1. Highly Available Inference Endpoints on ECS Clusters


    To deploy highly available inference endpoints on ECS (Elastic Container Service) clusters using Pulumi, you'll need a combination of AWS resources, which may include an ECS cluster, ECS service, task definition, and load balancing mechanisms. The task definition will define the Docker images and configuration for your inference server, while the service will ensure that a specified number of task instances are running and registering with a load balancer if necessary.

    ECS can be used in combination with other AWS services such as Elastic Load Balancing (ELB) to distribute traffic across the ECS tasks to provide high availability. ELBs can be used to handle incoming traffic and distribute it across multiple containers hosted on the ECS cluster, providing fault tolerance and increasing reliability.

    Here is a Pulumi program in Python to create a highly available inference endpoint on an AWS ECS cluster:

    import pulumi import pulumi_aws as aws # Create an ECS cluster where the inference services will run. cluster = aws.ecs.Cluster("inference_cluster") # Assume we have a load balancer set up. Here, we reference its ARN and listener ARN # which would be created beforehand or outside of this snippet. # Replace these with your actual ARN if available. load_balancer_arn = pulumi.Input("arn:aws:elasticloadbalancing:us-west-2:123456789012:loadbalancer/app/my-load-balancer/50dc6c495c0c9188") listener_arn = pulumi.Input("arn:aws:elasticloadbalancing:us-west-2:123456789012:listener/app/my-loadbalancer/50dc6c495c0c9188") # Create a task definition for the inference application. Replace the image with your inference Docker image. task_definition = aws.ecs.TaskDefinition("inference_task_def", family="service", cpu="256", memory="512", network_mode="awsvpc", requires_compatibilities=["FARGATE"], execution_role_arn=aws.iam.Role("ecs_execution_role").arn, container_definitions=pulumi.Output.all(cluster=cluster.name).apply( lambda args: f""" [ {{ "name": "inference-container", "image": "your-inference-image:latest", "portMappings": [ {{ "containerPort": 80, "hostPort": 80 }} ] }} ] """ ) ) # Create a service to run and maintain the desired count of tasks in the ECS cluster. service = aws.ecs.Service("inference_service", cluster=cluster.arn, desired_count=3, # This will ensure that 3 instances of the task are running for high availability. launch_type="FARGATE", task_definition=task_definition.arn, network_configuration=aws.ecs.ServiceNetworkConfigurationArgs( subnets=["subnet-abcdefgh", "subnet-12345678"], # Replace with your VPC subnet IDs. security_groups=["sg-0123456789abcdefg"], # Replace with your security group. assign_public_ip=True, ), load_balancers=[aws.ecs.ServiceLoadBalancerArgs( target_group_arn=load_balancer_arn, container_name="inference-container", container_port=80 )], depends_on=[listener_arn] ) # Export the ECS service name and cluster name. pulumi.export("cluster_name", cluster.name) pulumi.export("service_name", service.name)

    This Pulumi program creates an ECS cluster and a Fargate service with a desired count of three tasks. Fargate allows you to run containers without having to manage servers or clusters. The program specifies the task definition with the necessary CPU and memory required to run your inference container, along with the location of your Docker image.

    It also creates an ECS service designed to maintain a specified number of instances of the task definition within the ECS cluster. It assumes you have a load balancer set up, with this service registering tasks with the load balancer to ensure traffic is distributed across the available instances for high availability.

    Please replace placeholders such as "your-inference-image:latest", subnet IDs, and security group IDs with actual values that correspond to your infrastructure.

    You can export additional outputs like the load balancer DNS name using pulumi.export to make them easily accessible outside of Pulumi.