High-Throughput Inference Serving with GCP Kubernetes Engine

Question

Pulumi · Accepted Answer

To set up a high-throughput inference serving system using Kubernetes Engine on Google Cloud Platform (GCP), you would need to create a Kubernetes cluster and configure it appropriately to handle machine learning workloads efficiently. Google Kubernetes Engine (GKE) provides a managed environment for deploying, managing, and scaling containerized applications using Google infrastructure.

To begin, you would need to create a GKE cluster with enough capacity to handle your inference serving workload. You can leverage Google’s Compute Engine instances with attached GPUs or TPUs if your workload requires them.

Using Pulumi, you can define infrastructure as code (IaC), which allows you to version, reuse, and share your infrastructure setup as easily as you do with software code. Below you will find a Python program written with Pulumi which creates a GKE cluster suitable for high-throughput inference serving.

The following example will guide you through the process:

1. Setting up a GKE cluster with a specified node pool configuration.
2. Enabling necessary APIs and using appropriate machine types.
3. Optionally attaching accelerators like GPUs or TPUs if required.
4. Setting up roles and bindings if needed for serving models.

Here is a Pulumi program that accomplishes this:

```python
import pulumi
import pulumi_gcp as gcp

# Create a GKE cluster suitable for inference serving.
cluster = gcp.container.Cluster("high-throughput-inference-cluster",
    # Choose an appropriate machine type and node count depending on your serving needs.
    initial_node_count=3,
    min_master_version="latest",
    node_version="latest",
    node_config=gcp.container.ClusterNodeConfigArgs(
        machine_type="n1-standard-4",  # This is an example machine type.
        # If you want to attach GPUs, uncomment the following lines and specify the type and count.
        # oauth_scopes=[
        #     "https://www.googleapis.com/auth/compute",
        #     "https://www.googleapis.com/auth/devstorage.read_only",
        #     "https://www.googleapis.com/auth/logging.write",
        #     "https://www.googleapis.com/auth/monitoring"
        # ],
        # accelerators=[
        #     gcp.container.ClusterNodeConfigAcceleratorArgs(
        #         type="nvidia-tesla-t4",
        #         count=1,
        #     ),
        # ],
        disk_size_gb=100,
        disk_type="pd-standard",
    ),
    # The following settings can be adjusted for better network performance and security.
    network_policy=gcp.container.ClusterNetworkPolicyArgs(
        enabled=True,
        provider="CALICO",
    ),
    ip_allocation_policy=gcp.container.ClusterIpAllocationPolicyArgs(
        use_ip_aliases=True,
    ),
)

pulumi.export('cluster_name', cluster.name)
pulumi.export('cluster_endpoint', cluster.endpoint)
pulumi.export('cluster_master_version', cluster.master_version)
```

This program will:

- Create a new GKE cluster with a basic setup that you can customize according to your needs.
- The `initial_node_count` specifies the number of nodes you want to start within your cluster. You can adjust this number based on the expected workload.
- The `node_config` block allows you to specify the configuration for each of the nodes within the default node pool. In this example, `n1-standard-4` instances are used. If inference workloads require acceleration hardware (like TPUs or GPUs), you would uncomment the `accelerators` argument and specify the type and count.
- The `network_policy` block enables network policies for your GKE cluster. This is useful for controlling traffic flow at the IP address or port level, which is important in a high-throughput serving environment to enforce security and to isolate different workloads from each other.
- The `ip_allocation_policy` being set to use IP aliases ensures that the cluster is set up for VPC-native traffic routing, which aligns with best practices for performance and security.

This code creates the underlying infrastructure required for a high-throughput inference serving system. However, the specific deployment and service configuration for serving your machine learning models would be managed separately using Kubernetes manifests or additional Pulumi code that would define the required Deployment, Services, and potentially Ingress objects within your Kubernetes cluster.

Remember to configure `gcloud` CLI, set the project ID, and authenticate with GCP using `gcloud auth login` before running this Pulumi code. After your GKE cluster is created, you can deploy your inference workload using Kubernetes tools (`kubectl`) or Pulumi.