Resource Optimization for AI with Azure Resource Graph

Question

Pulumi · Accepted Answer

Resource optimization in cloud environments is a critical aspect of managing costs and ensuring that resources are used efficiently, especially for intensive workloads such as AI (Artificial Intelligence). Azure Resource Graph is a service in Azure that allows you to explore your Azure resources using a powerful query language. It's useful for understanding your resource landscape, managing inventory, and governance across subscriptions.

In the context of AI, you might be looking to:

1. Find and analyze the resources being used by AI services.
2. Optimize these resources, perhaps by scaling them down during off-peak times or scaling them up when more computational power is needed.
3. Audit resources for compliance with organizational policies.

Unfortunately, Pulumi itself does not interact directly with Azure Resource Graph as it is more of a querying tool for resources that exist within Azure. However, you can use Pulumi to manage the Azure resources that would host your AI workloads in an optimized manner.

One way to optimize resources for AI with Azure using Pulumi is to create and configure Azure Machine Learning workspaces, compute instances, compute clusters, and manage the scales and sizes according to the need of the workload. Below is a Pulumi program written in Python that provisions an Azure Machine Learning Workspace and a Compute Cluster, which could be used to run AI workloads:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Create an Azure Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace("ml_workspace",
    resource_group_name=resource_group.name,
    sku=azure_native.machinelearningservices.SkuArgs(
        name="Standard",
    ),
    location=resource_group.location,
    identity=azure_native.machinelearningservices.IdentityArgs(
        type="SystemAssigned",
    ),
)

# Create an Azure Machine Learning Compute Cluster
# Note: Instance types and scales are set to small values for cost-effectiveness
# Adjust these parameters based on the required AI workload
ml_compute_cluster = azure_native.machinelearningservices.ComputeInstance("ml_compute_cluster",
    compute_name="cpu-cluster",
    properties=azure_native.machinelearningservices.ComputeInstanceArgs(
        compute_type="AmlCompute",
        properties=azure_native.machinelearningservices.AmlComputeArgs(
            vm_size="STANDARD_DS3_V2",
            vm_priority="Dedicated",
            scale_settings=azure_native.machinelearningservices.ScaleSettingsArgs(
                max_node_count=4,
                min_node_count=1,
                node_idle_time_before_scale_down="PT5M"
            ),
        ),
    ),
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
)

# Export the Azure Machine Learning Workspace URL
pulumi.export("workspace_url", ml_workspace.workspace_url)
# Export the Azure Machine Learning Compute Cluster ID
pulumi.export("compute_cluster_id", ml_compute_cluster.id)
```

In the program above:

- A new Azure Resource Group is created to organize all our resources.
- An Azure Machine Learning Workspace is provisioned. This is where you would manage your machine learning experiments, data, models, and deployments. A "Standard" SKU is chosen for the example, which can be adjusted based on your requirements.
- An Azure Machine Learning Compute Cluster is defined. This is where your AI training and inference jobs would run. The cluster is configured with a specific VM size (`STANDARD_DS3_V2`) and scaling settings – in this case, it will scale down if the nodes are idle for more than 5 minutes to optimize costs.

If you wish to analyze and optimize resources currently utilized by AI services, you would typically use Azure Resource Graph Query Language (KQL) to run queries in the Azure portal or via Azure CLI, and then potentially write Pulumi code to adjust the resources based on the insights you gain from those queries.

Remember, actual resource optimization should be based on a thorough understanding of your AI workloads' performance characteristics and usage patterns.