1. Distributed AI Workloads with Hybrid Cloud via GCP VPNTunnel


    To set up a reliable and secure connection for distributed AI workloads with a hybrid cloud configuration using Google Cloud Platform (GCP), we can leverage a VPN Tunnel. A VPN Tunnel establishes a private link between your on-premises network and your virtual network within GCP, ensuring that the data exchanged between these networks is encrypted and isolated from public internet traffic.

    Here's what we need to create to set up the VPN tunnel in GCP:

    1. Google Compute Engine VPN Gateway (google-native.compute/v1.VpnGateway): The VPN gateway on the GCP side that manages the VPN connection.

    2. VPN Tunnel (google-native.compute/v1.VpnTunnel): The actual VPN tunnel that securely connects your on-premises network to the VPC network in GCP.

    3. Router (google-native.compute/v1.Router): A cloud router to direct traffic between your VPC and on-premises networks through the VPN tunnel.

    Below is a detailed Python program using Pulumi that defines these resources:

    import pulumi import pulumi_google_native as google_native # Replace these variables with your actual network and project configuration. project = 'my-gcp-project' region = 'us-central1' network_name = 'my-vpc-network' router_name = 'my-cloud-router' vpn_gateway_name = 'my-vpn-gateway' vpn_tunnel_name = 'my-vpn-tunnel' peer_ip = 'YOUR_ON_PREM_PEER_IP' # Create the VPN Gateway that will manage the VPN connection on GCP side. vpn_gateway = google_native.compute.v1.VpnGateway( vpn_gateway_name, project=project, region=region, network=pulumi.Input(f'projects/{project}/global/networks/{network_name}'), description='VPN Gateway for the AI Workload Hybrid Cloud') # Create the Cloud Router to manage traffic routing. router = google_native.compute.v1.Router( router_name, project=project, region=region, network=pulumi.Input(f'projects/{project}/global/networks/{network_name}'), description='Router to direct traffic between VPC and on-premises network') # Create the VPN Tunnel that establishes the secure connection. vpn_tunnel = google_native.compute.v1.VpnTunnel( vpn_tunnel_name, project=project, region=region, vpn_gateway=vpn_gateway.self_link, peer_ip=peer_ip, shared_secret='YOUR_SECRET', router=router.self_link, ike_version=2, description='The tunnel for the AI Workload Hybrid Cloud') # Export the URL of the created VPN Gateway pulumi.export('vpn_gateway_url', vpn_gateway.self_link) # Export the URL of the created VPN Tunnel pulumi.export('vpn_tunnel_url', vpn_tunnel.self_link)

    This code does the following:

    • It declares the necessary resources for setting up a VPN tunnel with Google Cloud Platform.
    • vpn_gateway is created to manage VPN connections, and a self-link is provided, which is a URL that uniquely identifies this resource.
    • router is responsible for routing traffic over the VPN, and again a self-link is exported for reference.
    • vpn_tunnel connects your GCP network with your on-premises network. You'll need to specify the remote peer IP address and configure a shared secret for IKE setup. The ike_version parameter is set to 2, as IKEv2 is generally recommended for better security.

    Make sure to replace placeholder values like YOUR_ON_PREM_PEER_IP and YOUR_SECRET with the actual IP address of your on-premises VPN and a strong pre-shared secret. Also, update the project, region, network, and other related configuration to match your environment's specifics.

    When you run this script using the Pulumi CLI, Pulumi will provision these resources in the correct order, ensuring the dependencies between resources are respected.

    The outputs vpn_gateway_url and vpn_tunnel_url provide you with direct links to the created resources for easy access and management.

    Remember, this configuration assumes you have already set up the corresponding VPN configuration on your on-premises side, which matches the settings provided here, including the shared secret for authentication.