1. Auto-Scaling Inference Services on GCP Container Node Pools


    To achieve auto-scaling inference services on GCP Container Node Pools, you need to configure a Google Kubernetes Engine (GKE) cluster with an auto-scaling node pool. The node pool will manage the underlying VM instances that power your Kubernetes pods, and you can set parameters to automatically scale this pool based on the workload.

    Here's how we'll set up the infrastructure for auto-scaling inference services on GCP using Pulumi with Python:

    1. GKE Cluster: We'll first create a GKE cluster, which is the foundational environment where our containers will run.

    2. Node Pool: We'll create a node pool within this cluster, specifying parameters for auto-scaling. This enables the cluster to automatically adjust the number of nodes based on CPU usage or other selected metrics.

    3. Inference Service Deployment: Although not detailed in this script, you would typically deploy your inference service as a set of pods within the cluster which can scale depending on the node pool's ability to provision additional resources.

    For the purpose of this demonstration, we'll focus on setting up the GKE cluster and auto-scaling node pool using Pulumi's GCP provider.

    import pulumi from pulumi_gcp import container # Define the GKE cluster cluster = container.Cluster("inference-cluster", # Set the initial number of nodes for the default node pool initial_node_count=3, # Choose the type of machine and other configurations for the default node pool node_config={ "machine_type": "n1-standard-1", "oauth_scopes": [ "https://www.googleapis.com/auth/cloud-platform" ], }, ) # Define the auto-scaling node pool auto_scaling_node_pool = container.NodePool("inference-autoscaling-node-pool", cluster=cluster.name, autoscaling={ "min_node_count": 1, "max_node_count": 10, # Set the min and max nodes for auto-scaling }, # The configuration of VMs in the node pool node_config={ "machine_type": "n1-standard-4", # Use a higher-spec machine for inference workloads "oauth_scopes": [ "https://www.googleapis.com/auth/cloud-platform", ], "labels": {"workload": "inference"}, # Label the nodes for easier management }, # Management settings including automatic repair and upgrade management={ "auto_repair": True, "auto_upgrade": True, }, initial_node_count=1 # Initial number of nodes to start with ) # Export the cluster name and auto-scaling node pool details pulumi.export("cluster_name", cluster.name) pulumi.export("node_pool_name", auto_scaling_node_pool.name)

    Let's break down what we're doing:

    1. We import Pulumi and the GCP container module to work with GKE resources.
    2. We create a GKE cluster named inference-cluster that starts with an initial node count of 3 on the default node pool. The machine_type and oauth_scopes are configured for basic use. See GKE Cluster for more details.
    3. We create a separate auto-scaling node pool named inference-autoscaling-node-pool within the cluster, which has auto-scaling enabled to range from 1 node to up to 10 nodes. The node configuration is set to a higher specification suitable for inference workloads. We also enable auto_repair and auto_upgrade to keep the pool robust and up-to-date. See GKE NodePool for more details.
    4. Finally, we export the cluster name and the auto-scaling node pool name as stack outputs.

    When you deploy this Pulumi program, it will provision the necessary infrastructure on GCP for an auto-scaling inference service. Subsequently, you can deploy your inference service workloads to your GKE cluster.