda(gpu)
+    optimizer = torch.optim.SGD(model.parameters(), 1e-4)
+    # Wrap the model
+    model = nn.parallel.DistributedDataParallel(model, device_ids=[gpu])
+    # Data loading code
+    train_dataset = torchvision.datasets.MNIST(root='./data',
+                                               train=True,
+                                               transform=transforms.ToTensor(),
+                                               download=True)
+    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
+                                                                    num_replicas=args.world_size,
+                                                                    rank=rank)
+    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                               batch_size=batch_size,
+                                               shuffle=False,
+                                               num_workers=0,
+                                               pin_memory=True,
+                                               sampler=train_sampler)
+
+    start = datetime.now()
+    total_step = len(train_loader)
+    for epoch in range(args.epochs):
+        for i, (images, labels) in enumerate(train_loader):
+            images = images.cuda(non_blocking=True)
+            labels = labels.cuda(non_blocking=True)
+            # Forward pass
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+
+            # Backward and optimize
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            if (i + 1) % 100 == 0 and gpu == 0:
+                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, args.epochs, i + 1, total_step,
+                                                                         loss.item()))
+    if gpu == 0:
+        print("Training complete in: " + str(datetime.now() - start))
+
+
+if __name__ == '__main__':
+    torch.multiprocessing.set_start_method('spawn')
+    main()