Endpoint Services for LLMs within Virtual Private Clouds

Question

Pulumi · Accepted Answer

To establish endpoint services for Language Learning Models (LLMs) within Virtual Private Clouds (VPCs), you would typically need to create a VPC with private subnets where the LLM services will run. Moreover, you would create VPC endpoints to allow connections from clients within the VPC to the LLM services without relying on the public internet, which can enhance security and potentially reduce latency.

Assuming you're working with AWS as your cloud provider, you'd use AWS services like VPC, Lambda for your LLMs, and possibly AWS PrivateLink for private, secure connectivity to services. Below is a Pulumi program written in Python that will:

1. Create a VPC.
2. Set up a Lambda function that could represent an LLM.
3. Establish a VPC endpoint service for the Lambda function.
4. Configure the necessary security groups to control the traffic to and from the Lambda function.

Here's how you can accomplish this:

```python
import pulumi
import pulumi_aws as aws

# Create a new VPC
vpc = aws.ec2.Vpc("llmVpc",
    cidr_block="10.0.0.0/16",
    enable_dns_hostnames=True,
    enable_dns_support=True
)

# Create a subnet within the VPC where the LLM will reside
subnet = aws.ec2.Subnet("llmSubnet",
    vpc_id=vpc.id,
    cidr_block="10.0.1.0/24"
)

# Set up an AWS Lambda function which will act as our LLM service
llm_lambda = aws.lambda_.Function("llmLambda",
    runtime=aws.lambda_.Runtime.PYTHON3_8,
    code=pulumi.FileArchive('./llm-lambda'), # replace with the correct path to your lambda code
    handler="llm_handler.handler", # match with your handler function
    role=iam_role.arn,
    environment=aws.lambda_.FunctionEnvironmentArgs(
        variables={
            "EXAMPLE_VARIABLE": "value",
        },
    ),
    vpc_config=aws.lambda_.FunctionVpcConfigArgs(
        subnet_ids=[subnet.id],
        security_group_ids=[lambda_sg.id]
    )
)

# IAM role for the lambda
iam_role = aws.iam.Role("lambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": {
            "Effect": "Allow",
            "Principal": {"Service": "lambda.amazonaws.com"},
            "Action": "sts:AssumeRole"
        }
    }"""
)

# Lambda execution policy
iam_policy = aws.iam.RolePolicy("lambdaPolicy",
    role=iam_role.id,
    policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "arn:aws:logs:*:*:*"
        }]
    }"""
)

# Create a security group for the Lambda function
lambda_sg = aws.ec2.SecurityGroup("lambdaSg",
    vpc_id=vpc.id,
    description="Allow inbound traffic to LLM Lambda",
    ingress=[
        aws.ec2.SecurityGroupIngressArgs(
            protocol="tcp",
            from_port=0,
            to_port=65535,
            cidr_blocks=["10.0.0.0/16"],
        )
    ],
    egress=[
        aws.ec2.SecurityGroupEgressArgs(
            protocol="-1",
            from_port=0,
            to_port=0,
            cidr_blocks=["0.0.0.0/0"],
        )
    ]
)

# Set up the VPC endpoint service
endpoint_service = aws.ec2.VpcEndpointService("llmEndpointService",
    acceptance_required=False,
    network_load_balancer_arns=[nlb.arn]
)

# Create a Network Load Balancer (NLB) to associate with the endpoint service
nlb = aws.lb.LoadBalancer("llmNlb",
    subnets=[subnet.id],
    internal=True,
    load_balancer_type="network"
)

# Create a target group for the NLB
target_group = aws.lb.TargetGroup("llmTargetGroup",
    vpc_id=vpc.id,
    port=80,
    protocol="TCP",
    target_type="lambda"
)

# Create a listener for the NLB that forwards requests to the target group
listener = aws.lb.Listener("llmListener",
    load_balancer_arn=nlb.arn,
    port=80,
    protocol="TCP",
    default_actions=[aws.lb.ListenerDefaultActionArgs(
        type="forward",
        target_group_arn=target_group.arn
    )]
)

# Register the Lambda function as a target
target = aws.lb.TargetGroupAttachment("llmTarget",
    target_group_arn=target_group.arn,
    target_id=llm_lambda.arn
)

# Output the endpoint service name and NLB DNS name for reference
pulumi.export("endpoint_service_name", endpoint_service.service_name)
pulumi.export("nlb_dns_name", nlb.dns_name)
```

In this program, you are creating a VPC and associated resources that allow you to host a private Lambda function, which acts as an LLM service. The Lambda function is attached to a Network Load Balancer, which is, in turn, associated with an endpoint service to expose the Lambda privately within the VPC.

The security group for the Lambda function is designed to be restrictive, only allowing traffic from within the VPC, and the Lambda execution policy is a minimal policy just allowing it to put logs in CloudWatch for basic monitoring.

**Please make sure that the Lambda function code is present at the path specified for the `FileArchive` and replace the 'llm_handler.handler' with the actual handler you will be using. The IAM role and policy should be refined to grant the necessary permissions required by your specific LLM use case.**

Remember to install Pulumi AWS SDK by running the command below:

```sh
pip install pulumi_aws
```

And then, you could deploy it via Pulumi CLI:

```sh
pulumi up
```

To destroy the created resources:

```sh
pulumi destroy
```

This setup helps you run your LLM within a secure and isolated environment of a VPC, providing a dedicated network setup for your LLM workload.