1. Scalable ML Model Inference with GCP Compute Instances


    To set up a scalable Machine Learning (ML) model inference environment on Google Cloud Platform (GCP) using Pulumi, we will create a managed instance group that can scale up or down based on demand. This allows you to handle varying loads of inference requests efficiently.

    We will achieve this through the following steps:

    1. Create a GCP Compute Instance Template, specifying a machine type and image suitable for ML inference.
    2. Define an Instance Group Manager based on that template to manage our group of instances.
    3. Optional: Configure autoscaling policies that will automatically adjust the size of the instance group based on predefined criteria such as CPU usage.

    The machine type and image should be chosen based on the requirements of the ML model. For example, if your model is GPU-accelerated, you might choose an image with CUDA pre-installed and a machine type that includes GPUs.

    Here's a Python program using Pulumi which sets up a scalable ML model inference environment on GCP:

    import pulumi import pulumi_gcp as gcp # Configuration project = "your-gcp-project" # Replace with your GCP project ID zone = "us-central1-a" # Replace with your preferred GCP zone machine_type = "n1-standard-2" # Replace with the desired machine type instance_image = "your-ml-model-image" # Replace with the image of your ML model # Create a GCP Compute Instance Template compute_instance_template = gcp.compute.InstanceTemplate("ml-inference-template", project=project, machine_type=machine_type, disks=[{ "boot": True, "autoDelete": True, "type": "PERSISTENT", "initializeParams": { "image": instance_image, }, }], network_interfaces=[{ "network": "default", "accessConfigs": [{ "type": "ONE_TO_ONE_NAT", "networkTier": "PREMIUM", }], }], ) # Create an Instance Group Manager instance_group_manager = gcp.compute.InstanceGroupManager("ml-inference-group-manager", project=project, zone=zone, base_instance_name="ml-inference", instance_template=compute_instance_template.self_link, target_size=1, # Start with 1 instance and let autoscaling take control ) # Optional: Define autoscaling policies based on CPU usage autoscaler = gcp.compute.Autoscaler("ml-inference-autoscaler", project=project, target=instance_group_manager.self_link, autoscaling_policy={ "min_replicas": 1, "max_replicas": 5, # Maximum number of instances "cpu_utilization": { "utilization_target": 0.6 # Scale up if CPU utilization exceeds 60% }, "cooldown_period": 60 # Number of seconds to wait after a scaling action }, ) # Export the instance group manager URL pulumi.export('instance_group_manager_url', instance_group_manager.instance_group)

    In the given program, we have:

    • Defined a Compute Instance Template which specifies the configuration for instances that will run our ML model.
    • Created an Instance Group Manager which will use the template to create a managed group of instances.
    • Set up an optional Autoscaler to allow our instance group to scale based on the CPU load, allowing us to handle higher inference loads when needed.
    • Exported the URL of the instance group manager so we can access it if needed.

    Make sure to replace the placeholder values like project, zone, machine_type, and instance_image with the appropriate values for your project and ML model requirements.

    The choice of machine type (machine_type) and the image that contains your ML model (instance_image) should be tailored to the specific needs of your model. If GPUs are required, make sure to select a machine type that includes GPUs and configure the instance template accordingly.

    You can further customize the instance group manager and autoscaler to match your precise scalability requirements.

    After the code is written, you can run this with the Pulumi CLI to deploy it to GCP. If you haven't done so already, you will need to log in to GCP and set up the Pulumi GCP provider to have access to the necessary credentials to create resources in your project.