High-Availability Inference Services with GCP Instance Groups.

Question

Pulumi · Accepted Answer

To establish high-availability inference services on Google Cloud Platform (GCP) with instance groups, we will need to create a managed instance group that can automatically scale and manage a collection of virtual machine (VM) instances across multiple zones. This allows us to ensure that our inference services are fault-tolerant and can handle varying loads.

We will use the following resources from GCP provided by Pulumi:

- `InstanceTemplate`: This resource defines the properties of the VM instances that are created as part of an instance group. It includes the machine type, boot disk image, network settings, and more.
  - Documentation: [InstanceTemplate](https://www.pulumi.com/registry/packages/gcp/api-docs/compute/instancetemplate/)

- `InstanceGroupManager`: This resource manages a group of instances that are created based on the instance template. It can be set to automatically scale the number of instances based on defined criteria, automatically repair unhealthy instances, and spread instances across multiple zones for high availability.
  - Documentation: [InstanceGroupManager](https://www.pulumi.com/registry/packages/gcp/api-docs/compute/instancegroupmanager/)

- `RegionInstanceGroupManager`: Similar to `InstanceGroupManager`, this resource allows you to manage instance groups at a regional level, providing high availability across multiple zones within the region.
  - Documentation: [RegionInstanceGroupManager](https://www.pulumi.com/registry/packages/gcp/api-docs/compute/regioninstancegroupmanager/)

Here's a program in Python using Pulumi which sets up a high-availability inference service using GCP's Instance Groups:

```python
import pulumi
import pulumi_gcp as gcp

# Create an Instance Template for our inference service VMs.
# Adjust the machineType, sourceImage, and other properties based on your inference workload requirements.
instance_template = gcp.compute.InstanceTemplate("inference-instance-template",
    properties={
        "machineType": "n1-standard-1",  # Choose an appropriate machine type
        "disks": [{
            "boot": True,
            "initializeParams": {
                "image": "projects/deeplearning-platform-release/global/images/family/tf2-latest-cpu",  # Use an appropriate image
            },
        }],
        "networkInterfaces": [{
            "network": "default",
            "accessConfigs": [{}],  # Access configs for external IP allocation
        }],
    }
)

# Create a Regional Instance Group Manager for high-availability across multiple zones.
# This assumes the resources for the manager are sufficient for auto scaling and high availability.
# Set the targetSize to the initial number of instances you want in your group.
region_instance_group_manager = gcp.compute.RegionInstanceGroupManager("inference-region-instance-group-manager",
    base_instance_name="inference-vm",
    instance_template=instance_template.self_link,
    region="us-central1",  # Choose an appropriate region
    target_size=3,  # Set the initial target size of the instance group
    auto_healing_policies=[{  # Auto-heal unhealthy instances
        "health_check": pulumi.Resource("health-check"),  # Replace with a real health check
        "initial_delay_sec": 300  # Time before initiating auto-healing
    }],
)

# Export the URL of the instance group to access the deployed inference services.
pulumi.export('instance_group_manager_url', region_instance_group_manager.instance_group)
```

In this program, we first created an `InstanceTemplate` with specs that match the needs of our inference workload. We've used an image from the Deep Learning Platform Releases with TensorFlow installed, which is well-suited for inference services based on TensorFlow models.

Then, we created a `RegionInstanceGroupManager` using the instance template we defined. This manager is responsible for ensuring our inference VMs are spread across multiple zones within a region, supporting high-availability. We also enabled auto-healing with a health check, so any unhealthy VM instances will be automatically repaired.

This program assumes that you already have a health check configured, so you'll need to provide the health check resource or create one as part of your Pulumi program.

Make sure to adjust the properties such as `machineType`, `sourceImage`, and `region` according to the needs of your specific inference services. Also, configure the `health_check` property to point to a valid health check for your VM instances.

With this setup, your inference services will be highly available and resilient to failures across multiple zones in the region. Pulumi's infrastructure as code approach allows for configurable, reproducible, and scalable deployments.