1. Binding GPU Clusters to Backend Pools for Accelerated Inference


    To bind GPU clusters to backend pools for accelerated inference, you typically need a solution that involves creating a cluster of compute instances equipped with GPUs and configuring a load balancing mechanism to distribute incoming inference requests across the cluster. These tasks often involve creating a Kubernetes cluster where each node is a GPU-enabled virtual machine, and setting up services and ingress resources to manage traffic.

    For this, we will use two Pulumi resources:

    1. A Kubernetes cluster with node pools configured to use GPU instances. This is necessary because we want to perform GPU-accelerated inference tasks. For this purpose, we can use cloud-specific resources like azure.containerservice.KubernetesClusterNodePool for Microsoft Azure or oci.ContainerEngine.NodePool for Oracle Cloud Infrastructure depending on the cloud provider.
    2. A load balancer to distribute the inference requests across the nodes of the cluster. For Google Cloud Platform, we can use google-native.compute/alpha.TargetPool, and for Azure, we can use azure-native.machinelearningservices.InferencePool.

    Let's consider an example using Microsoft Azure where we create an AKS (Azure Kubernetes Service) cluster and configure a node pool with GPU instances. We will then create an inference pool that would serve as our backend for load-balanced inference requests.

    Make sure you have the Azure provider configured for Pulumi and that you can authenticate against it. You can find information about Azure setup in the Pulumi documentation.

    Below is a Pulumi program in Python to create such an infrastructure:

    import pulumi import pulumi_azure as azure import pulumi_azure_native as azure_native # Create an Azure Resource Group, which is a logical container into which Azure resources are deployed and managed. resource_group = azure.core.ResourceGroup("gpu-inference-rg") # Create an AKS cluster. # This will be our Kubernetes cluster where we will deploy our applications for GPU-accelerated inference. aks_cluster = azure.containerservice.KubernetesCluster( "gpu-inference-cluster", resource_group_name=resource_group.name, default_node_pool={ "name": "default", "node_count": 1, "vm_size": "Standard_DS2_v2", }, identity={ "type": "SystemAssigned", }, ) # Define a node pool with GPU-enabled instances. # Here we are specifying the size of VMs to be one that is known to come with a GPU (e.g., Standard_NC6). gpu_node_pool = azure_native.containerservice.AgentPool( "gpu-node-pool", resource_group_name=resource_group.name, cluster_name=aks_cluster.name, vm_size="Standard_NC6", # Specify a VM size that includes GPU. min_count=1, max_count=4, mode="User", node_count=1, scale_set_priority="Spot", # Optional: Use Spot instances for cost savings. tags={ "purpose": "gpu-inference" }, ) # The following code creates an inference pool, which will be used as the backend pool for serving inference requests. # Please note, to fully configure the inference pool, additional configurations like networking and security settings might be needed. inference_pool = azure_native.machinelearningservices.InferencePool( "inference-pool", resource_group_name=resource_group.name, location=resource_group.location, identity={ "type": "SystemAssigned", }, sku={ "name": "Standard_NC6", # This should match the VM size used in your GPU node pool. }, # Additional properties for the inference pool could be set here. ) pulumi.export("resource_group_name", resource_group.name) pulumi.export("aks_cluster_name", aks_cluster.name) pulumi.export("gpu_node_pool_id", gpu_node_pool.id) pulumi.export("inference_pool_id", inference_pool.id)


    1. Resource Group: We begin by creating a Resource Group, which is a fundamental building block of Azure and provides a way to manage multiple resources that share the same lifecycle, permissions, and policies.

    2. Kubernetes Cluster (AKS): We create an AKS cluster, which provides us with a managed Kubernetes service. This cluster will have a default node pool where the actual non-GPU workloads can run.

    3. GPU Node Pool: We add a separate node pool consisting of GPU-enabled VM instances. This node pool is where our GPU-accelerated workloads (like inference services) will run. The VM size Standard_NC6 is an example size that includes a GPU. Modify it based on your specific cloud provider and requirements.

    4. Inference Pool: This resource represents a pool of compute resources in Azure Machine Learning services that can be used for batch inference or real-time serving of machine learning models. We've provisioned it with the same VM size as the node pool for simplicity.

    5. Exports: Finally, we export the identifiers of the created resources. These outputs can be used to interact with or reference the resources within Pulumi or in external scripts and systems.

    This Pulumi program is the first step toward building a GPU-accelerated inference system. You would still need to deploy your machine learning models and services into the AKS cluster and configure the service endpoints and ingress controllers to route traffic properly to leverage the GPU Node Pool and Inference Pool. Additionally, you will need to ensure that your cloud account has enough GPU quota and that the selected VM sizes with GPUs are available in your region.