1. Balanced Cost and Performance for ECS-Based AI Workloads


    Creating a cloud infrastructure that balances cost and performance can be a challenging yet pivotal aspect for running AI workloads. Pulumi's infrastructure-as-code allows us to define and deploy such infrastructure programmatically. As your requirement suggests, you want a setup on AWS using ECS (Elastic Container Service), which is a managed container orchestration service. Here's how you could do it:

    1. Use ECS Fargate: AWS ECS supports two launch types: EC2 and Fargate. Fargate is a serverless compute engine that allows you to run containers without having to manage servers or clusters. It offers a good balance between cost and performance as you only pay for the resources your containers use.

    2. Spot Instances: For workloads that can tolerate interruptions, you could use Spot Instances which can save up to 90% over On-Demand prices. This is especially useful for batch processing jobs which are common in AI workloads.

    3. Auto Scaling: Auto Scaling ensures that you have the right number of Amazon ECS tasks running to handle the load of your application. This helps in improving performance during high workload times and reducing cost when the demand is low.

    4. Infrastructure-as-code with Pulumi: We will write a Pulumi program to define and deploy our ECS services which will use Fargate and Spot Instances for running an AI application.

    Below is a Pulumi Python program for deploying an ECS-based AI workload that is optimized for both cost and performance. The Pulumi program provides the needed resources, including an ECS cluster, a task definition with Fargate and Spot configuration, and an Auto Scaling policy.

    import pulumi import pulumi_aws as aws # Define a new ECS cluster where our services will be hosted. ecs_cluster = aws.ecs.Cluster( "ecs-cluster", capacity_providers=["FARGATE", "FARGATE_SPOT"], settings=[{ "name": "containerInsights", "value": "enabled" }] ) # Create an IAM role for the ECS task with the necessary permissions. task_execution_role = aws.iam.Role( "task-execution-role", assume_role_policy=aws.iam.get_policy_document( statements=[{ "actions": ["sts:AssumeRole"], "effect": "Allow", "principals": [{ "identifiers": ["ecs-tasks.amazonaws.com"], "type": "Service", }], }] ).json ) # Attach the Amazon ECS task execution role policy to the IAM role. task_execution_role_policy = aws.iam.RolePolicyAttachment( "task-execution-role-policy", role=task_execution_role, policy_arn="arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy" ) # Define a task definition with a container to run. task_definition = aws.ecs.TaskDefinition( "task-definition", family="family", cpu="256", # Adjust according to your workload requirements. memory="512", # Adjust according to your workload requirements. network_mode="awsvpc", requires_compatibilities=["FARGATE"], execution_role_arn=task_execution_role.arn, container_definitions=pulumi.Output.all().apply(lambda _: pulumi.Json([ { "name": "my-container", "image": "my_image", # Specify your AI workload container image here. "cpu": 256, "memory": 512, "essential": True, "portMappings": [{ "containerPort": 80, "hostPort": 80 }], # Add other required configurations. } ])) ) # Create a service to run the task definition created above. ecs_service = aws.ecs.Service( "ecs-service", cluster=ecs_cluster.arn, task_definition=task_definition.arn, launch_type="FARGATE", capacity_provider_strategies=[{ "capacityProvider": "FARGATE_SPOT", "weight": 1, }], desired_count=2, # Define how many tasks you want to run concurrently. network_configuration={ "assignPublicIp": True, "subnets": ["subnet-id"] # Substitute with your own subnet IDs. }, load_balancers=[{ "targetGroupArn": "arn:aws:elasticloadbalancing:region:123456789012:targetgroup/target-group-name", "containerName": "my-container", "containerPort": 80, }], # Specify additional ECS service configurations if needed. ) # Define Auto Scaling policy for the ECS service. scaling_policy = aws.appautoscaling.Policy( "scaling-policy", resource_id=ecs_service.arn, scalable_dimension="ecs:service:DesiredCount", service_namespace="ecs", policy_type="TargetTrackingScaling", target_tracking_scaling_policy_configuration={ "target_value": 75.0, "predefined_metric_specification": { "predefinedMetricType": "ECSServiceAverageCPUUtilization", }, }, # Define more properties here as needed. ) # Export the cluster name and service name as stack outputs. pulumi.export('cluster_name', ecs_cluster.name) pulumi.export('service_name', ecs_service.name)

    This Pulumi program uses aws.ecs.Cluster to create an ECS cluster that can use both AWS Fargate and Fargate Spot as capacity providers. Then an IAM role is created for tasks to give ECS tasks the required permissions.

    An ECS task definition is created with a container that would run your AI application; make sure to replace "my_image" with your container image. Then, we define an ECS service which uses Fargate Spot for cost optimization and set the desired task count.

    We've also attached an application auto scaling policy that allows the service to scale in and out based on CPU utilization, this ensures performance stays consistent during changing workloads.

    Adjust the CPU and memory settings according to your workload's requirements. Also, provide the correct image name for your AI workload and specify the subnet IDs for the network configuration.

    Deploying this Pulumi program will create an AWS ECS setup optimized for cost and performance balance, which is ideal for AI workloads that do not require continuous compute power or have flexible start and end times.