1. GPU-enabled Azure VMs for Deep Learning.


    To create GPU-enabled Azure Virtual Machines (VMs) for deep learning tasks, we will utilize the azure-native Pulumi package. This package provides direct access to Azure resources and will allow us to provision a VM with a specific size that supports GPUs.

    In Azure, VMs that are suitable for deep learning and have GPU capabilities often belong to the N-Series. An example of a GPU-enabled VM size is Standard_NC6, which comes with NVIDIA Tesla K80 GPUs.

    Here's how we can create such a VM with Pulumi in Python:

    First, we need to set up the necessary resources:

    • A resource group to contain our VM.
    • A virtual network and subnet to connect our VM to.
    • A public IP address so we can access our VM over the internet.
    • A network interface (NIC) for our VM to attach to the network.
    • Finally, the VM itself, with a GPU-enabled size.

    Throughout this process, we'll use the best practices for defining cloud resources with Pulumi, including strongly-typed classes and leveraging the .apply method for outputs that need processing.

    Let's start building our program:

    import pulumi from pulumi_azure_native import compute, network, resources # Define a resource group resource_group = resources.ResourceGroup('gpu-vm-resource-group') # Create a virtual network and a subnet for the VM virtual_network = network.VirtualNetwork('gpu-vm-vnet', resource_group_name=resource_group.name, address_space=network.AddressSpaceArgs( address_prefixes=[''], ), ) subnet = network.Subnet('gpu-vm-subnet', resource_group_name=resource_group.name, virtual_network_name=virtual_network.name, address_prefix=network.SubnetArgs( address_prefix='', ), ) # Provision a public IP for our VM to be accessible over the internet public_ip = network.PublicIPAddress('gpu-vm-ip', resource_group_name=resource_group.name, public_ip_allocation_method='Dynamic', ) # Create a network interface for our VM network_interface = network.NetworkInterface('gpu-vm-nic', resource_group_name=resource_group.name, ip_configurations=[network.NetworkInterfaceIPConfigurationArgs( name='gpu-vm-nic-ipconfig', subnet=network.SubnetArgs( id=subnet.id, ), public_ip_address=network.PublicIPAddressArgs( id=public_ip.id, ), )], ) # Now, create the VM with GPU capability vm = compute.VirtualMachine('gpu-vm', resource_group_name=resource_group.name, location=resource_group.location, network_profile=compute.NetworkProfileArgs( network_interfaces=[compute.NetworkInterfaceReferenceArgs( id=network_interface.id, primary=True, )], ), hardware_profile=compute.HardwareProfileArgs( vm_size='Standard_NC6', # This is an example VM size with GPU capability for deep learning ), os_profile=compute.OSProfileArgs( computer_name='gpudeeplearnVM', admin_username='adminuser', admin_password='Password#1234', # Replace with a secure password. ), storage_profile=compute.StorageProfileArgs( image_reference=compute.ImageReferenceArgs( publisher='Canonical', offer='UbuntuServer', sku='18.04-LTS', version='latest', ), ), ) # Export the public IP address of the VM pulumi.export('public_ip', public_ip.ip_address)

    In this program:

    • We create a resource group which will contain all other resources.
    • We set up a virtual network with a single subnet within the resource group.
    • A dynamic public IP address is created to allow public internet access to the VM.
    • We provision a network interface associated with the virtual network's subnet and public IP.
    • We then create the Virtual Machine itself, specifying Standard_NC6 for the VM size, which is equipped with NVIDIA Tesla K80 GPUs, suitable for deep learning tasks.
    • The VM is configured with an Ubuntu Server image, but you can choose another OS image that supports your deep learning framework.
    • Finally, we export the public IP address so you can SSH into your VM or access it for other purposes.

    Remember to replace admin_password with a strong, secure password and keep it confidential.

    To proceed from here, you would:

    1. Install Pulumi and configure Azure if you haven't already (see Pulumi's Get Started with Azure).
    2. Save the above code to a file, for example __main__.py.
    3. Run pulumi up in the directory containing the code to deploy your VM.

    Pulumi's CLI will provide you with a summary of the resources to be created and ask for confirmation before provisioning. The output will include the public IP address which you can use to access your VM once it's up and running.