Scalable Virtual Machines for Large Language Models

Question

Pulumi · Accepted Answer

To achieve scalable virtual machines for running large language models, we can use a cloud service that offers Virtual Machine Scale Sets (VMSS). Azure provides this feature, allowing us to easily create and manage a group of load-balanced VMs. With VMSS, the number of VM instances can automatically increase or decrease in response to demand or a defined schedule. This scalability is particularly useful when dealing with workload variations, as is common with large language model processing.

We'll build a VM scale set on Azure using Pulumi's infrastructure as code. This scale set will be configured with a given instance size suitable for computational workloads such as language models. Instances will have a managed disk, networking capabilities, and, optionally, extensions for further management or monitoring. We can define the desired capacity for the scale set and rely on Azure's autoscaling capabilities to handle demand changes.

Below is a Pulumi program in Python that creates a scalable Azure Virtual Machine Scale Set:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure resource group in which all resources will be created
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Define the scale set configuration
vm_scale_set = azure_native.compute.VirtualMachineScaleSet("vm_scale_set",
    # Assign the resource group and location
    resource_group_name=resource_group.name,
    location=resource_group.location,
    
    # Define the scale set properties
    sku=azure_native.compute.SkuArgs(
        name="Standard_F8s_v2",  # This is an example size, choose one that suits language model workloads
        capacity=2,  # Initial instance count, can be autoscaled based on demand
    ),
    overprovision=True,  # Enable over-provisioning to improve deployment speed
    upgrade_policy=azure_native.compute.UpgradePolicyArgs(
        mode="Manual"  # Manual or automatic upgrades of VM instances
    ),
    virtual_machine_profile=azure_native.compute.VirtualMachineScaleSetVMProfileArgs(
        # Define the OS profile (Linux or Windows configurations)
        os_profile=azure_native.compute.VirtualMachineScaleSetOSProfileArgs(
            computer_name_prefix="languagemodel",
            admin_username="adminuser",
            # For security reasons, use a method like Azure KeyVault to set admin passwords instead of hardcoding
        ),
        # Define the network profile such as network interface configurations
        network_profile=azure_native.compute.VirtualMachineScaleSetNetworkProfileArgs(
            network_interface_configurations=[
                azure_native.compute.VirtualMachineScaleSetNetworkConfigurationArgs(
                    name="network_config",
                    primary=True,
                    ip_configurations=[azure_native.compute.VirtualMachineScaleSetIPConfigurationArgs(
                        name="ip_config",
                        subnet=azure_native.compute.ApiEntityReferenceArgs(
                            id="/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.Network/virtualNetworks/{vnet_name}/subnets/{subnet_name}"
                            # Placeholder IDs, replace with actual subscription, resource group, virtual network and subnet IDs
                        ),
                    )],
                ),
            ],
        ),
        # Define the storage profile such as OS, image, and disk configuration
        storage_profile=azure_native.compute.VirtualMachineScaleSetStorageProfileArgs(
            image_reference=azure_native.compute.ImageReferenceArgs(
                publisher="Canonical",
                offer="UbuntuServer",
                sku="18.04-LTS",  # Choose the appropriate Linux version or another OS
                version="latest",
            ),
            os_disk=azure_native.compute.VirtualMachineScaleSetOSDiskArgs(
                caching="ReadWrite",
                create_option="FromImage",
                managed_disk=azure_native.compute.VirtualMachineScaleSetManagedDiskParametersArgs(
                    storage_account_type="Premium_LRS",
                )
            ),
        ),
        # Optionally, define extensions for additional configuration tasks or software installation
    ))

# Export the VM scale set ID
pulumi.export('vm_scale_set_id', vm_scale_set.id)
```

In this program, we first create an Azure Resource Group which is a container for managing related resources. We then define the configuration for our Virtual Machine Scale Set including the instance size, initial capacity, OS, networking, and storage details. The SKU (e.g., "Standard_F8s_v2") should be selected based on the requirements of the large language models you intend to run. "UbuntuServer" is chosen as the OS for this example, you can select a different one based on your requirements.

This scale set is initialized with two instances, but through the Azure portal or the Azure CLI, you can configure autoscaling rules based on metrics such as CPU usage, memory demand, or custom metrics.

Exporting properties like the `vm_scale_set_id` can help in tracking and managing the created resources outside of Pulumi.

To apply this Pulumi program, you need to set up and log in to Azure and Pulumi, then run `pulumi up` to create and deploy the resources. Make sure to select appropriate values for placeholders like `{subscription_id}`, `{resource_group_name}`, `{vnet_name}`, and `{subnet_name}`.

Scaling behavior and detailed configurations can also be specified by changing the parameters or by introducing new properties in the configuration objects. If you want to run custom initialization scripts or install extra software on VM instances, you can use VM extensions (not provided in the example for brevity).