Multi-VPC Machine Learning Data Access with EC2 Transit Gateway

Question

Pulumi · Accepted Answer

When dealing with a multi-VPC setup in AWS, one of the challenges is enabling different Virtual Private Clouds (VPCs) to communicate with each other in a centralized and efficient manner. This is where AWS Transit Gateway comes into play. It acts as a hub that controls how traffic is routed among all the connected networks which makes managing your network easier.

In the context of machine learning, suppose you have different VPCs for different stages or components of your workflow – one for data collection, another for model training, and perhaps another for inference. An EC2 Transit Gateway allows these distinct VPCs to interact with each other smoothly. For example, your model training VPC needs access to the data collection VPC to retrieve training data.

Below is a Pulumi Python program that sets up an AWS Transit Gateway and attaches two VPCs to it. Each VPC could represent different parts of a machine learning pipeline. Note, the program assumes you have pre-existing VPCs and uses placeholders for their IDs.

```python
import pulumi
import pulumi_aws as aws

# Create the Transit Gateway for connecting multiple VPCs
transit_gateway = aws.ec2transitgateway.TransitGateway("mlDataAccessGateway",
    description="Transit Gateway for ML Data Access between VPCs",
    tags={
        "Name": "MLDataAccessGateway"
    })

# Attach first VPC to the Transit Gateway
vpc_attachment_1 = aws.ec2transitgateway.TransitGatewayVpcAttachment("vpcAttachment1",
    vpc_id="vpc-1",  # Replace with your VPC ID
    subnet_ids=["subnet-1a2b3c", "subnet-1d2e3f"],  # Replace with your Subnet IDs
    transit_gateway_id=transit_gateway.id,
    tags={
        "Name": "VpcAttachment1"
    })

# Attach second VPC to the Transit Gateway
vpc_attachment_2 = aws.ec2transitgateway.TransitGatewayVpcAttachment("vpcAttachment2",
    vpc_id="vpc-2",  # Replace with your VPC ID
    subnet_ids=["subnet-4a5b6c", "subnet-4d5e6f"],  # Replace with your Subnet IDs
    transit_gateway_id=transit_gateway.id,
    tags={
        "Name": "VpcAttachment2"
    })

# Export the IDs of the Transit Gateway and attachments
pulumi.export("transit_gateway_id", transit_gateway.id)
pulumi.export("vpc_attachment_1_id", vpc_attachment_1.id)
pulumi.export("vpc_attachment_2_id", vpc_attachment_2.id)
```

Here's what this Pulumi program does:

1. It creates an AWS Transit Gateway which acts as a network transit hub.
2. It attaches two VPCs to the Transit Gateway. This is where you'd replace the placeholder values with actual VPC and subnet IDs. To connect more VPCs, you'd follow a similar pattern: create additional transit gateway attachments.
3. It exports the IDs of the created resources, helping you to reference them in the future, for example, when writing routing rules or querying resources.

With the above in place, you can set up routing between the VPCs through the Transit Gateway to ensure your VPCs can communicate as needed for your machine learning data access requirements.

Remember to replace the `vpc_id` and `subnet_ids` with actual values from your setup. Also, ensure that your IAM permissions and AWS provider configuration in Pulumi are set up to allow the creation of these resources.

After running this program using the Pulumi CLI, the declared resources will be created and managed by Pulumi, and you will have a base network infrastructure on which you can further build your multi-VPC machine learning environment.