1. ALB for Canary Deployments of New AI Models


    To set up an Application Load Balancer (ALB) in AWS for canary deployments of new AI models, we need to create an ALB with distinct target groups. One target group will serve production traffic, while the other will serve canary traffic, allowing you to slowly introduce and test the new AI model with a subset of users.

    Here's a general explanation of the Pulumi resources we will use:

    1. aws.alb.LoadBalancer: This resource creates the load balancer itself. We'll configure it to be internet-facing so it can receive traffic from users.

    2. aws.alb.TargetGroup: We need two target groups for the two sets of traffic: production and canary. We will attach EC2 instances or ECS services that run the actual AI model application to these target groups.

    3. aws.alb.Listener: The listener checks for connection requests from clients, using the protocol and port that you configure, and forwards requests to one or more target groups based on rules.

    4. aws.alb.ListenerRule: For canary deployment, we'll define rules to determine how traffic is directed between the production and canary target groups. You can specify conditions like HTTP headers, paths, query parameters, etc., to route a certain percentage of traffic to the canary version.

    5. aws.ec2.SecurityGroup: Security groups act as a virtual firewall for your ALB to control inbound and outbound traffic.

    Here's a Python program that uses Pulumi to set up a basic ALB for canary deployments. You'll need to provide your VPC and subnet IDs, and modify the percentage of canary traffic as needed:

    import pulumi import pulumi_aws as aws # Define the security group for the ALB alb_security_group = aws.ec2.SecurityGroup("albSecurityGroup", description="Allow all inbound traffic", vpc_id="YOUR_VPC_ID", ingress=[ aws.ec2.SecurityGroupIngressArgs( protocol="-1", from_port=0, to_port=0, cidr_blocks=[""], ), ], egress=[ aws.ec2.SecurityGroupEgressArgs( protocol="-1", from_port=0, to_port=0, cidr_blocks=[""], ), ]) # Create an external Load Balancer alb = aws.lb.LoadBalancer("appLB", security_groups=[alb_security_group.id], subnets=["YOUR_SUBNET_ID_A", "YOUR_SUBNET_ID_B"], load_balancer_type="application") # Create a target group for production traffic prod_target_group = aws.lb.TargetGroup("prodTargetGroup", port=80, protocol="HTTP", vpc_id="YOUR_VPC_ID") # Create a target group for canary traffic canary_target_group = aws.lb.TargetGroup("canaryTargetGroup", port=80, protocol="HTTP", vpc_id="YOUR_VPC_ID") # Create a listener for the Load Balancer listener = aws.lb.Listener("listener", load_balancer_arn=alb.arn, port=80, default_actions=[aws.lb.ListenerDefaultActionArgs( type="forward", target_group_arn=prod_target_group.arn, )]) # Define canary deployment rules canary_rule = aws.lb.ListenerRule("canaryRule", listener_arn=listener.arn, priority=1, conditions=[aws.lb.ListenerRuleConditionArgs( http_request_method=aws.lb.ListenerRuleConditionHttpRequestMethodArgs( values=["GET", "POST"], )), ], actions=[aws.lb.ListenerRuleActionArgs( type="forward", order=1, target_group_arn=prod_target_group.arn ), aws.lb.ListenerRuleActionArgs( type="forward", order=2, target_group_arn=canary_target_group.arn )]) # Modify the weights to control the percentage of traffic going to the canary prod_weight = aws.lb.TargetGroupAttachment("prodWeight", target_group_arn=prod_target_group.arn, weight=99, # Assuming 99% traffic goes to production id="TARGET_ID_FOR_PROD") canary_weight = aws.lb.TargetGroupAttachment("canaryWeight", target_group_arn=canary_target_group.arn, weight=1, # Assuming 1% traffic goes to canary id="TARGET_ID_FOR_CANARY") # Export the DNS name of the Load Balancer pulumi.export('alb_dns_name', alb.dns_name)

    In this code:

    • Replace YOUR_VPC_ID with your AWS VPC ID where the ALB should be deployed.
    • Replace YOUR_SUBNET_ID_A and YOUR_SUBNET_ID_B with your subnet IDs for high availability.
    • Replace TARGET_ID_FOR_PROD and TARGET_ID_FOR_CANARY with your actual target identifiers to attach to respective target groups.

    The canary deployment is simulated by adjusting the weights for the target group attachments. Here, 99 and 1 represent a 99:1 traffic split between the production and canary target groups. You'll adjust these weights based on the amount of traffic you want to route to the canary deployment. This is a basic approach; in a real-world scenario, you'd use more sophisticated traffic splitting and routing mechanisms.

    After setting up the infrastructure with this Pulumi program, you'd deploy your AI models to the compute resources (like EC2 instances or ECS tasks) registered with these target groups. Then, you could monitor and compare the performance and error rates of the canary AI model against the production model. If everything looks good, you'd gradually increase the weight on the canary target group until the canary becomes the new production.