1. Virtual Machines for Training Deep Learning Models on Azure


    To create a Virtual Machine (VM) for training deep learning models on Azure, you'd typically need a VM with specific capabilities like high-performance GPUs, ample CPU, memory resources, and potentially high-speed networking. Azure offers various VM sizes that are optimized for AI and machine learning tasks, such as the Azure NC series, which are GPU-enabled VMs ideal for compute-intensive, graphics-intensive, and visualization workloads.

    Below, I'll provide you with a Pulumi program written in Python that will create an Azure Virtual Machine suitable for deep learning tasks. This VM will be part of a resource group and have a network interface, a public IP, and a virtual network associated with it to allow network connectivity. It will use an Ubuntu Server image, which is common for deep learning environments, and have a GPU-enabled VM size.

    Here's a step-by-step guide on what the Pulumi program will do:

    1. Create a Resource Group: Organize related resources within Azure.
    2. Create a Virtual Network and Subnet: Define a network space where your VM will live.
    3. Create a Public IP: Assign an IP so you can access the VM from the internet.
    4. Create a Network Interface: Connect your VM to the network and expose it through the public IP.
    5. Create a Virtual Machine: This will be your deep learning environment.
    6. Install GPU Drivers and Tools: This step is usually performed manually or as part of an initialization script since it may require custom configurations based on the specific deep learning frameworks and tools you plan to use.

    I'll use the azure_native.compute.VirtualMachine resource from the azure-native package as it offers greater control over the creation and management of VMs in Azure. This is a higher-level component than VMs created with the classic azure package and is preferred for new resources.

    import pulumi import pulumi_azure_native as azure_native # Replace these variables with your own desired settings vm_name = "DeepLearningVM" location = "West US 2" admin_username = "azureuser" admin_password = "SET_YOUR_PASSWORD" # Please use a secure way to manage your passwords/secrets resource_group_name = "ai_resources" network_name = "ai_network" subnet_name = "ai_subnet" ip_name = "ai_ip" nic_name = "ai_nic" # Configure the resource group resource_group = azure_native.resources.ResourceGroup("resource_group", resource_group_name=resource_group_name, location=location) # Configure the virtual network and subnet virtual_network = azure_native.network.VirtualNetwork("virtual_network", resource_group_name=resource_group.name, location=location, address_space=azure_native.network.AddressSpace( address_prefixes=[""] ), virtual_network_name=network_name) subnet = azure_native.network.Subnet("subnet", resource_group_name=resource_group.name, virtual_network_name=virtual_network.name, address_prefix="", subnet_name=subnet_name) # Create a public IP for our VM to be accessible from the internet public_ip = azure_native.network.PublicIPAddress("public_ip", resource_group_name=resource_group.name, location=location, public_ip_address_version="IPv4", public_ip_allocation_method="Dynamic", public_ip_address_name=ip_name) # Create a network interface for the VM network_interface = azure_native.network.NetworkInterface("network_interface", resource_group_name=resource_group.name, location=location, network_interface_name=nic_name, ip_configurations=[azure_native.network.NetworkInterfaceIPConfigurationArgs( name="ipconfig1", subnet=azure_native.network.SubnetArgs(id=subnet.id), public_ip_address=azure_native.network.PublicIPAddressArgs( id=public_ip.id), )]) # Create an Azure VM configuration vm = azure_native.compute.VirtualMachine("vm", resource_group_name=resource_group.name, location=location, vm_name=vm_name, hardware_profile=azure_native.compute.HardwareProfileArgs( vm_size="Standard_NC6" # This is a GPU enabled VM size ), os_profile=azure_native.compute.OSProfileArgs( computer_name=vm_name, admin_username=admin_username, admin_password=admin_password, ), network_profile=azure_native.compute.NetworkProfileArgs( network_interfaces=[ azure_native.compute.NetworkInterfaceReferenceArgs( id=network_interface.id, primary=True, ) ] ), storage_profile=azure_native.compute.StorageProfileArgs( image_reference=azure_native.compute.ImageReferenceArgs( publisher="Canonical", offer="UbuntuServer", sku="18.04-LTS", version="latest" ), os_disk=azure_native.compute.OSDiskArgs( create_option="FromImage", disk_size_gb=30, # Size of the OS disk ), data_disks=[ # Add additional disks as needed azure_native.compute.DataDiskArgs( create_option="Empty", disk_size_gb=1024, # Size of the data disk lun=0, ) ] )) # Export the public IP address of the VM pulumi.export("public_ip", public_ip.ip_address)

    This program will create the necessary Azure resources to host a VM suitable for deep learning tasks.

    Note: For deep learning workloads, you might want to configure additional settings such as GPU driver installation, CUDA toolkit installation, and deep learning framework setups (like TensorFlow, PyTorch, etc.). Commonly, this is handled by user data scripts or custom VM images preloaded with such tools, but these details are beyond the scope of this program.

    After running this Pulumi program, you'll have an Azure VM up and ready. You can then SSH into the VM using the public IP and set up your deep learning environment as required.