Securely Connect Multiple EC2 VPCs for Distributed ML Training

Question

Pulumi · Accepted Answer

To securely connect multiple EC2 VPCs for distributed ML training, you would typically establish a network infrastructure that allows VPCs to communicate securely with each other. AWS Transit Gateway is a service that enables customers to connect their Amazon Virtual Private Clouds (VPCs) and their on-premises networks to a single gateway. With AWS Transit Gateway, you can simplify your network and put an end to complex peering relationships. It acts as a cloud router – each new connection is only made once.

The following Pulumi program demonstrates how you can create an AWS Transit Gateway and attach it to multiple VPCs. In this example, we'll create two VPCs and an AWS Transit Gateway, then we will attach the VPCs to the Transit Gateway, which can be used for distributed ML training that needs inter VPC communication:

```python
import pulumi
import pulumi_aws as aws

# Create VPCs for distributed ML training
vpc1 = aws.ec2.Vpc("vpc1",
    cidr_block="10.1.0.0/16",
    enable_dns_hostnames=True,
    enable_dns_support=True)

vpc2 = aws.ec2.Vpc("vpc2",
    cidr_block="10.2.0.0/16",
    enable_dns_hostnames=True,
    enable_dns_support=True)

# Create an AWS Transit Gateway
tgw = aws.ec2transitgateway.TransitGateway("tgw",
    description="Transit Gateway for Distributed ML Training")

# Attach the VPCs to the AWS Transit Gateway
attachment1 = aws.ec2transitgateway.VpcAttachment("attachment1",
    vpc_id=vpc1.id,
    transit_gateway_id=tgw.id,
    subnet_ids=vpc1.public_subnets.apply(lambda subnets: [s.id for s in subnets]))

attachment2 = aws.ec2transitgateway.VpcAttachment("attachment2",
    vpc_id=vpc2.id,
    transit_gateway_id=tgw.id,
    subnet_ids=vpc2.public_subnets.apply(lambda subnets: [s.id for s in subnets]))

# Export necessary information
pulumi.export("vpc1_id", vpc1.id)
pulumi.export("vpc2_id", vpc2.id)
pulumi.export("transit_gateway_id", tgw.id)
```

In the above program, we start by importing `pulumi` and the `pulumi_aws` module. Then we proceed to create two VPCs with different CIDR blocks — this is where the distributed ML workloads would operate. `enable_dns_hostnames` and `enable_dns_support` are set to `True` to allow DNS resolution within and between the VPCs.

We define an AWS Transit Gateway using `aws.ec2transitgateway.TransitGateway`, which acts as a cloud router. Once the Transit Gateway is provisioned, we attach our VPCs to it using `aws.ec2transitgateway.VpcAttachment`. To do this, we provide the ID of each VPC and the IDs of their public subnets.

Finally, we export some of the resource IDs (like the VPC IDs and the Transit Gateway ID) so they can be easily retrieved and used in other parts of your infrastructure or applications.

By establishing a Transit Gateway and connecting the VPCs to it, we've built a hub-and-spoke network topology that allows secure communication for distributed ML training workloads across the two VPCs.