Unified VPC Networking for Collaborative AI Research Projects

Question

Pulumi · Accepted Answer

In a collaborative AI research environment, it's often necessary to set up a network infrastructure that allows secure, efficient communication between various resources and participants. Unified VPC (Virtual Private Cloud) networking can help isolate resources while providing the necessary connectivity for your AI workloads.

Let’s walk through setting up a unified VPC networking environment on Google Cloud Platform using Pulumi. We'll define a Shared VPC that allows multiple projects to communicate over a single shared network. In GCP, a Shared VPC allows an organization to connect resources from multiple projects to a common Virtual Private Cloud network, making it easier to manage and secure access across those projects.

Here’s what we need to do:

1. Create a Shared VPC Host Project: This is the central GCP project where the shared network resources will live.
2. Create a Shared VPC: This is the network within the host project that you’ll share across the service projects.
3. Attach Service Projects: These are the GCP projects that will leverage the Shared VPC for networking resources.

Before we begin writing the code, ensure you have the Pulumi CLI and GCP SDK setup correctly. You also need the appropriate permissions on GCP to create projects and manage networking resources.

Below is a Pulumi program in Python that sets up a unified networking environment using `gcp.compute.SharedVPCHostProject` for creating a host project and `gcp.compute.SharedVPCServiceProject` for attaching service projects to the Shared VPC.

```python
import pulumi
import pulumi_gcp as gcp

# Replace these variables with your own desired values
host_project_id = "my-shared-vpc-host"
service_project_id = "my-ai-service-project"

# Create a Shared VPC Host Project
host_project = gcp.compute.SharedVPCHostProject(
    "shared-vpc-host-project",
    project=host_project_id,
    # You can add more configuration here such as the billing account
)

# Create the Shared VPC within the Host Project
shared_vpc_network = gcp.compute.Network(
    "shared-vpc-network",
    project=host_project.project,
    auto_create_subnetworks=False  # Use custom subnetworks
)

# Attach a Service Project to the Shared VPC
service_project_attachment = gcp.compute.SharedVPCServiceProject(
    "shared-vpc-service-project",
    host_project=host_project.project,
    service_project=service_project_id
)

# Export the Shared VPC id to provide an output upon completion
pulumi.export("shared_vpc_network_id", shared_vpc_network.id)
```

In the program above, we start by importing the required modules. We define variables for our host and service project IDs.

We create a Shared VPC host using the `gcp.compute.SharedVPCHostProject` resource. This serves as the fundamental container for our VPC network. Then we define the VPC network within this host project with `gcp.compute.Network`, where we set `auto_create_subnetworks` to be `False` as we want to create our subnets manually for granular control.

Lastly, we attach a service project to the Shared VPC using the `gcp.compute.SharedVPCServiceProject` resource. This allows resources within the service project to communicate over the Shared VPC.

After executing `pulumi up` with this program, Pulumi will deploy these resources to GCP and output the Shared VPC network's ID. You can use this ID to reference the network in other parts of your infrastructure, for example, when deploying AI models or datasets that need to be accessed across the projects in the VPC.

By using Pulumi to provision your cloud infrastructure, you can create the networking backbone for your collaborative AI research projects in an easily repeatable and maintainable manner.