quickstart added

tutorials/get-started-day1/IDE-users/01-create-workspace.py

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+# 01-create-workspace.py
+from azureml.core import Workspace
+
+# Example locations: 'westeurope' or 'eastus2' or 'westus2' or 'southeastasia'.
+ws = Workspace.create(name='<my_workspace_name>',
+                      subscription_id='<azure-subscription-id>',
+                      resource_group='<myresourcegroup>',
+                      create_resource_group=True,
+                      location='<NAME_OF_REGION>')
+
+# write out the workspace details to a configuration file: .azureml/config.json
+ws.write_config(path='.azureml')

tutorials/get-started-day1/IDE-users/02-create-compute.py

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+# 02-create-compute.py
+from azureml.core import Workspace
+from azureml.core.compute import ComputeTarget, AmlCompute
+from azureml.core.compute_target import ComputeTargetException
+
+ws = Workspace.from_config()
+
+# Choose a name for your CPU cluster
+cpu_cluster_name = "cpu-cluster"
+
+# Verify that cluster does not exist already
+try:
+    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
+    print('Found existing cluster, use it.')
+except ComputeTargetException:
+    cfg = AmlCompute.provisioning_configuration(
+        vm_size='STANDARD_D2_V2',
+        max_nodes=4,
+        idle_seconds_before_scaledown=2400
+    )
+    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, cfg)
+
+cpu_cluster.wait_for_completion(show_output=True)

tutorials/get-started-day1/IDE-users/03-run-hello.py

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+# 03-run-hello.py
+from azureml.core import Workspace, Experiment, ScriptRunConfig
+
+ws = Workspace.from_config()
+experiment = Experiment(workspace=ws, name='day1-experiment-hello')
+
+config = ScriptRunConfig(source_directory='./src',
+                         script='hello.py',
+                         compute_target='cpu-cluster')
+
+run = experiment.submit(config)
+aml_url = run.get_portal_url()
+print(aml_url)

tutorials/get-started-day1/IDE-users/04-run-pytorch.py

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+# 04-run-pytorch.py
+from azureml.core import Workspace
+from azureml.core import Experiment
+from azureml.core import Environment
+from azureml.core import ScriptRunConfig
+
+if __name__ == "__main__":
+    ws = Workspace.from_config()
+    experiment = Experiment(workspace=ws, name='day1-experiment-train')
+    config = ScriptRunConfig(source_directory='./src',
+                             script='train.py',
+                             compute_target='cpu-cluster')
+
+    # set up pytorch environment
+    env = Environment.from_conda_specification(
+        name='pytorch-env',
+        file_path='./environments/pytorch-env.yml'
+    )
+    config.run_config.environment = env
+
+    run = experiment.submit(config)
+
+    aml_url = run.get_portal_url()
+    print(aml_url)

tutorials/get-started-day1/IDE-users/05-upload-data.py

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+# 05-upload-data.py
+from azureml.core import Workspace
+ws = Workspace.from_config()
+datastore = ws.get_default_datastore()
+datastore.upload(src_dir='./data',
+                 target_path='datasets/cifar10',
+                 overwrite=True)

tutorials/get-started-day1/IDE-users/06-run-pytorch-data.py

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+# 06-run-pytorch-data.py
+from azureml.core import Workspace
+from azureml.core import Experiment
+from azureml.core import Environment
+from azureml.core import ScriptRunConfig
+from azureml.core import Dataset
+
+if __name__ == "__main__":
+    ws = Workspace.from_config()
+    datastore = ws.get_default_datastore()
+    dataset = Dataset.File.from_files(path=(datastore, 'datasets/cifar10'))
+
+    experiment = Experiment(workspace=ws, name='day1-experiment-data')
+
+    config = ScriptRunConfig(
+        source_directory='./src',
+        script='train.py',
+        compute_target='cpu-cluster',
+        arguments=[
+            '--data_path', dataset.as_named_input('input').as_mount(),
+            '--learning_rate', 0.003,
+            '--momentum', 0.92],
+    )
+    # set up pytorch environment
+    env = Environment.from_conda_specification(
+        name='pytorch-env',
+        file_path='./environments/pytorch-env.yml'
+    )
+    config.run_config.environment = env
+
+    run = experiment.submit(config)
+    aml_url = run.get_portal_url()
+    print("Submitted to compute cluster. Click link below")
+    print("")
+    print(aml_url)

tutorials/get-started-day1/IDE-users/README.md

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+# Get Started (day 1) with Azure Machine Learning: IDE Users
+
+This folder has been setup for IDE user (for example, VS Code or Pycharm) following the [Get started (day 1) with Azure Machine Learning tutorial series](https://aka.ms/day1aml).
+
+The directory is structured as follows:
+
+```Text
+IDE-users
+└──environments
+|  └──pytorch-env.yml
+└──src
+|  └──hello.py
+|  └──model.py
+|  └──train.py
+└──01-create-workspace.py
+└──02-create-compute.py
+└──03-run-hello.py
+└──04-run-pytorch.py
+└──05-upload-data.py
+└──06-run-pytorch-data.py
+```
+
+Please refer to [the documentation](https://aka.ms/day1aml) for more details on these files.
+
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/get-started-day1/IDE/README.png)

tutorials/get-started-day1/IDE-users/environments/pytorch-env.yml

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+
+name: pytorch-env
+channels:
+    - defaults
+    - pytorch
+dependencies:
+    - python=3.6.2
+    - pytorch
+    - torchvision

tutorials/get-started-day1/IDE-users/src/hello.py

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+
+print("hello world!")

tutorials/get-started-day1/IDE-users/src/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/IDE-users/src/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")