1. Autoscaling ML Model Inference Servers on GCP


    To create an autoscaling ML model inference server on Google Cloud Platform (GCP) using Pulumi, we need to use a combination of GCP's Machine Learning (ML) and Compute resources.

    1. ML Model: First, we define an ML model using the gcp.ml.EngineModel resource. This will create a new model in Google Cloud Machine Learning Engine, which can be used for serving online predictions.

    2. Compute Instance Template: We define a Compute Engine instance template using the gcp.compute.InstanceTemplate resource. This template specifies the configuration of the instances that will serve the model, including the machine type, disk, and any startup scripts needed to install the inference server.

    3. Instance Group Manager: We utilize the gcp.compute.InstanceGroupManager to create and manage a group of identical instances based on the previously defined instance template. This manager can also be configured to automatically heal instances, replacing them if they become unhealthy.

    4. Autoscaler: An autoscaler is created using the gcp.compute.Autoscaler resource, which automatically scales the number of instances in the managed instance group based on the defined utilization policy.

    Below is a Pulumi program in Python that implements an autoscaling ML model inference server setup. This program assumes that you have a machine learning model ready to be served and the necessary scripts to install your ML inference server upon instance startup.

    import pulumi import pulumi_gcp as gcp # Replace the placeholders with the actual values for your resources project = "your-gcp-project" model_name = "your-ml-model-name" instance_template_name = "ml-model-server-template" instance_group_manager_name = "ml-model-server-group" autoscaler_name = "ml-model-server-autoscaler" region = "us-central1" # Create the Machine Learning Engine Model ml_model = gcp.ml.EngineModel("ml-engine-model", project=project, name=model_name, description="ML Model for online predictions", regions=[region], # Define other properties of your ML model here, if necessary ) # Define the Compute Engine Instance Template for inference servers instance_template = gcp.compute.InstanceTemplate("instance-template", project=project, name=instance_template_name, machine_type="n1-standard-1", disk=[{ "boot": True, "autoDelete": True, "sourceImage": "projects/debian-cloud/global/images/family/debian-9", # Specify the necessary disk configurations }], network_interfaces=[{ "network": "default", # Omitting accessConfigs defaults to egress-only internet access }], # Specify startup script to install and run the ML inference server metadata_startup_script="startup-script.sh", ) # Create an Instance Group Manager for managing the group of inference instances instance_group_manager = gcp.compute.InstanceGroupManager("instance-group-manager", project=project, name=instance_group_manager_name, base_instance_name="inference-instance", instance_template=instance_template.self_link, target_size=1, # Start with 1 instance and let the autoscaler scale as necessary zone=region + "-a", # Specify the appropriate zone for your use case # Define other properties such as auto-healing policies if needed ) # Set up an Autoscaler to automatically adjust the number of instances autoscaler = gcp.compute.Autoscaler("autoscaler", project=project, name=autoscaler_name, target=instance_group_manager.self_link, autoscaling_policy={ "min_replicas": 1, "max_replicas": 5, # Adjust max replicas based on your needs "cpu_utilization": { "target": 0.6, # Target CPU usage; scale out when above this percentage }, "cool_down_period": 90, # Seconds to wait after a new instance has started before evaluating conditions }, zone=region + "-a", ) # Export the URL of the created model to access later pulumi.export("ml_model_id", ml_model.id)

    This program sets up your infrastructure for autoscaling ML model inference servers. It starts with a single instance and scales out to more instances as CPU usage increases, helping to manage cost and performance.

    Things to Note:

    • Remember to replace placeholder variables (project, model_name, etc.) with the corresponding values for your GCP resources.
    • The metadata_startup_script should specify the path to your startup script stored in Google Cloud Storage, or inline the script contents directly. This script is responsible for installing necessary software and starting the inference server.
    • The autoscaler resource's cpu_utilization is used to determine when to scale in or out. Set the target attribute to the CPU utilization threshold that triggers scaling.
    • Adjust min_replicas and max_replicas according to your specific needs, which control the minimum and maximum number of instances the autoscaler can manage.
    • Resource names must be unique within a project and comply with GCP's naming conventions.

    By running this program with Pulumi, these resources will be provisioned on GCP in accordance with your specifications, enabling an autoscaling environment for your ML model inference workload.