quickstart added

tutorials/get-started-day1/code/hello/hello.py

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+
+print("hello world!")

tutorials/get-started-day1/code/pytorch-cifar10-train-with-logging/model.py

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+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x

tutorials/get-started-day1/code/pytorch-cifar10-train-with-logging/train.py

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+import torch
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+from model import Net
+from azureml.core import Run
+
+
+# ADDITIONAL CODE: get AML run from the current context
+run = Run.get_context()
+
+# download CIFAR 10 data
+trainset = torchvision.datasets.CIFAR10(
+    root='./data',
+    train=True,
+    download=True,
+    transform=torchvision.transforms.ToTensor()
+)
+trainloader = torch.utils.data.DataLoader(
+    trainset,
+    batch_size=4,
+    shuffle=True,
+    num_workers=2
+)
+
+if __name__ == "__main__":
+
+    # define convolutional network
+    net = Net()
+
+    # set up pytorch loss /  optimizer
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+    # train the network
+    for epoch in range(2):
+
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 0):
+            # unpack the data
+            inputs, labels = data
+
+            # zero the parameter gradients
+            optimizer.zero_grad()
+
+            # forward + backward + optimize
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            # print statistics
+            running_loss += loss.item()
+            if i % 2000 == 1999:
+                loss = running_loss / 2000
+                # ADDITIONAL CODE: log loss metric to AML
+                run.log('loss', loss)
+                print(f'epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}')
+                running_loss = 0.0
+
+    print('Finished Training')

tutorials/get-started-day1/code/pytorch-cifar10-train/model.py

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+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x

tutorials/get-started-day1/code/pytorch-cifar10-train/train.py

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+import torch
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+from model import Net
+
+# download CIFAR 10 data
+trainset = torchvision.datasets.CIFAR10(
+    root="./data",
+    train=True,
+    download=True,
+    transform=torchvision.transforms.ToTensor(),
+)
+trainloader = torch.utils.data.DataLoader(
+    trainset, batch_size=4, shuffle=True, num_workers=2
+)
+
+if __name__ == "__main__":
+
+    # define convolutional network
+    net = Net()
+
+    # set up pytorch loss /  optimizer
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+    # train the network
+    for epoch in range(2):
+
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 0):
+            # unpack the data
+            inputs, labels = data
+
+            # zero the parameter gradients
+            optimizer.zero_grad()
+
+            # forward + backward + optimize
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            # print statistics
+            running_loss += loss.item()
+            if i % 2000 == 1999:
+                loss = running_loss / 2000
+                print(f"epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}")
+                running_loss = 0.0
+
+    print("Finished Training")

tutorials/get-started-day1/code/pytorch-cifar10-your-data/model.py

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+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x

tutorials/get-started-day1/code/pytorch-cifar10-your-data/train.py

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+
+import os
+import argparse
+import torch
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+from model import Net
+from azureml.core import Run
+
+run = Run.get_context()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--data_path',
+        type=str,
+        help='Path to the training data'
+    )
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        default=0.001,
+        help='Learning rate for SGD'
+    )
+    parser.add_argument(
+        '--momentum',
+        type=float,
+        default=0.9,
+        help='Momentum for SGD'
+    )
+
+    args = parser.parse_args()
+
+    print("===== DATA =====")
+    print("DATA PATH: " + args.data_path)
+    print("LIST FILES IN DATA PATH...")
+    print(os.listdir(args.data_path))
+    print("================")
+
+    # prepare DataLoader for CIFAR10 data
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+    ])
+    trainset = torchvision.datasets.CIFAR10(
+        root=args.data_path,
+        train=True,
+        download=False,
+        transform=transform,
+    )
+    trainloader = torch.utils.data.DataLoader(
+        trainset,
+        batch_size=4,
+        shuffle=True,
+        num_workers=2
+    )
+
+    # define convolutional network
+    net = Net()
+
+    # set up pytorch loss /  optimizer
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = optim.SGD(
+        net.parameters(),
+        lr=args.learning_rate,
+        momentum=args.momentum,
+    )
+
+    # train the network
+    for epoch in range(2):
+
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 0):
+            # unpack the data
+            inputs, labels = data
+
+            # zero the parameter gradients
+            optimizer.zero_grad()
+
+            # forward + backward + optimize
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            # print statistics
+            running_loss += loss.item()
+            if i % 2000 == 1999:
+                loss = running_loss / 2000
+                run.log('loss', loss)  # log loss metric to AML
+                print(f'epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}')
+                running_loss = 0.0
+
+    print('Finished Training')