Deploy containers on ECS and Fargate. Configure task definitions, services, and load balancing. Use when running containerized workloads on AWS.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill aws-ecs-fargate
Run containerized applications on Amazon ECS with Fargate serverless compute or EC2 launch type.
ecs:*, ecr:*, elasticloadbalancing:*, logs:*, iam:PassRole# Create an ECS cluster with Container Insights enabled
aws ecs create-cluster \
--cluster-name production \
--capacity-providers FARGATE FARGATE_SPOT \
--default-capacity-provider-strategy '[
{"capacityProvider": "FARGATE", "weight": 1, "base": 2},
{"capacityProvider": "FARGATE_SPOT", "weight": 3}
]' \
--settings '[{"name": "containerInsights", "value": "enabled"}]'
# List clusters
aws ecs list-clusters
# Describe cluster details
aws ecs describe-clusters --clusters production --include STATISTICS ATTACHMENTS
# Create ECR repository
aws ecr create-repository \
--repository-name myapp \
--image-scanning-configuration scanOnPush=true \
--encryption-configuration encryptionType=KMS
# Authenticate Docker to ECR
aws ecr get-login-password --region us-east-1 | \
docker login --username AWS --password-stdin 123456789012.dkr.ecr.us-east-1.amazonaws.com
# Build, tag, and push
docker build -t myapp:latest .
docker tag myapp:latest 123456789012.dkr.ecr.us-east-1.amazonaws.com/myapp:latest
docker push 123456789012.dkr.ecr.us-east-1.amazonaws.com/myapp:latest
{
"family": "myapp",
"networkMode": "awsvpc",
"requiresCompatibilities": ["FARGATE"],
"cpu": "512",
"memory": "1024",
"executionRoleArn": "arn:aws:iam::123456789012:role/ecsTaskExecutionRole",
"taskRoleArn": "arn:aws:iam::123456789012:role/ecsTaskRole",
"containerDefinitions": [
{
"name": "myapp",
"image": "123456789012.dkr.ecr.us-east-1.amazonaws.com/myapp:latest",
"essential": true,
"portMappings": [
{
"containerPort": 8080,
"protocol": "tcp"
}
],
"environment": [
{"name": "NODE_ENV", "value": "production"},
{"name": "PORT", "value": "8080"}
],
"secrets": [
{
"name": "DB_PASSWORD",
"valueFrom": "arn:aws:secretsmanager:us-east-1:123456789012:secret:myapp/db-password"
},
{
"name": "API_KEY",
"valueFrom": "arn:aws:ssm:us-east-1:123456789012:parameter/myapp/api-key"
}
],
"healthCheck": {
"command": ["CMD-SHELL", "curl -f http://localhost:8080/health || exit 1"],
"interval": 30,
"timeout": 5,
"retries": 3,
"startPeriod": 60
},
"logConfiguration": {
"logDriver": "awslogs",
"options": {
"awslogs-group": "/ecs/myapp",
"awslogs-region": "us-east-1",
"awslogs-stream-prefix": "ecs",
"awslogs-create-group": "true"
}
},
"ulimits": [
{"name": "nofile", "softLimit": 65536, "hardLimit": 65536}
]
}
]
}
# Register the task definition
aws ecs register-task-definition --cli-input-json file://task-definition.json
# List task definition revisions
aws ecs list-task-definitions --family-prefix myapp
# Deregister an old revision
aws ecs deregister-task-definition --task-definition myapp:1
# Create the CloudWatch log group first
aws logs create-log-group --log-group-name /ecs/myapp
aws logs put-retention-policy --log-group-name /ecs/myapp --retention-in-days 30
# Create ECS service with load balancer
aws ecs create-service \
--cluster production \
--service-name myapp \
--task-definition myapp:2 \
--desired-count 3 \
--launch-type FARGATE \
--platform-version LATEST \
--deployment-configuration '{
"deploymentCircuitBreaker": {"enable": true, "rollback": true},
"maximumPercent": 200,
"minimumHealthyPercent": 100
}' \
--network-configuration '{
"awsvpcConfiguration": {
"subnets": ["subnet-private-a", "subnet-private-b"],
"securityGroups": ["sg-app"],
"assignPublicIp": "DISABLED"
}
}' \
--load-balancers '[{
"targetGroupArn": "arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/myapp-tg/abc123",
"containerName": "myapp",
"containerPort": 8080
}]' \
--service-registries '[{
"registryArn": "arn:aws:servicediscovery:us-east-1:123456789012:service/srv-abc123"
}]' \
--enable-execute-command \
--propagate-tags SERVICE
# Rolling update - update task definition and force new deployment
aws ecs update-service \
--cluster production \
--service myapp \
--task-definition myapp:3 \
--force-new-deployment
# Watch deployment progress
aws ecs describe-services \
--cluster production \
--services myapp \
--query "services[0].deployments[].{Status:status,Running:runningCount,Desired:desiredCount,TaskDef:taskDefinition}" \
--output table
# Wait for service to stabilize
aws ecs wait services-stable --cluster production --services myapp
# Exec into a running container for debugging
aws ecs execute-command \
--cluster production \
--task arn:aws:ecs:us-east-1:123456789012:task/production/abc123 \
--container myapp \
--interactive \
--command "/bin/sh"
# Register ECS service as a scalable target
aws application-autoscaling register-scalable-target \
--service-namespace ecs \
--resource-id service/production/myapp \
--scalable-dimension ecs:service:DesiredCount \
--min-capacity 2 \
--max-capacity 20
# Target tracking policy - scale on CPU utilization
aws application-autoscaling put-scaling-policy \
--service-namespace ecs \
--resource-id service/production/myapp \
--scalable-dimension ecs:service:DesiredCount \
--policy-name cpu-target-tracking \
--policy-type TargetTrackingScaling \
--target-tracking-scaling-policy-configuration '{
"PredefinedMetricSpecification": {
"PredefinedMetricType": "ECSServiceAverageCPUUtilization"
},
"TargetValue": 70.0,
"ScaleInCooldown": 300,
"ScaleOutCooldown": 60
}'
# Scale on request count per target (ALB)
aws application-autoscaling put-scaling-policy \
--service-namespace ecs \
--resource-id service/production/myapp \
--scalable-dimension ecs:service:DesiredCount \
--policy-name request-count-tracking \
--policy-type TargetTrackingScaling \
--target-tracking-scaling-policy-configuration '{
"PredefinedMetricSpecification": {
"PredefinedMetricType": "ALBRequestCountPerTarget",
"ResourceLabel": "app/my-alb/abc123/targetgroup/myapp-tg/def456"
},
"TargetValue": 1000.0,
"ScaleInCooldown": 300,
"ScaleOutCooldown": 60
}'
resource "aws_ecs_cluster" "main" {
name = "production"
setting {
name = "containerInsights"
value = "enabled"
}
}
resource "aws_ecs_task_definition" "myapp" {
family = "myapp"
network_mode = "awsvpc"
requires_compatibilities = ["FARGATE"]
cpu = 512
memory = 1024
execution_role_arn = aws_iam_role.ecs_execution.arn
task_role_arn = aws_iam_role.ecs_task.arn
container_definitions = jsonencode([{
name = "myapp"
image = "${aws_ecr_repository.myapp.repository_url}:latest"
essential = true
portMappings = [{
containerPort = 8080
protocol = "tcp"
}]
logConfiguration = {
logDriver = "awslogs"
options = {
awslogs-group = aws_cloudwatch_log_group.myapp.name
awslogs-region = "us-east-1"
awslogs-stream-prefix = "ecs"
}
}
healthCheck = {
command = ["CMD-SHELL", "curl -f http://localhost:8080/health || exit 1"]
interval = 30
timeout = 5
retries = 3
startPeriod = 60
}
}])
}
resource "aws_ecs_service" "myapp" {
name = "myapp"
cluster = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.myapp.arn
desired_count = 3
launch_type = "FARGATE"
deployment_circuit_breaker {
enable = true
rollback = true
}
network_configuration {
subnets = aws_subnet.private[*].id
security_groups = [aws_security_group.app.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.myapp.arn
container_name = "myapp"
container_port = 8080
}
enable_execute_command = true
propagate_tags = "SERVICE"
}
# Tail logs from CloudWatch
aws logs tail /ecs/myapp --follow --since 1h
# Get logs for a specific task
aws logs get-log-events \
--log-group-name /ecs/myapp \
--log-stream-name "ecs/myapp/abc123def456" \
--start-from-head
# Filter logs for errors
aws logs filter-log-events \
--log-group-name /ecs/myapp \
--filter-pattern "ERROR" \
--start-time $(date -d '1 hour ago' +%s000)
| Problem | Cause | Fix |
|---|---|---|
| Task stuck in PROVISIONING | No available capacity in subnets | Check subnet availability and capacity provider |
| Task fails immediately | Container crashes on startup | Check CloudWatch logs; run image locally first |
| Health check failing | App not ready within startPeriod | Increase startPeriod; verify health endpoint |
| Cannot pull ECR image | Execution role missing ECR permissions | Attach AmazonECSTaskExecutionRolePolicy |
| Service stuck at 0 running | Security group blocks ALB health check | Allow ALB SG to reach container port |
| Exec command fails | SSM agent not initialized | Ensure enableExecuteCommand is true; check task role |
| High Fargate costs | Not using Fargate Spot for tolerant workloads | Add FARGATE_SPOT capacity provider |
| Container OOM killed | Memory limit too low | Increase memory in task definition; check for leaks |
| Slow deployments | minimumHealthyPercent too high | Set to 50% for faster rolling updates |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
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Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
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Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
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