1. Directing Ingress and Egress for EC2-based Inference Endpoints

    Python

    To direct ingress and egress traffic for EC2-based inference endpoints, we will use a combination of AWS resources that includes Security Groups and optionally VPC Endpoints if you want to control the traffic going to AWS services. Here's how these resources can be utilized:

    • Security Groups: Acts as a virtual firewall for your EC2 instances to control inbound and outbound traffic. Ingress and egress rules can be specified based on protocols, ports, and source/destination IP ranges or other security groups.
    • VPC Endpoints (optional): Allows you to privately connect your VPC to supported AWS services and VPC endpoint services powered by PrivateLink without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. Instances in your VPC do not require public IP addresses to communicate with resources in the service.

    The following Pulumi program in Python sets up a security group for an EC2 instance, which can be part of an inference endpoint. It will create rules to allow inbound HTTP and HTTPS traffic and restrict all outbound traffic except for the necessary ports for inference services or to specific IP ranges or other security groups if needed.

    Note that you may need to adjust the ingress and egress rules according to the specific requirements of your application.

    import pulumi import pulumi_aws as aws # Create a VPC - adjust your CIDR block as necessary vpc = aws.ec2.Vpc("example-vpc", cidr_block="10.0.0.0/16") # Create an Internet Gateway for the VPC internet_gateway = aws.ec2.InternetGateway("example-igw", vpc_id=vpc.id) # Create a Security Group within the VPC for controlling access to EC2 instances security_group = aws.ec2.SecurityGroup("inference-endpoint-sg", vpc_id=vpc.id, description="Allow inbound HTTP and HTTPS traffic", ingress=[ # Your application may require different ports; adjust as needed. aws.ec2.SecurityGroupIngressArgs( protocol="tcp", from_port=80, to_port=80, cidr_blocks=["0.0.0.0/0"], description="Allow HTTP access from anywhere", ), aws.ec2.SecurityGroupIngressArgs( protocol="tcp", from_port=443, to_port=443, cidr_blocks=["0.0.0.0/0"], description="Allow HTTPS access from anywhere", ), ], egress=[ # Restrict all outbound traffic except for necessary ports # This is just an example; you'll need to tailor it to your requirements aws.ec2.SecurityGroupEgressArgs( protocol="-1", from_port=0, to_port=0, cidr_blocks=["0.0.0.0/0"], description="Allow all outbound traffic to specific ports only", ), ], tags={ "Name": "Inference Endpoint Security Group", }, ) # Export the Security Group ID pulumi.export('security_group_id', security_group.id)

    In this program, we created an EC2 VPC and an Internet Gateway to allow communication between the instances and the outside world. Then we defined a Security Group inference-endpoint-sg which specifies that our EC2 instances can receive HTTP and HTTPS traffic (ports 80 and 443 are open to all, indicated by 0.0.0.0/0). The egress rule allows all outbound traffic. Again, you should tailor the egress rules to your use case, perhaps limiting outbound traffic to only the necessary services and ports.

    You can attach this security group to your EC2 instances when you launch them.

    To make this EC2 instance part of an "inference endpoint," you will also need to deploy your machine learning model and expose it through an HTTP server (like Flask or Django for Python applications) running on these instances.

    Additionally, consider using other AWS services like Auto Scaling Groups to manage the EC2 instances, Elastic Load Balancing to distribute traffic, and Amazon RDS or DynamoDB for database needs. Remember that managing inference endpoints at scale may require more complex orchestration, monitoring, and security considerations, which involve additional AWS services and Pulumi components.


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