Merge branch 'master' of github.com:CSCfi/pytorch-ddp-examples

Author: mvsjober

mnist_mp.py

@@ -0,0 +1,80 @@
+from datetime import datetime
+from torch.utils.data import DataLoader
+from torchvision.datasets import MNIST
+import multiprocessing as mp
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+
+
+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(batch_size):
+    num_epochs = 100
+
+    torch.manual_seed(0)
+    verbose = True
+
+    model = ConvNet().cuda()
+
+    criterion = nn.CrossEntropyLoss().cuda()
+    optimizer = torch.optim.SGD(model.parameters(), 1e-4)
+
+    train_dataset = MNIST(root='./data', train=True,
+                          transform=transforms.ToTensor(), download=True)
+    train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size,
+                              shuffle=False, num_workers=0, pin_memory=True)
+
+    start = datetime.now()
+    for epoch in range(num_epochs):
+        tot_loss = 0
+        for i, (images, labels) in enumerate(train_loader):
+            images = images.cuda(non_blocking=True)
+            labels = labels.cuda(non_blocking=True)
+
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            tot_loss += loss.item()
+
+        if verbose:
+            print('Epoch [{}/{}], batch_size={} average loss: {:.4f}'.format(
+                epoch + 1,
+                num_epochs,
+                batch_size,
+                tot_loss / (i+1)))
+    if verbose:
+        print("Training completed in: " + str(datetime.now() - start))
+
+
+if __name__ == '__main__':
+    bs_list = [16, 32, 64, 128]
+    num_processes = 4
+
+    with mp.Pool(processes=num_processes) as pool:
+        pool.map(train, bs_list)