1. Hybrid Cloud AI workloads with Shared VPC


    When working with hybrid cloud AI workloads that span across public and private cloud environments, you may need to implement a network architecture that enables secure and efficient data exchange between computing resources. Shared Virtual Private Cloud (VPC) is a cloud networking design that allows for such integrations, often essential for AI workloads that require significant processing power and data-sharing capabilities across environments.

    In the context of Google Cloud Platform (GCP), a shared VPC allows you to connect resources from multiple projects to a common VPC network so that they can communicate with each other securely and efficiently using internal IP addresses. This setup is particularly beneficial for AI workflows where you might run computations on private on-premises hardware (or in a separate cloud environment) while managing data storage, analytics, and other services in GCP.

    To set up a shared VPC in GCP with Pulumi, you would typically create a SharedVPCHostProject which designates a project as the host for the shared VPC network, and then associate other service projects to this host using SharedVPCServiceProject. You would also configure the necessary networking resources such as Network and NetworkPeering if needed.

    Here's a Pulumi program written in Python that demonstrates how to set up a shared VPC in GCP for hybrid AI workloads:

    import pulumi import pulumi_gcp as gcp # Host project for the Shared VPC # This is where the shared network will be created and managed. host_project_id = "your-host-project-id" shared_vpc_host_project = gcp.compute.SharedVPCHostProject("shared-vpc-host", project=host_project_id) # Create a VPC network in the host project vpc_network_name = "shared-vpc-network" vpc_network = gcp.compute.Network(vpc_network_name, auto_create_subnetworks=False, # Custom subnetworks will be manually created if needed routing_mode="REGIONAL", project=shared_vpc_host_project.project) # Service project that participates in the Shared VPC # Resources from this project can use the VPC network of the host project. service_project_id = "your-service-project-id" shared_vpc_service_project = gcp.compute.SharedVPCServiceProject("shared-vpc-service", host_project=shared_vpc_host_project.project, service_project=service_project_id) # (Optional) Set up VPC peering between GCP and other cloud providers if hybrid connectivity is needed # Replace `peer_network` with the network resource URL from the other cloud provider network_peering_name = "vpc-peering-example" network_peering = gcp.compute.NetworkPeering(network_peering_name, network=vpc_network.self_link, peer_network="other-cloud-provider-vpc-network-url") # Export the network details pulumi.export("shared_vpc_host_project_id", shared_vpc_host_project.project) pulumi.export("shared_vpc_id", vpc_network.id) pulumi.export("shared_vpc_service_project_id", shared_vpc_service_project.service_project) pulumi.export("network_peering_name", network_peering.name)


    • SharedVPCHostProject: Designates which GCP project will act as the host for the shared VPC. This allows you to centralize networking resources in a single project for better management.
    • Network: Defines the shared VPC network where your resources will be located. You can set properties like whether to automatically create subnetworks or the routing mode.
    • SharedVPCServiceProject: Allows you to associate a service project with the shared VPC host so that the service project's resources can access the shared VPC.
    • NetworkPeering: (Optional) If you need to set up peering between your GCP VPC and VPC networks in other cloud providers, you can create a NetworkPeering. This is particularly useful for hybrid clouds where workloads are distributed across multiple environments.

    This configuration enables the host project to share its network with the service project. Resources created in the service project, such as Compute Engine instances, can use the shared VPC network for internal communication, enjoying lower latency and secure data transmission.

    Keep in mind that the above Pulumi program only outlines the network connectivity aspects for hybrid AI workloads. Depending on the specific needs and services utilized, additional resources like IAM roles, data storage, and machine learning APIs may also need to be configured.