Secure Model Training Data Transfer with GCP VPN Tunnel

Question

Pulumi · Accepted Answer

To set up a secure environment for transferring your model training data between your on-premises network and Google Cloud Platform (GCP), you can use a VPN tunnel. A VPN tunnel encrypts the data traffic between your site and Google Cloud, allowing for secure data transfer.

In this Pulumi program for GCP in Python, we will use the following resources:
- `VPNGateway`: A virtual private network (VPN) gateway on GCP that sends encrypted traffic between your virtual private cloud (VPC) and your on-premises network.
- `VPNTunnel`: The VPN tunnel resource represents an actual secure connection over which the data will travel between your GCP VPN gateway and your on-premises gateway.
- `Router`: A router resource on GCP is needed to create routes that direct traffic between your VPC and the VPN tunnel.

When setting up the VPN, you'll also need to configure the following:
- `Network`: The VPC network that you will connect to the VPN.
- `Subnetwork`: The subnetwork within your VPC where the resources will reside.
- `ExternalVPNGateway`: If you are connecting to an external (on-premises) VPN gateway, you need to define this gateway in your configuration to establish the connection.

Here's a Pulumi program, written in Python, that will create the necessary resources for a secure VPN tunnel on GCP:

```python
import pulumi_gcp as gcp

# Replace these variables with your own information
project_id = 'your-gcp-project-id'
network_name = 'your-vpc-network-name'
region = 'your-gcp-region'
vpn_gateway_name = 'your-vpn-gateway-name'
vpn_tunnel_name = 'your-vpn-tunnel-name'
router_name = 'your-router-name'
# The IP address of your on-premises VPN gateway
on_prem_gateway_ip = 'your-on-prem-vpn-gateway-ip'

# Create a GCP network
network = gcp.compute.Network(network_name,
                              auto_create_subnetworks=False,
                              project=project_id)

# Create a subnetwork
subnetwork = gcp.compute.Subnetwork('your-subnetwork',
                                    network=network.id,
                                    region=region,
                                    ip_cidr_range='10.0.1.0/24',  # Replace with the desired CIDR range
                                    project=project_id)

# Create a GCP VPN gateway
vpn_gateway = gcp.compute.VPNGateway(vpn_gateway_name,
                                     region=region,
                                     network=network.id,
                                     project=project_id)

# Create an external VPN gateway that represents your on-premises VPN gateway
external_vpn_gateway = gcp.compute.ExternalVPNGateway('your-external-vpn-gateway',
                                                      candidate_subnets=['your-candidate-subnet'],  # replace with a subnet if applicable
                                                      project=project_id)

# Create a VPN tunnel that connects the GCP VPN gateway and your on-premises VPN gateway
vpn_tunnel = gcp.compute.VPNTunnel(vpn_tunnel_name,
                                   vpn_gateway=vpn_gateway.id,
                                   peer_external_gateway=external_vpn_gateway.id,
                                   region=region,
                                   shared_secret='your-shared-secret',  # Replace with the shared secret
                                   project=project_id)

# Create a router connected to the VPN gateway
router = gcp.compute.Router(router_name,
                            network=network.id,
                            region=region,
                            project=project_id,
                            bgp=gcp.compute.RouterBgpArgs(asn=64514))  # ASN you want to use for BGP

# Create a BGP peer, if you plan to use dynamic routing with BGP
bgp_peer = gcp.compute.RouterPeer('your-bgp-peer',
                                  router=router.name,
                                  region=region,
                                  ip_address=vpn_tunnel.router_ip_address,
                                  peer_ip_address=on_prem_gateway_ip,
                                  peer_asn=12345,  # The ASN of your on-premises VPN gateway
                                  project=project_id)

# The following exports are useful for obtaining information about created resources
pulumi.export('VPN Gateway Name', vpn_gateway.name)
pulumi.export('VPN Tunnel Name', vpn_tunnel.name)
pulumi.export('Router Name', router.name)
pulumi.export('External VPN Gateway', external_vpn_gateway.name)
```

This program initializes the required resources for a secure VPN connection. It creates a VPC network, a VPN gateway on GCP, and a tunnel with an external gateway that represents your on-premises gateway. Additionally, it sets up a router for managing traffic between GCP and your on-premises site, with an optional BGP peer configuration for dynamic routing.

Make sure to replace the placeholder values with the actual details of your on-premises VPN setup and desired GCP configuration.

You will need to have a GCP service account with the appropriate permissions and you should have configured your Pulumi environment to authenticate with GCP using this service account.

This configuration assumes you have a routing mechanism (like BGP) and an external VPN gateway set up on-premises. It's essential you work with those responsible for the on-premises network to ensure that all configurations are compatible and that you use the correct IP ranges, ASN, and shared secret.