diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000..c1dba23 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,9 @@ +## Summary + + +## Test plan +- [ ] ... + +## Documentation +- [ ] Updated relevant docs in `misc/website/docs/` (if adding/changing examples or architecture) +- [ ] Docs site builds cleanly (`cd misc/website && npm ci && npm run build`) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..e459b60 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,52 @@ +name: docs + +on: + push: + branches: + - main + paths: + - "misc/website/**" + - "examples/**/README.md" + - ".github/workflows/docs.yml" + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + cache-dependency-path: misc/website/package-lock.json + + - run: npm ci + working-directory: misc/website + + - run: npm run build + working-directory: misc/website + + - uses: actions/upload-pages-artifact@v3 + with: + path: misc/website/build + + deploy: + needs: build + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - id: deployment + uses: actions/deploy-pages@v4 diff --git a/.gitignore b/.gitignore index e3fe715..921e650 100644 --- a/.gitignore +++ b/.gitignore @@ -54,3 +54,8 @@ examples/pod-autoscaling/keda/vllm-qwen3/model-qwen3-4b-fp8-with-sqs.yaml # Claude Generated Artifacts for Management of Repo PLAN.md claude-md/ + +# Docusaurus +misc/website/node_modules/ +misc/website/build/ +misc/website/.docusaurus/ diff --git a/README.md b/README.md index e29d2ed..6f932c0 100644 --- a/README.md +++ b/README.md @@ -4,37 +4,35 @@ - [Overview](#overview) - [Prerequisites](#prerequisites) - [Quick Start](#quick-start) -- [Architecture](#architecture) - [Examples](#examples) - [Cleanup](#cleanup) -- [Security Considerations](#security-considerations) +- [Configuration](#configuration) +- [Components](#components) +- [Learn More](#learn-more) - [Contributing](#contributing) - [License and Disclaimer](#license-and-disclaimer) ## Overview -[Amazon EKS Auto Mode](https://aws.amazon.com/eks/auto-mode/) simplifies Kubernetes cluster management on AWS. Key benefits include: -🚀 **Simplified Management** -- One-click cluster provisioning -- Automated compute, storage, and networking -- Seamless integration with AWS services +[Amazon EKS Auto Mode](https://aws.amazon.com/eks/auto-mode/) simplifies Kubernetes cluster management by automating compute, storage, and networking decisions. Under the hood it runs Karpenter, the AWS Load Balancer Controller, and the EBS CSI driver as managed components — you get the benefits without installing or upgrading any of them. -⚡ **Workload Support** -- Graviton instances for optimal price-performance -- GPU acceleration for ML/AI workloads -- Inferentia2 for cost-effective ML inference -- Mixed architecture support +This repository is an educational companion. Each example demonstrates a specific EKS Auto Mode pattern (Graviton, GPU, Spot, ODCR targeting, disruption budgets, etc.) with a self-contained README explaining the "why" alongside the "how." Deploy the base cluster once, then apply individual examples to explore. -🔧 **Infrastructure Features** -- Auto-scaling with Karpenter -- Automated load balancer configuration -- Cost optimization through node consolidation +**Key capabilities covered:** -This repository provides a production-ready template for deploying various workloads on EKS Auto Mode. +- Graviton (ARM64) and x86 workloads side by side +- GPU and Inferentia2 (Neuron) ML inference +- Spot and On-Demand mixed pools with overprovision headroom +- On-Demand Capacity Reservation targeting +- Static capacity pools and disruption budgets +- HPA and KEDA-driven autoscaling +- KMS encryption for ephemeral node storage +- CloudWatch Container Insights observability +- 5-layer resource tagging for cost allocation ## Prerequisites -🛠️ **Required Tools** +**Required Tools:** - [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html) - [kubectl](https://kubernetes.io/docs/tasks/tools/) - [Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli) @@ -45,20 +43,13 @@ This repository provides a production-ready template for deploying various workl 1. **Clone Repository**: ```bash -# Get the code git clone https://github.com/aws-samples/sample-aws-eks-auto-mode.git cd sample-aws-eks-auto-mode - -# Configure remote -git remote set-url origin https://github.com/aws-samples/sample-aws-eks-auto-mode.git ``` 2. **Deploy Cluster**: ```bash -# Navigate to Terraform directory cd terraform - -# Initialize and apply Terraform terraform init terraform apply -auto-approve @@ -66,146 +57,50 @@ terraform apply -auto-approve $(terraform output -raw configure_kubectl) ``` -## Components - -### 🔄 NodePools -EKS Auto Mode leverages [Karpenter](https://karpenter.sh/docs/) for intelligent node management: - -⚡ **Auto-scaling Features** -- Dynamic node provisioning -- Workload-aware scaling -- Resource optimization - -📦 **Preconfigured NodePools** -In these samples we configure the following Nodepools for you: -- ARM64-optimized Graviton nodes -- EC2 Spot nodes -- GPU-accelerated compute nodes -- Inferentia2 ML inference nodes - -> 📘 **Note**: Check [NodePool Templates](nodepool-templates) for detailed configurations. - - -### 🛠️ NodeClass Configuration -EKS Auto Mode uses [NodeClass](https://docs.aws.amazon.com/eks/latest/userguide/create-node-class.html) for granular control over infrastructure-level settings: - -⚙️ **Customization Options** -- Subnet selection for node placement -- Security group configuration -- Ephemeral storage settings -- Network policies and SNAT configuration -- Custom tagging for resource management - -📦 **Implementation Approach** -In these samples, we create a custom NodeClass for each example workload type: -- Each NodeClass is defined in the same file as its corresponding NodePool -- Custom NodeClasses are only needed for specific infrastructure customizations -- For most use cases, the default NodeClass works best with EKS Auto Mode - -> ⚠️ **Important**: When creating custom NodeClasses, be aware of these considerations: -> - If you change the Node IAM Role, you'll need to create a new Access Entry -> - Custom NodeClasses may require additional configuration to work properly with EKS Auto Mode -> - Do not name your custom NodeClass "default" as this is reserved - -### 🌐 Load Balancer Configuration -EKS Auto Mode automates load balancer setup with AWS best practices: - -1. 🔹 **Application Load Balancer (ALB)** - - IngressClass-based configuration - - [AWS Documentation](https://docs.aws.amazon.com/eks/latest/userguide/auto-configure-alb.html) - - Example: [2048 Game Ingress](/examples/graviton/2048-ingress.yaml) - -2. 🔸 **Network Load Balancer (NLB)** - - Native Kubernetes service integration - - [AWS Documentation](https://docs.aws.amazon.com/eks/latest/userguide/auto-configure-nlb.html) - -> **Important**: If subnet IDs are not specified in IngressClassParams, AWS requires specific tags on subnets for proper load balancer functionality: -> - Public subnets: `kubernetes.io/role/elb: "1"` -> - Private subnets: `kubernetes.io/role/internal-elb: "1"` -> -> Our Terraform code automatically creates these necessary subnet tags, but you may need to add them manually if using custom networking configurations. - -#### 🌍 Public exposure (opt-in) - -By default this stack is **safe-by-default**: every example workload exposes an *internal-scheme* load balancer reachable only via `kubectl port-forward`. Nothing is published to the public internet without an explicit opt-in. - -To expose the example workloads on a real domain over HTTPS, set `var.base_domain` (and optionally `var.subdomain`) to a public Route53 hosted zone you already own: - +3. **Apply an example** (e.g., Graviton): ```bash -terraform apply -var='base_domain=example.com' -var='subdomain=automode' +kubectl apply -f examples/graviton/ ``` -When `base_domain` is set, Terraform will: +## Examples -- Look up the existing public hosted zone (it does **not** create one — the zone must already exist and be the authoritative DNS for that name). -- Issue an ACM wildcard certificate `*..` validated via DNS records added to the zone. -- Install [external-dns](https://github.com/kubernetes-sigs/external-dns) bound to a Pod Identity IAM role scoped to **only** that hosted zone (not `Route53FullAccess`). -- Switch the cluster-wide `IngressClass alb` to `internet-facing` with a shared ALB group so all example Ingresses share one load balancer. -- Render each example with a public hostname and the appropriate annotations. +Each example has its own README with detailed explanations of the underlying mechanics. -Workload hostnames once enabled: +### Compute Patterns -| Example | URL | -|---|---| -| `examples/graviton` | `https://2048-graviton.` | -| `examples/spot` | `https://2048-spot.` | -| `examples/gpu` | `https://gpu.` | -| `examples/neuron` | `https://neuron.` | +| Example | Description | +|---------|-------------| +| [Graviton](examples/graviton/) | ARM64 workloads on cost-effective Graviton instances. Deploys a 2048 game. | +| [Spot](examples/spot/) | Fault-tolerant workloads on EC2 Spot with diverse instance families. Deploys a 2048 game. | +| [GPU](examples/gpu/) | GPU-accelerated ML inference (Qwen 3 on NVIDIA GPUs). | +| [Neuron](examples/neuron/) | ML inference on AWS Inferentia2 (DeepSeek-R1-Qwen3-8B served by vLLM). | -The ALB controller picks the right certificate via SNI from each Ingress's `host:` against the wildcard cert — no `certificateArn` is configured anywhere. +### Cost Optimization -To revert to safe-by-default, unset `var.base_domain` and re-apply. +| Example | Description | +|---------|-------------| +| [Cost Optimization](examples/cost-optimization/) | OD/Spot mixed pools with weighted priorities and pause-pod overprovision headroom. | -### 💾 EBS CSI Driver Configuration -EKS Auto Mode automates persistent storage setup with Amazon EBS: +### Advanced Scheduling -1. 🔹 **Automated Storage Management** - - No need to install the EBS CSI controller on EKS Auto Mode clusters - - [AWS Documentation](https://docs.aws.amazon.com/eks/latest/userguide/ebs-csi.html) +| Example | Description | +|---------|-------------| +| [Capacity Reservation](examples/capacity-reservation/) | Pin workloads to On-Demand Capacity Reservations (ODCRs) so reserved capacity is consumed. | +| [Static Capacity](examples/static-capacity/) | Maintain a fixed fleet of always-on nodes using `spec.replicas`, immune to consolidation. | +| [Batch Jobs](examples/batch-jobs/) | Protect long-running jobs from eviction using `do-not-disrupt` annotations and dedicated NodePools. | +| [Disruption Budgets](examples/disruption-budgets/) | Limit simultaneous node drains during consolidation to prevent cascading failures. | -2. 🔸 **Storage Classes and PVCs** - - Native Kubernetes storage integration - - Optimized for various workload requirements +### Autoscaling -> **Important**: The EBS CSI driver requires specific IAM permissions to make calls to AWS APIs. EKS Auto Mode simplifies this setup, but you should be aware of the following: -> - Only platform versions created from a storage class using `ebs.csi.eks.amazonaws.com` as the provisioner can be mounted on nodes created by EKS Auto Mode -> - Existing platform versions must be migrated to the new storage class using a volume snapshot -> - For custom KMS key encryption, additional IAM permissions may be required +| Example | Description | +|---------|-------------| +| [Pod Autoscaling](examples/pod-autoscaling/) | HPA for CPU-based scaling plus KEDA for event-driven scaling (SQS queue depth). | -## Examples +### Observability -🚀 Get started with our sample workloads: - -### ARM64 Applications -🎮 [Running Graviton Workloads](examples/graviton/) -- Cost-effective ARM64 deployments -- Optimized performance -- Example: 2048 game application - -### Fault Tolerant Applications -💰 [Running Spot Workloads](examples/spot/) -- Cost-effective deployments -- Diverse and flexible compute choices -- Example: 2048 game application - -### GPU Applications -📱 [Running GPU Workloads](examples/gpu/) -- ML/AI model deployment -- GPU-accelerated computing -- Example: Qwen 3 model inference - -### Neuron Applications -🧠 [Running Neuron Workloads](examples/neuron/) -- ML inference on Inferentia2 -- Cost-effective acceleration -- Example: DeepSeek-R1-Qwen3-8B served by vLLM - -### Pod Autoscaling -📊 [Pod Autoscaling with HPA and KEDA](examples/pod-autoscaling/) -- Horizontal Pod Autoscaler (HPA) basics -- CPU-based autoscaling with PHP Apache server -- KEDA for event-driven autoscaling -- SQS queue-based GPU workload scaling +| Example | Description | +|---------|-------------| +| [Observability](examples/observability/) | CloudWatch Container Insights integration for metrics, pod logs, and Application Signals tracing. | ## Cleanup @@ -233,7 +128,7 @@ The script runs in three phases: 2. **Terraform destroy** — runs `terraform init` + `destroy` for both the main and KEDA terraform roots. 3. **Orphan sweep** — scans for resources tagged with the cluster name (or matching known patterns for untaggable resources like Auto Mode internal volumes) and prompts for deletion. -> ⚠️ **Why not just `terraform destroy`?** A bare `terraform destroy` doesn't drain Kubernetes-managed resources first. ALBs, EBS volumes, EC2 instances, and ENIs created by in-cluster controllers (ALB controller, EBS CSI, Karpenter) are not in Terraform state — they persist as orphans after the cluster is gone. The cleanup script handles these. +> **Why not just `terraform destroy`?** A bare `terraform destroy` doesn't drain Kubernetes-managed resources first. ALBs, EBS volumes, EC2 instances, and ENIs created by in-cluster controllers (ALB controller, EBS CSI, Karpenter) are not in Terraform state — they persist as orphans after the cluster is gone. The cleanup script handles these.
Manual alternative (not recommended) @@ -247,19 +142,120 @@ terraform destroy --auto-approve This only destroys Terraform-managed resources. You will need to manually find and delete any orphaned load balancers, volumes, instances, security groups, IAM roles, OIDC providers, and CloudWatch log groups.
+## Configuration + +All inputs are defined in `terraform/variables.tf`. Override them with `-var` flags or a `terraform.tfvars` file. + +| Variable | Description | Default | +|----------|-------------|---------| +| `name` | Name of the VPC and EKS cluster | `automode-cluster` | +| `region` | AWS region to deploy into | `us-west-2` | +| `eks_cluster_version` | EKS Kubernetes version | `1.34` | +| `vpc_cidr` | VPC CIDR block (RFC 1918) | `10.0.0.0/16` | +| `tags` | Tags applied to every taggable resource (provider default\_tags, EKS primary SG, NodeClass EC2/EBS/ENI, StorageClass EBS, ALB) | `{"auto-delete" = "never"}` | +| `base_domain` | Public Route53 hosted zone for HTTPS exposure. Leave empty for internal-only (safe-by-default). | `""` | +| `subdomain` | Optional prefix under `base_domain` (e.g., `automode` gives `automode.example.com`). Ignored when `base_domain` is empty. | `""` | +| `ephemeral_storage_kms_key_id` | KMS key ID for encrypting ephemeral node storage. Leave empty for default encryption. | `""` | +| `enable_observability` | Enable CloudWatch Container Insights addon (metrics, logs, Application Signals). Incurs CloudWatch costs. | `false` | + +**Example — public exposure with observability:** +```bash +terraform apply \ + -var='base_domain=example.com' \ + -var='subdomain=automode' \ + -var='enable_observability=true' +``` + +## Components + +### How EKS Auto Mode Works + +EKS Auto Mode fully automates the operational overhead of running Kubernetes on AWS. Rather than requiring you to install, configure, and upgrade individual cluster add-ons, Auto Mode runs them as managed components inside the EKS control plane. Specifically, it: + +- **Provisions, scales, and consolidates compute** — powered by Karpenter, it matches pending pods to optimal EC2 instances, bins-packs efficiently, and removes underutilized nodes automatically. +- **Manages pod networking** — handles VPC CNI configuration, IP address allocation, and security group enforcement without any DaemonSet you need to maintain. +- **Handles persistent storage** — provisions and attaches EBS volumes from PersistentVolumeClaims via the managed EBS CSI driver. +- **Automates load balancing** — creates ALBs and NLBs from Ingress and Service resources, including TLS termination with ACM certificates. +- **Runs CoreDNS** — cluster DNS is a managed component with no installation or tuning required. +- **Manages Pod Identity Agent** — enables fine-grained IAM roles for pods without manual IRSA configuration. +- **Monitors node health** — detects unhealthy nodes and automatically repairs or replaces them. +- **Handles AMI selection and patching** — picks the correct AMI for each instance type, applies security patches, and remediates drift. + +All of these run in the control plane — you never install, configure, or upgrade them. You interact through standard Kubernetes APIs (NodePool, NodeClass, Ingress, StorageClass, etc.) and EKS handles the rest. + +### NodePool → NodeClass → EC2 Flow + +The provisioning path: + +``` +Pod pending → Karpenter matches NodePool constraints (instance families, AZs, capacity type) + → NodePool references a NodeClass (subnet selection, security groups, tags, storage) + → Karpenter launches an EC2 instance matching the constraints + → kubelet registers the node and the pod is scheduled +``` + +**NodePools** define what to launch (instance types, architectures, capacity type, taints/labels). **NodeClasses** define how to launch (subnets, SGs, ephemeral storage, tags pushed to EC2/EBS/ENI). + +### Load Balancer Configuration + +EKS Auto Mode automates ALB and NLB setup: + +- **Application Load Balancer (ALB)** — IngressClass-based, supports shared ALB groups across namespaces. [Docs](https://docs.aws.amazon.com/eks/latest/userguide/auto-configure-alb.html) +- **Network Load Balancer (NLB)** — Native Kubernetes Service type LoadBalancer. [Docs](https://docs.aws.amazon.com/eks/latest/userguide/auto-configure-nlb.html) + +> **Subnet tagging requirement**: If subnet IDs are not explicit in IngressClassParams, subnets need `kubernetes.io/role/elb: "1"` (public) or `kubernetes.io/role/internal-elb: "1"` (private). The Terraform code in this repo adds these tags automatically. + +#### Public exposure (opt-in) + +By default this stack is **safe-by-default**: every example workload exposes an *internal-scheme* load balancer reachable only via `kubectl port-forward`. Nothing is published to the public internet without an explicit opt-in. + +To expose the example workloads on a real domain over HTTPS, set `var.base_domain` (and optionally `var.subdomain`) to a public Route53 hosted zone you already own: + +```bash +terraform apply -var='base_domain=example.com' -var='subdomain=automode' +``` + +When `base_domain` is set, Terraform will: + +- Look up the existing public hosted zone (it does **not** create one — the zone must already exist and be the authoritative DNS for that name). +- Issue an ACM wildcard certificate `*..` validated via DNS records added to the zone. +- Install [external-dns](https://github.com/kubernetes-sigs/external-dns) bound to a Pod Identity IAM role scoped to **only** that hosted zone (not `Route53FullAccess`). +- Switch the cluster-wide `IngressClass alb` to `internet-facing` with a shared ALB group so all example Ingresses share one load balancer. +- Render each example with a public hostname and the appropriate annotations. + +Workload hostnames once enabled: + +| Example | URL | +|---|---| +| `examples/graviton` | `https://2048-graviton.` | +| `examples/spot` | `https://2048-spot.` | +| `examples/gpu` | `https://gpu.` | +| `examples/neuron` | `https://neuron.` | + +The ALB controller picks the right certificate via SNI from each Ingress's `host:` against the wildcard cert — no `certificateArn` is configured anywhere. + +To revert to safe-by-default, unset `var.base_domain` and re-apply. + +### EBS CSI Driver + +EKS Auto Mode includes the EBS CSI driver as a managed component — no installation required. + +- Only volumes provisioned from a StorageClass using `ebs.csi.eks.amazonaws.com` can mount on Auto Mode nodes. +- Existing volumes need migration via volume snapshots. +- Custom KMS encryption may require additional IAM permissions. + +[AWS Documentation](https://docs.aws.amazon.com/eks/latest/userguide/ebs-csi.html) + +## Learn More + +- [EKS Auto Mode documentation](https://docs.aws.amazon.com/eks/latest/userguide/automode.html) — official AWS guide covering setup, NodePools, NodeClasses, and managed components +- [Karpenter documentation](https://karpenter.sh/docs/) — the provisioner that powers Auto Mode's compute layer; useful for understanding NodePool/NodeClass semantics +- [karpenter-blueprints](https://github.com/aws-samples/karpenter-blueprints) — additional Karpenter patterns beyond what this repo covers +- [platform-engineering-on-eks](https://github.com/aws-samples/platform-engineering-on-eks) — broader platform engineering patterns on EKS + ## Security Considerations -Our code is continuously scanned using [Checkov](https://www.checkov.io/5.Policy%20Index/kubernetes.html). The following security considerations are documented for transparency: - -|Checks |Details |Reasons | -|--- |--- |--- | -|CKV_TF_1 |Ensure Terraform module sources use a commit hash |For easy experimentation, we set version of module, instead of setting a commit hash. Consider implementing a commit hash in a production cluster. [Read more on why we need to set commit hash for modules here.](https://medium.com/boostsecurity/erosion-of-trust-unmasking-supply-chain-vulnerabilities-in-the-terraform-registry-2af48a7eb2) | -|CKV2_K8S_6 |Minimize the admission of pods which lack an associated NetworkPolicy |All Pod to Pod communication is allowed by default for easy experimentation in this project. Amazon VPC CNI now supports [Kubernetes Network Policies](https://aws.amazon.com/blogs/containers/amazon-vpc-cni-now-supports-kubernetes-network-policies/) to secure network traffic in kubernetes clusters | -|CKV_K8S_8 |Liveness Probe Should be Configured |For easy experimentation, no health checks is to be performed against the container to determine whether it is alive or not. Consider implementing [health checks](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) in a production cluster. | -|CKV_K8S_9 |Readiness Probe Should be Configured |For easy experimentation, no health checks is to be performed against the container to determine whether it is alive or not. Consider implementing health checks in a production cluster. | -|CKV_K8S_22 |Use read-only filesystem for containers where possible |We've made an exception for the workloads that requires are Read/Write file system. [Configure your images with read-only root file system](https://docs.aws.amazon.com/eks/latest/best-practices/pod-security.html#_configure_your_images_with_read_only_root_file_system) | -|CKV_K8S_23 |Minimize the admission of root containers |This project uses default root container configurations for demonstration purposes. While this doesn't follow security best practices, it ensures compatibility with demo images. For production, configure runAsNonRoot: true and follow [guidance](https://docs.docker.com/engine/reference/builder/#user) on building images with specified user ID. | -|CKV_K8S_37 |Minimize the admission of containers with capabilities assigned |For easy experimentation, we've made exception for the workloads that requires added capability. For production purposes, we recommend [capabilities field](https://docs.aws.amazon.com/eks/latest/best-practices/pod-security.html#_linux_capabilities) that allows granting certain privileges to a process without granting all the privileges of the root user. | -|CKV_K8S_40 |Containers should run as a high UID to avoid host conflict |We've used publicly available container images in this project for customers' easy access. For test purposes, the container images user id are left intact. See [how to define UID](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-pod). | + +See [SECURITY_CONSIDERATIONS.md](SECURITY_CONSIDERATIONS.md) for Checkov scan results and documented exceptions. ## Contributing Contributions welcome! Please read our [Contributing Guidelines](CONTRIBUTING.md) and [Code of Conduct](CODE_OF_CONDUCT.md). diff --git a/SECURITY_CONSIDERATIONS.md b/SECURITY_CONSIDERATIONS.md new file mode 100644 index 0000000..dff3817 --- /dev/null +++ b/SECURITY_CONSIDERATIONS.md @@ -0,0 +1,14 @@ +# Security Considerations + +Our code is continuously scanned using [Checkov](https://www.checkov.io/5.Policy%20Index/kubernetes.html). The following security considerations are documented for transparency: + +|Checks |Details |Reasons | +|--- |--- |--- | +|CKV_TF_1 |Ensure Terraform module sources use a commit hash |For easy experimentation, we set version of module, instead of setting a commit hash. Consider implementing a commit hash in a production cluster. [Read more on why we need to set commit hash for modules here.](https://medium.com/boostsecurity/erosion-of-trust-unmasking-supply-chain-vulnerabilities-in-the-terraform-registry-2af48a7eb2) | +|CKV2_K8S_6 |Minimize the admission of pods which lack an associated NetworkPolicy |All Pod to Pod communication is allowed by default for easy experimentation in this project. Amazon VPC CNI now supports [Kubernetes Network Policies](https://aws.amazon.com/blogs/containers/amazon-vpc-cni-now-supports-kubernetes-network-policies/) to secure network traffic in kubernetes clusters | +|CKV_K8S_8 |Liveness Probe Should be Configured |For easy experimentation, no health checks is to be performed against the container to determine whether it is alive or not. Consider implementing [health checks](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) in a production cluster. | +|CKV_K8S_9 |Readiness Probe Should be Configured |For easy experimentation, no health checks is to be performed against the container to determine whether it is alive or not. Consider implementing health checks in a production cluster. | +|CKV_K8S_22 |Use read-only filesystem for containers where possible |We've made an exception for the workloads that requires are Read/Write file system. [Configure your images with read-only root file system](https://docs.aws.amazon.com/eks/latest/best-practices/pod-security.html#_configure_your_images_with_read_only_root_file_system) | +|CKV_K8S_23 |Minimize the admission of root containers |This project uses default root container configurations for demonstration purposes. While this doesn't follow security best practices, it ensures compatibility with demo images. For production, configure runAsNonRoot: true and follow [guidance](https://docs.docker.com/engine/reference/builder/#user) on building images with specified user ID. | +|CKV_K8S_37 |Minimize the admission of containers with capabilities assigned |For easy experimentation, we've made exception for the workloads that requires added capability. For production purposes, we recommend [capabilities field](https://docs.aws.amazon.com/eks/latest/best-practices/pod-security.html#_linux_capabilities) that allows granting certain privileges to a process without granting all the privileges of the root user. | +|CKV_K8S_40 |Containers should run as a high UID to avoid host conflict |We've used publicly available container images in this project for customers' easy access. For test purposes, the container images user id are left intact. See [how to define UID](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-pod). | diff --git a/examples/batch-jobs/README.md b/examples/batch-jobs/README.md new file mode 100644 index 0000000..bac3bdc --- /dev/null +++ b/examples/batch-jobs/README.md @@ -0,0 +1,80 @@ +# Batch Jobs: Protecting Long-Running Workloads from Disruption + +## The problem + +Karpenter (and EKS Auto Mode) continuously consolidates underutilized nodes. This is great for cost optimization, but catastrophic for long-running batch jobs. A 6-hour ML training run evicted at hour 5 wastes 5 hours of GPU compute. An ETL pipeline disrupted mid-write can leave data in an inconsistent state. + +Without protection, consolidation treats your 8-hour training job the same as a stateless web server -- just another pod to reschedule. + +## How `karpenter.sh/do-not-disrupt` works + +Adding this annotation to a pod's metadata tells Auto Mode: "do not voluntarily evict this pod for consolidation or drift remediation." + +```yaml +metadata: + annotations: + karpenter.sh/do-not-disrupt: "true" +``` + +When this annotation is present on any pod running on a node, that entire node becomes protected from voluntary disruption. The node will not be consolidated, drifted, or removed for emptiness as long as the annotated pod is running. + +## Scope of protection + +| Disruption Type | Protected? | Example | +|----------------|-----------|---------| +| Consolidation (underutilized) | Yes | Karpenter wants to bin-pack pods onto fewer nodes | +| Drift remediation | Yes | AMI updated, Karpenter wants to roll nodes | +| Empty node removal | Yes | All other pods drained, but annotated pod remains | +| Spot interruption | **No** | AWS reclaims the instance with 2-min warning | +| Node health failure | **No** | EC2 status check fails | +| Manual `kubectl drain` | **No** | Human or automation explicitly drains | + +**Key insight**: this protects against the scheduler's optimization decisions, not against infrastructure failures. For Spot protection, use on-demand instances. For health failures, implement checkpointing. + +## Why annotation vs taint + +These solve different problems: + +- **Taints** control which pods CAN schedule onto a node (admission control) +- **do-not-disrupt** controls whether a node with this pod CAN be consolidated (eviction control) + +A GPU taint prevents CPU pods from landing on GPU nodes. `do-not-disrupt` prevents Karpenter from evicting your training job to consolidate that GPU node. + +## When to use + +- ML training jobs (hours to days) +- ETL pipelines with expensive restart costs +- Video transcoding (long-running, stateful progress) +- Database migrations or backfills +- Any batch workload where: **restart cost > idle node cost** + +## Deploy + +```bash +kubectl apply -f batch-training-job.yaml + +# Verify the job is running +kubectl get jobs -n batch-jobs +kubectl get pods -n batch-jobs -o wide +``` + +## What to observe + +```bash +# Confirm the annotation is on the running pod +kubectl get pod -n batch-jobs -l app=ml-training -o jsonpath='{.items[0].metadata.annotations}' + +# Watch karpenter logs -- you should see "cannot disrupt" messages for this node +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -f | grep "cannot disrupt\|do-not-disrupt" + +# Identify which node the job landed on +NODE=$(kubectl get pod -n batch-jobs -l app=ml-training -o jsonpath='{.items[0].spec.nodeName}') +echo "Protected node: $NODE" + +# Verify that node is NOT being considered for consolidation +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "$NODE" + +# Once the job completes, the annotation disappears with the pod. +# The node becomes eligible for consolidation again. +kubectl get nodes -w # Watch the node get consolidated after job completion +``` diff --git a/examples/batch-jobs/batch-training-job.yaml b/examples/batch-jobs/batch-training-job.yaml new file mode 100644 index 0000000..d6f2f50 --- /dev/null +++ b/examples/batch-jobs/batch-training-job.yaml @@ -0,0 +1,38 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: batch-jobs +--- +apiVersion: batch/v1 +kind: Job +metadata: + name: ml-training-example + namespace: batch-jobs +spec: + completions: 1 + parallelism: 1 + backoffLimit: 2 + template: + metadata: + annotations: + karpenter.sh/do-not-disrupt: "true" + labels: + app: ml-training + spec: + restartPolicy: OnFailure + tolerations: + - key: "nvidia.com/gpu" + operator: "Equal" + value: "true" + effect: "NoSchedule" + containers: + - name: training + image: public.ecr.aws/nvidia/cuda:12.4.0-base-ubuntu22.04 + command: ["sh", "-c", "echo 'Simulating 2-hour training job...' && sleep 7200"] + resources: + requests: + cpu: "4" + memory: 16Gi + nvidia.com/gpu: "1" + limits: + nvidia.com/gpu: "1" diff --git a/examples/capacity-reservation/README.md b/examples/capacity-reservation/README.md new file mode 100644 index 0000000..cfd9a55 --- /dev/null +++ b/examples/capacity-reservation/README.md @@ -0,0 +1,143 @@ +# On-Demand Capacity Reservation (ODCR) Targeting in EKS Auto Mode + +## What are ODCRs? + +On-Demand Capacity Reservations (ODCRs) let you reserve compute capacity in a specific Availability Zone for a specific instance type. Once created, the capacity is held for you regardless of whether any instances are running against it. You pay the on-demand rate for the reserved capacity whether it is used or not, so the goal is to ensure your workloads actually land on the reservation rather than launching as regular on-demand instances beside it. + +ODCRs are not the same as Reserved Instances or Savings Plans. Those are billing constructs that apply discounts retroactively. An ODCR is a physical capacity guarantee: the hosts are allocated and waiting for you. + +## Why This Matters for ML/GPU Workloads + +GPU instance families (p5, g6e, inf2, trn1) are frequently capacity-constrained in popular regions. You may submit a RunInstances call and receive an InsufficientInstanceCapacity error because the AZ is out of that type. + +ODCRs solve this by pre-allocating the capacity. However, if your Karpenter/Auto Mode NodeClass does not explicitly target the reservation, launched instances will consume regular on-demand capacity and leave the ODCR idle (still costing you money). Correctly configuring `capacityReservationSelectorTerms` ensures nodes preferentially land on your reserved capacity. + +Common scenarios: + +- Multi-day distributed training jobs on p5.48xlarge +- Batch inference pipelines with predictable GPU demand +- Real-time inference with guaranteed baseline capacity +- Compliance requirements mandating dedicated or reserved tenancy + +## How `capacityReservationSelectorTerms` Works + +The NodeClass field `capacityReservationSelectorTerms` tells Auto Mode which ODCRs to target when launching nodes. There are three targeting strategies: + +### 1. Target by Reservation ID (most specific) + +```yaml +capacityReservationSelectorTerms: + - id: cr-0a1b2c3d4e5f67890 +``` + +Use this when you have a single known reservation and want deterministic placement. + +### 2. Target by Tags (flexible, recommended) + +```yaml +capacityReservationSelectorTerms: + - tags: + purpose: ml-training + team: platform +``` + +Use this when you manage multiple reservations with a tagging convention. As you create or retire ODCRs, the NodeClass automatically picks up matching ones without manifest changes. + +### 3. Target by Owner (for shared reservations) + +```yaml +capacityReservationSelectorTerms: + - owner: 123456789012 +``` + +Use this when another account shares ODCRs with you via AWS Resource Access Manager (RAM). + +You can combine multiple terms; Auto Mode evaluates them in order and uses the first reservation with available capacity. + +## Fallback Behavior + +If all matching ODCRs are fully utilized (every slot occupied by a running instance), Auto Mode falls back to launching regular on-demand instances. Your workloads still schedule and run; they simply do not benefit from the reservation guarantee. + +This means: + +- Pods are never stuck Pending solely because a reservation is full. +- You do not need separate "overflow" NodePools for the non-ODCR case. +- The same NodePool handles both reserved and unreserved launches transparently. + +Monitor the `UsedInstanceCount` vs `TotalInstanceCount` in the EC2 Capacity Reservations console to see whether your ODCRs are being utilized. + +## When to Use + +| Scenario | Why ODCR helps | +|----------|---------------| +| GPU training jobs (multi-hour/day) | Guarantees capacity won't be reclaimed mid-job | +| Batch inference with known parallelism | Ensures all workers launch simultaneously | +| Real-time inference baseline | Baseline capacity is always available; burst goes on-demand | +| Compliance / dedicated tenancy | Some regulations require pre-allocated, non-shared capacity | +| Event-driven spikes (launches, demos) | Reserve ahead, release after the event | + +## Prerequisites + +1. **An existing ODCR** in the target AZ for the instance type you need. + Create one via the EC2 console or CLI: + ``` + aws ec2 create-capacity-reservation --instance-type g6e.xlarge --instance-platform Linux/UNIX --availability-zone us-west-2a --instance-count 4 --tag-specifications 'ResourceType=capacity-reservation,Tags=[{Key=purpose,Value=ml-training}]' + ``` + See [AWS docs: Create a Capacity Reservation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-reservations.html#capacity-reservations-create) for full options. + +2. **Appropriate IAM permissions** on the node role to describe and use capacity reservations: + - `ec2:DescribeCapacityReservations` + - `ec2:RunInstances` with the reservation target + +3. **Matching AZ and instance type** between the ODCR and the NodePool requirements. Auto Mode will not launch a g6e instance into a p5 reservation. + +4. **Tags on the ODCR** if using tag-based selector terms (recommended for flexibility). + +## Deploy + +1. Render the template with your cluster values: + ``` + terraform output -raw odcr_nodepool_manifest > odcr-nodepool.yaml + ``` + Or substitute the variables manually in `odcr-nodepool.yaml.tpl`. + +2. Apply to your cluster: + ``` + kubectl apply -f odcr-nodepool.yaml + ``` + +3. Launch a GPU workload that tolerates the `nvidia.com/gpu` taint: + ```yaml + tolerations: + - key: "nvidia.com/gpu" + operator: Equal + value: "true" + effect: NoSchedule + resources: + limits: + nvidia.com/gpu: 1 + ``` + +## What to Observe + +1. **EC2 Console > Capacity Reservations**: Watch `Used instance count` increase as Auto Mode launches nodes into the reservation. + +2. **Node labels**: Nodes launched into an ODCR carry standard EC2 metadata. Check instance details: + ``` + aws ec2 describe-instances --instance-ids --query 'Reservations[].Instances[].CapacityReservationId' + ``` + +3. **NodePool counters**: Verify the NodePool's resource usage is increasing: + ``` + kubectl get nodepool odcr-gpu-nodepool -o yaml | grep -A5 status + ``` + +4. **Fallback scenario**: If you scale beyond the reservation size, additional nodes will launch as regular on-demand. The `CapacityReservationId` field will be empty on those instances. + +## Cleanup + +To release ODCR capacity when no longer needed: + +1. Scale down or delete workloads using the GPU taint. +2. Delete the NodePool and NodeClass: `kubectl delete nodepool odcr-gpu-nodepool && kubectl delete nodeclass odcr-gpu-nodeclass` +3. Cancel the capacity reservation: `aws ec2 cancel-capacity-reservation --capacity-reservation-id cr-0a1b2c3d4e5f67890` diff --git a/examples/capacity-reservation/odcr-nodepool.yaml.tpl b/examples/capacity-reservation/odcr-nodepool.yaml.tpl new file mode 100644 index 0000000..9797f77 --- /dev/null +++ b/examples/capacity-reservation/odcr-nodepool.yaml.tpl @@ -0,0 +1,52 @@ +--- +apiVersion: eks.amazonaws.com/v1 +kind: NodeClass +metadata: + name: odcr-gpu-nodeclass +spec: + role: ${node_iam_role_name} + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: ${cluster_name} + securityGroupSelectorTerms: + - tags: + aws:eks:cluster-name: ${cluster_name} + capacityReservationSelectorTerms: + - tags: + purpose: ml-training + tags: + ${indent(4, yamlencode(tags))} +%{ if kms_key_id != "" ~} + ephemeralStorage: + kmsKeyID: ${kms_key_id} +%{ endif ~} +--- +apiVersion: karpenter.sh/v1 +kind: NodePool +metadata: + name: odcr-gpu-nodepool +spec: + template: + spec: + nodeClassRef: + group: eks.amazonaws.com + kind: NodeClass + name: odcr-gpu-nodeclass + requirements: + - key: "eks.amazonaws.com/instance-family" + operator: In + values: ["g6e", "p5"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["on-demand"] + taints: + - key: "nvidia.com/gpu" + value: "true" + effect: NoSchedule + limits: + cpu: 500 + disruption: + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 60s + budgets: + - nodes: "10%" diff --git a/examples/cost-optimization/README.md b/examples/cost-optimization/README.md new file mode 100644 index 0000000..4f3c6d6 --- /dev/null +++ b/examples/cost-optimization/README.md @@ -0,0 +1,122 @@ +# Cost Optimization Patterns for EKS Auto Mode + +This example demonstrates two key cost optimization patterns: **OD/Spot mixed capacity** and **overprovision headroom via pause pods**. + +--- + +## Why OD/Spot Split Matters + +EC2 Spot instances are 60-90% cheaper than On-Demand, but AWS can reclaim them with 2 minutes notice. For production workloads you need a mix: + +- **On-Demand** provides a stable baseline that won't disappear mid-request. +- **Spot** provides cheap burst capacity for stateless, fault-tolerant work. + +EKS Auto Mode (via Karpenter) labels nodes with `karpenter.sh/capacity-type: on-demand` or `karpenter.sh/capacity-type: spot`. You use topology spread constraints to distribute pods across both capacity types evenly. + +--- + +## How Topology Spread Constraints Work + +```yaml +topologySpreadConstraints: + - maxSkew: 1 + topologyKey: karpenter.sh/capacity-type + whenUnsatisfiable: DoNotSchedule + labelSelector: + matchLabels: + app: web-mixed-capacity +``` + +| Field | Meaning | +|-------|---------| +| `maxSkew` | Maximum allowed difference in pod count between any two topology domains. A skew of 1 means "at most 1 pod difference between OD and Spot." | +| `topologyKey` | The node label that defines topology domains. Using `karpenter.sh/capacity-type` creates two domains: `on-demand` and `spot`. | +| `whenUnsatisfiable` | `DoNotSchedule` blocks new pods if they'd violate the skew (hard constraint). `ScheduleAnyway` is a soft preference. | +| `labelSelector` | Which pods count toward the spread calculation. Must match the pod's own labels. | + +With 6 replicas and maxSkew 1, you get 3 pods on OD nodes and 3 on Spot nodes. If a Spot node is reclaimed, you still have 3 OD pods serving traffic while replacements schedule. + +--- + +## What Overprovision / Headroom Is + +When a new pod arrives and no node has capacity, EKS Auto Mode launches a new node. That takes 1-2 minutes. For latency-sensitive scale-out, that delay is unacceptable. + +**Overprovision** solves this by keeping spare capacity pre-warmed. You run low-priority "pause" pods that reserve CPU and memory on nodes but do nothing. When a real workload arrives, the scheduler **preempts** the pause pods instantly, giving the real pod immediate access to already-running node capacity. + +The pause pods use the `registry.k8s.io/pause:3.9` image — a 500KB container that literally does nothing except hold a resource reservation. + +--- + +## How PriorityClasses and Preemption Work Together + +``` +PriorityClass "pause-pods" → value: -1 (lowest) +Default priority → value: 0 (all normal workloads) +``` + +The scheduler always prefers higher-priority pods. When a real pod (priority 0) cannot be scheduled due to lack of resources, the scheduler looks for lower-priority pods it can evict. The pause pods at priority -1 are always the first victims. + +The flow: +1. Pause pods hold 3 x (1 CPU + 1Gi memory) of headroom on nodes. +2. A real deployment scales up and needs resources. +3. Scheduler evicts pause pods (instant — `terminationGracePeriodSeconds: 0`). +4. Real pods land immediately on the freed capacity. +5. The now-pending pause pods trigger new node creation in the background, restoring headroom. + +--- + +## When to Use Each Pattern + +| Pattern | Use case | +|---------|----------| +| **OD/Spot split** | Stateless HTTP services, batch processors, queue workers — anything that tolerates pod replacement gracefully. | +| **Overprovision headroom** | Latency-sensitive scale-out where waiting 1-2 min for a node is unacceptable: real-time APIs, gaming backends, autoscaled inference endpoints. | + +You can combine both — run pause pods on Spot capacity so your headroom is cheap, while real workloads spread across OD and Spot. + +--- + +## Deploy + +```bash +kubectl apply -f examples/cost-optimization/mixed-od-spot-deployment.yaml +kubectl apply -f examples/cost-optimization/overprovision-pause-pods.yaml +``` + +--- + +## What to Observe + +**For the OD/Spot split deployment:** + +```bash +# Check that pods spread across capacity types +kubectl get pods -n cost-optimization -l app=web-mixed-capacity -o wide + +# Verify nodes have different capacity types +kubectl get nodes -L karpenter.sh/capacity-type + +# Count pods per capacity type +kubectl get pods -n cost-optimization -l app=web-mixed-capacity -o json | \ + jq -r '.items[].spec.nodeName' | \ + xargs -I{} kubectl get node {} -o jsonpath='{.metadata.labels.karpenter\.sh/capacity-type}' | \ + sort | uniq -c +``` + +**For overprovision/headroom:** + +```bash +# Verify pause pods are running and holding resources +kubectl get pods -n cost-optimization -l app=overprovision + +# Check their priority class +kubectl get pods -n cost-optimization -l app=overprovision -o jsonpath='{.items[0].spec.priorityClassName}' + +# Watch preemption in action — scale a real workload and observe events +kubectl get events -n cost-optimization --field-selector reason=Preempted -w + +# Measure scheduling latency (time from creation to running) +kubectl get pods -n cost-optimization -o json | \ + jq '.items[] | select(.status.phase=="Running") | .metadata.name + ": " + (.status.conditions[] | select(.type=="PodScheduled") | .lastTransitionTime)' +``` diff --git a/examples/cost-optimization/mixed-od-spot-deployment.yaml b/examples/cost-optimization/mixed-od-spot-deployment.yaml new file mode 100644 index 0000000..910cad9 --- /dev/null +++ b/examples/cost-optimization/mixed-od-spot-deployment.yaml @@ -0,0 +1,41 @@ +apiVersion: v1 +kind: Namespace +metadata: + name: cost-optimization +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: web-mixed-capacity + namespace: cost-optimization +spec: + replicas: 6 + selector: + matchLabels: + app: web-mixed-capacity + template: + metadata: + labels: + app: web-mixed-capacity + spec: + topologySpreadConstraints: + - maxSkew: 1 + topologyKey: karpenter.sh/capacity-type + whenUnsatisfiable: DoNotSchedule + labelSelector: + matchLabels: + app: web-mixed-capacity + tolerations: + - key: "spot" + operator: "Equal" + value: "true" + effect: "NoSchedule" + nodeSelector: + kubernetes.io/arch: arm64 + containers: + - name: nginx + image: public.ecr.aws/nginx/nginx:latest + resources: + requests: + cpu: 256m + memory: 512Mi diff --git a/examples/cost-optimization/overprovision-pause-pods.yaml b/examples/cost-optimization/overprovision-pause-pods.yaml new file mode 100644 index 0000000..3dd2bbb --- /dev/null +++ b/examples/cost-optimization/overprovision-pause-pods.yaml @@ -0,0 +1,39 @@ +apiVersion: scheduling.k8s.io/v1 +kind: PriorityClass +metadata: + name: pause-pods +value: -1 +globalDefault: false +description: "Low-priority class for overprovision pause pods. These get preempted when real workloads arrive." +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: overprovision + namespace: cost-optimization +spec: + replicas: 3 + selector: + matchLabels: + app: overprovision + template: + metadata: + labels: + app: overprovision + spec: + priorityClassName: pause-pods + terminationGracePeriodSeconds: 0 + tolerations: + - key: "arm64" + operator: "Equal" + value: "true" + effect: "NoSchedule" + nodeSelector: + kubernetes.io/arch: arm64 + containers: + - name: pause + image: registry.k8s.io/pause:3.9 + resources: + requests: + cpu: "1" + memory: 1Gi diff --git a/examples/disruption-budgets/README.md b/examples/disruption-budgets/README.md new file mode 100644 index 0000000..2fb5498 --- /dev/null +++ b/examples/disruption-budgets/README.md @@ -0,0 +1,110 @@ +# Disruption Budgets + +## What disruption budgets are + +Disruption budgets limit how many nodes EKS Auto Mode can voluntarily disrupt at once. Without them, a consolidation decision can drain multiple nodes simultaneously, causing cascading failures as pods compete for remaining capacity. + +They are defined in `spec.disruption.budgets[]` on a NodePool and act as a throttle on voluntary disruption velocity. + +## Types of budgets + +### Percentage-based + +```yaml +budgets: + - nodes: "10%" +``` + +At most 10% of nodes managed by this pool can be disrupting at any time. Scales with your fleet -- 100 nodes means max 10 disrupting simultaneously. + +### Count-based + +```yaml +budgets: + - nodes: "3" +``` + +At most 3 nodes disrupting at once, regardless of fleet size. Use for small pools where percentages don't make sense. + +### Zero budgets with schedules (maintenance windows) + +```yaml +budgets: + - nodes: "0" + schedule: "0 9 * * 1-5" + duration: 8h +``` + +Block ALL voluntary disruption during business hours (Mon-Fri 09:00-17:00 UTC). Disruption only happens outside this window. This gives you change-window discipline without manual intervention. + +### Reason-specific budgets + +```yaml +budgets: + - nodes: "0" + schedule: "0 9 * * 1-5" + duration: 8h + reasons: + - Drifted + - Underutilized +``` + +Scope the budget to specific disruption reasons. This example blocks drift remediation and consolidation during business hours, but still allows empty node removal (cost savings with zero risk). + +Valid reasons: `Drifted`, `Underutilized`, `Empty`. + +## How overlapping budgets work + +When multiple budgets match the current time and disruption reason, the **most restrictive wins**. This lets you layer policies: + +1. Baseline: `nodes: "10%"` (always active, caps total disruption) +2. Business hours: `nodes: "0"` for `Underutilized` and `Drifted` (blocks risky disruptions during peak) +3. Maintenance window: `nodes: "33%"` for `Drifted` (opens a wider window on weekends for drift remediation) + +At 10am Monday, budgets 1 and 2 both apply. Budget 2 says "0" for Underutilized, so no consolidation happens even though budget 1 would allow 10%. + +At 2am Saturday, budgets 1 and 3 apply. Budget 3 says "33%" for Drifted, but budget 1 says "10%" for everything. The 10% cap wins because it is more restrictive. + +## Schedule format + +Schedules use standard cron syntax (5 fields) with a `duration` that defines how long the budget is active after the cron trigger fires. + +``` +schedule: "0 9 * * 1-5" # Fire at 09:00 UTC Monday-Friday +duration: 8h # Active for 8 hours (until 17:00 UTC) +``` + +If no `schedule` and `duration` are set, the budget is always active. + +## When to use + +- **Production clusters** where uncontrolled consolidation causes request failures +- **Compliance environments** requiring change-window discipline +- **Mixed workloads** where some disruption reasons are safe (Empty) but others are risky (Underutilized) +- **Large fleets** where simultaneous node replacement would exhaust IP addresses or ENIs +- **Stateful workloads** where PV reattachment takes time and parallel disruption causes data unavailability + +## Deploy + +```bash +kubectl apply -f advanced-budgets-nodepool.yaml + +# Verify the disruption policy +kubectl get nodepool production-nodepool -o yaml | grep -A 30 disruption +``` + +## What to observe + +```bash +# Check current disruption budget state +kubectl get nodepool production-nodepool -o jsonpath='{.status.disruption}' + +# Watch karpenter logs for budget enforcement +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -f | grep "budget\|disruption" + +# During business hours, verify no consolidation occurs +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "blocked by budget" + +# During maintenance window, verify drift remediation proceeds +kubectl get nodes --sort-by=.metadata.creationTimestamp +``` diff --git a/examples/disruption-budgets/advanced-budgets-nodepool.yaml b/examples/disruption-budgets/advanced-budgets-nodepool.yaml new file mode 100644 index 0000000..af1e378 --- /dev/null +++ b/examples/disruption-budgets/advanced-budgets-nodepool.yaml @@ -0,0 +1,44 @@ +apiVersion: karpenter.sh/v1 +kind: NodePool +metadata: + name: production-nodepool +spec: + template: + spec: + nodeClassRef: + group: eks.amazonaws.com + kind: NodeClass + name: graviton-nodeclass + requirements: + - key: "eks.amazonaws.com/instance-category" + operator: In + values: ["c", "m", "r"] + - key: "kubernetes.io/arch" + operator: In + values: ["arm64"] + taints: + - key: "arm64" + value: "true" + effect: "NoSchedule" + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 120s + budgets: + # Baseline: never disrupt more than 10% of nodes at once + - nodes: "10%" + # Business hours (Mon-Fri 09:00-17:00 UTC): block all voluntary disruption + # except for empty nodes (cost savings still apply) + - nodes: "0" + schedule: "0 9 * * 1-5" + duration: 8h + reasons: + - Drifted + - Underutilized + # Weekend maintenance window: allow up to 33% disruption for drift remediation + - nodes: "33%" + schedule: "0 2 * * 6" + duration: 4h + reasons: + - Drifted diff --git a/examples/observability/README.md b/examples/observability/README.md new file mode 100644 index 0000000..6f5957d --- /dev/null +++ b/examples/observability/README.md @@ -0,0 +1,168 @@ +# Observability with CloudWatch Container Insights + +This guide explains how EKS Auto Mode integrates with Amazon CloudWatch Container Insights to provide full-stack observability for your cluster without managing any monitoring infrastructure yourself. + +## What Container Insights Provides + +CloudWatch Container Insights delivers observability across three pillars: + +**Metrics** — CPU, memory, network, and disk utilization at every level of the hierarchy: cluster, node, pod, and individual container. These metrics are collected at 1-minute intervals and stored in the `ContainerInsights` CloudWatch metrics namespace. + +**Logs** — Pod stdout/stderr logs are shipped to CloudWatch Logs automatically. This gives you a centralized, searchable log store without deploying a separate logging stack. + +**Application Signals (Distributed Tracing)** — Auto-instrumentation for Java, Python, Node.js, and .NET applications. When enabled, traces flow to CloudWatch Application Signals, giving you service maps, latency percentiles, and error rates across microservices. + +## How It Works in Auto Mode + +EKS Auto Mode manages the node lifecycle (provisioning, scaling, patching, termination). The `amazon-cloudwatch-observability` EKS addon handles the observability plane: + +1. The addon deploys a **CloudWatch agent DaemonSet** on every node. This agent collects container-level metrics and forwards them to CloudWatch Metrics. +2. A **Fluent Bit sidecar** captures pod logs and streams them to CloudWatch Logs. +3. An **OpenTelemetry collector** (optional, via Application Signals) collects traces and sends them to X-Ray/Application Signals. + +Because Auto Mode manages nodes and the addon manages observability, you get a fully hands-off monitoring stack. No Helm charts to maintain, no Prometheus to scale, no Fluentd config files to debug. + +## What You Get Out of the Box + +Once enabled, the following are created automatically: + +### CloudWatch Log Groups + +| Log Group | Contents | +|-----------|----------| +| `/aws/containerinsights//application` | Pod stdout/stderr logs | +| `/aws/containerinsights//performance` | Cadvisor and kubelet metrics in structured JSON | +| `/aws/containerinsights//dataplane` | Kubelet, kube-proxy, and container runtime logs | +| `/aws/containerinsights//host` | Node-level system logs | + +### CloudWatch Metrics Namespace + +All metrics land in `ContainerInsights` with dimensions for ClusterName, Namespace, PodName, and ContainerName. Key metrics include: + +- `pod_cpu_utilization`, `pod_memory_utilization` +- `node_cpu_utilization`, `node_memory_utilization` +- `pod_network_rx_bytes`, `pod_network_tx_bytes` +- `cluster_node_count`, `cluster_failed_node_count` + +### Container Insights Dashboard + +The CloudWatch console provides a pre-built Container Insights dashboard at: + +``` +CloudWatch > Container Insights > Performance monitoring +``` + +This shows top resource consumers, pod restart trends, and node capacity in a single pane. + +## Cost Awareness + +CloudWatch Container Insights is metered. Key cost drivers: + +| Dimension | Cost Driver | Mitigation | +|-----------|-------------|------------| +| **Metrics** | Custom metrics at $0.30/metric/month (first 10k) | Metrics are per pod/container; large clusters generate many | +| **Logs** | Ingestion at $0.50/GB + storage at $0.03/GB/month | Set log retention policies (default is never-expire) | +| **Traces** | $1.00 per million traces sampled | Use sampling rules to reduce volume | + +**Recommendations by environment:** + +- **Dev/Staging** — Enable with defaults. Cost is minimal for small clusters. Good learning environment. +- **Production** — Set CloudWatch Logs retention to 30 days. Use metric filters to drop noisy metrics. Configure trace sampling at 5-10% for high-throughput services. +- **Cost-sensitive** — Consider enabling metrics only (disable logs/traces) or use the Enhanced Observability tier selectively. + +Set log retention via AWS CLI after deployment: + +```bash +aws logs put-retention-policy --log-group-name /aws/containerinsights//application --retention-in-days 30 +``` + +## Application Signals (Distributed Tracing) + +Application Signals provides auto-instrumentation without code changes. To enable tracing for a workload, add an annotation to your pod spec: + +**Java:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-java: "true" +``` + +**Python:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-python: "true" +``` + +**Node.js:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-nodejs: "true" +``` + +**.NET:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-dotnet: "true" +``` + +Once annotated, pods are automatically instrumented on next restart. Traces appear in: + +``` +CloudWatch > Application Signals > Services +``` + +You get service maps, latency histograms, error rates, and dependency graphs with zero code changes. + +--- + +## Enable + +```bash +terraform apply -var='enable_observability=true' +``` + +This creates the `amazon-cloudwatch-observability` EKS addon and attaches the `CloudWatchAgentServerPolicy` to the node IAM role. + +## Verify + +Check that the CloudWatch agent pods are running: + +```bash +kubectl get pods -n amazon-cloudwatch +``` + +Expected output shows `amazon-cloudwatch-observability-controller-manager` and `cloudwatch-agent` DaemonSet pods (one per node). + +Confirm metrics are flowing: + +```bash +aws cloudwatch list-metrics --namespace ContainerInsights --dimensions Name=ClusterName,Value= --region +``` + +## Explore + +Once deployed, explore these CloudWatch console paths: + +- **Container Insights dashboard:** `CloudWatch > Container Insights > Performance monitoring` +- **Pod logs:** `CloudWatch > Logs > Log groups > /aws/containerinsights//application` +- **Application Signals:** `CloudWatch > Application Signals > Services` +- **Metrics explorer:** `CloudWatch > Metrics > ContainerInsights` + +Direct console URL (also available as a Terraform output): + +``` +https://.console.aws.amazon.com/cloudwatch/home?region=#container-insights:infrastructure +``` + +## Disable + +To remove the observability addon and stop incurring costs: + +```bash +terraform apply -var='enable_observability=false' +``` + +This removes the addon and detaches the IAM policy. Existing logs and metrics in CloudWatch are retained according to their retention settings. diff --git a/examples/static-capacity/README.md b/examples/static-capacity/README.md new file mode 100644 index 0000000..42c5a7a --- /dev/null +++ b/examples/static-capacity/README.md @@ -0,0 +1,80 @@ +# Static Capacity Pools + +## What are static capacity pools? + +Setting `spec.replicas` on a NodePool tells EKS Auto Mode to maintain exactly N nodes at all times, regardless of pod demand. These nodes are never consolidated away when empty. They exist whether workloads are scheduled on them or not. + +```yaml +spec: + replicas: 2 # Always maintain exactly 2 nodes +``` + +## Why they exist + +Some workloads need guaranteed capacity available at all times: + +- **Always-on inference endpoints** that must respond in milliseconds, not minutes +- **Database replicas** where cold-start means replay from WAL/snapshot +- **License-bound software** where the license is tied to a running host +- **Latency-sensitive services** where waiting for a node to provision (30-90s) is unacceptable + +Dynamic scaling introduces cold-start latency. For workloads where that latency violates SLOs, static pools eliminate it entirely. + +## How it differs from dynamic pools + +| Behavior | Dynamic Pool | Static Pool | +|----------|-------------|-------------| +| Scale-from-zero | Yes | No -- always N nodes | +| Consolidation | Removes underutilized nodes | Never removes nodes | +| Scaling trigger | Pending pods | Manual (`kubectl scale nodepool`) | +| Cost model | Pay only for what you use | Pay for N nodes 24/7 | +| Cold-start risk | Yes (30-90s node provision) | None | + +You can resize a static pool at any time: + +```bash +kubectl scale nodepool static-gpu-nodepool --replicas=4 +``` + +## The trade-off + +You pay for N nodes 24/7 whether they are fully loaded or completely idle. Use static pools only where: + +> **Cost of idle nodes < Cost of cold-start latency** + +If your workload can tolerate 60-90 seconds of scale-up time, a dynamic pool is cheaper. + +## When to use + +- Always-on model serving (vLLM, TGI, Triton) +- Stateful databases (PostgreSQL, Redis cluster nodes) +- License servers (FlexLM, RLM) +- Baseline capacity layer -- handle steady-state traffic with static nodes, burst traffic with a separate dynamic pool + +## Deploy + +```bash +# Render the template (if using Terraform templatefile) +terraform apply + +# Or apply directly after filling variables +kubectl apply -f static-nodepool.yaml +``` + +## What to observe + +```bash +# Verify exactly 2 nodes exist for this pool (even with zero pods) +kubectl get nodes -l karpenter.sh/nodepool=static-gpu-nodepool + +# Confirm replicas are set +kubectl get nodepool static-gpu-nodepool -o jsonpath='{.spec.replicas}' + +# Watch that consolidation does NOT touch these nodes +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -f | grep static-gpu + +# Scale up +kubectl scale nodepool static-gpu-nodepool --replicas=4 +# Watch a new node provision immediately (no pending pod required) +kubectl get nodes -w -l karpenter.sh/nodepool=static-gpu-nodepool +``` diff --git a/examples/static-capacity/static-nodepool.yaml.tpl b/examples/static-capacity/static-nodepool.yaml.tpl new file mode 100644 index 0000000..fb1e57a --- /dev/null +++ b/examples/static-capacity/static-nodepool.yaml.tpl @@ -0,0 +1,45 @@ +--- +apiVersion: eks.amazonaws.com/v1 +kind: NodeClass +metadata: + name: static-gpu-nodeclass +spec: + role: ${node_iam_role_name} + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: ${cluster_name} + securityGroupSelectorTerms: + - tags: + aws:eks:cluster-name: ${cluster_name} + tags: + ${indent(4, yamlencode(tags))} +%{ if kms_key_id != "" ~} + ephemeralStorage: + kmsKeyID: ${kms_key_id} +%{ endif ~} +--- +apiVersion: karpenter.sh/v1 +kind: NodePool +metadata: + name: static-gpu-nodepool +spec: + replicas: 2 + template: + spec: + nodeClassRef: + group: eks.amazonaws.com + kind: NodeClass + name: static-gpu-nodeclass + requirements: + - key: "eks.amazonaws.com/instance-family" + operator: In + values: ["g6e"] + - key: "karpenter.sh/capacity-type" + operator: In + values: ["on-demand"] + taints: + - key: "nvidia.com/gpu" + value: "true" + effect: NoSchedule + limits: + cpu: 500 diff --git a/misc/website/docs/architecture/cleanup.md b/misc/website/docs/architecture/cleanup.md new file mode 100644 index 0000000..990d7c0 --- /dev/null +++ b/misc/website/docs/architecture/cleanup.md @@ -0,0 +1,71 @@ +--- +sidebar_position: 2 +title: Cleanup Playbook +--- + +# EKS Auto Mode — Cleanup Playbook + +## Why `terraform destroy` alone fails + +`aws_eks_cluster` deletion tears down the control plane but does NOT drain in-cluster workloads. CSI drivers and the ALB Controller lose their API server mid-reconcile, so PVCs become orphaned EBS volumes, Ingresses become orphaned ALBs/TGs, and EFS access points leak. This applies equally to Auto Mode and Standard Mode clusters. + +## Drain-before-destroy order + +1. **Delete all Ingresses** — triggers ALB Controller finalizers; wait 30-60s for ALB/TG deletion via `describe-load-balancers` polling. +2. **Delete LoadBalancer-type Services** — NLBs live here, separate from Ingress ALBs. +3. **Delete all PVCs** — triggers EBS CSI volume detach+delete; poll `describe-volumes` until cluster-tagged vols are gone. +4. **Remove KEDA ScaledObjects** — prevents rescaling during drain (skip if no KEDA CRDs). +5. **Uninstall Helm releases** — triggers external-dns record cleanup via finalizers. +6. **Delete custom NodePools + NodeClaims** — triggers node termination; skip the `general-purpose` pool (AWS-managed in Auto Mode). +7. **Wait for LB cleanup** — poll `elbv2.k8s.aws/cluster=` tagged LBs (up to 3 min). +8. **Run `terraform destroy`** (KEDA sub-module first, then main). + +## Post-destroy orphan checklist + +Delete in this order (dependencies before parents): + +| # | Resource type | How to find | Notes | +|---|---|---|---| +| 1 | Target Groups | `elbv2 describe-tags` with `elbv2.k8s.aws/cluster=` | Delete before LBs | +| 2 | Load Balancers | Same tag filter | Delete listeners first | +| 3 | EC2 Instances | Tags: `karpenter.sh/discovery`, `aws:eks:cluster-name`, `kubernetes.io/cluster/` | Terminate | +| 4 | EBS Volumes | `status=available` + tag `kubernetes.io/cluster/` | Detached = safe to delete | +| 5 | ENIs | `status=available` + description/SG containing cluster name | | +| 6 | Security Groups | Name or tag contains cluster name | Revoke all rules first | +| 7 | IAM Roles | Name contains cluster name (skip `AWSServiceRole*`) | Detach policies first | +| 8 | OIDC Providers | ARN contains the 32-char OIDC ID (capture while cluster is alive) | | +| 9 | CloudWatch Logs | Prefix `/aws/eks/` and `/aws/containerinsights/` | | +| 10 | KMS Keys | Alias contains cluster name | Schedule deletion (7-day min) | +| 11 | Route53 Records | A/CNAME under `*.` + TXT with `txtOwnerId` | | +| 12 | SQS Queues | Queue name prefix = cluster name | | +| 13 | Launch Templates | Name or `aws:eks:cluster-name` tag | | +| 14 | Elastic IPs | Unassociated + name/tag containing cluster name | | + +## Usage + +```bash +./scripts/cleanup.sh [OPTIONS] + +--dry-run Show what would be deleted without deleting +--yes Skip all prompts (full non-interactive delete) +--keep-storage Preserve PVC/EBS/EFS resources +--region REGION Override auto-detected region +--cluster-name N Override auto-detected cluster name +--domain DOMAIN Full domain for Route53 sweep +--skip-terraform Orphan sweep only (TF already destroyed) +--skip-keda Skip KEDA sub-module destroy +``` + +Common invocations: + +```bash +./scripts/cleanup.sh --dry-run # preview +./scripts/cleanup.sh --yes # full non-interactive teardown +./scripts/cleanup.sh --skip-terraform --yes # post-hoc orphan sweep +``` + +## Key gotchas (Auto Mode specific) + +- **Managed `default` NodeClass tags revert silently** — always use a custom NodeClass for durable tags. +- **OIDC provider ID must be captured while cluster is alive** — script does this in Phase 1. +- **`provider default_tags` don't reach EKS primary SG** — pass via `cluster_tags`. diff --git a/misc/website/docs/architecture/index.md b/misc/website/docs/architecture/index.md new file mode 100644 index 0000000..ca1f90c --- /dev/null +++ b/misc/website/docs/architecture/index.md @@ -0,0 +1,14 @@ +--- +sidebar_position: 5 +title: Architecture +--- + +# Architecture + +Deep-dive reference documents covering EKS Auto Mode operational patterns. + +| Document | Description | +|----------|-------------| +| [5-Layer Tagging](./tagging) | How to tag every resource in an Auto Mode cluster | +| [Cleanup Playbook](./cleanup) | Safe teardown: drain, destroy, sweep | +| [Security Considerations](./security) | Checkov scan results and documented exceptions | diff --git a/misc/website/docs/architecture/security.md b/misc/website/docs/architecture/security.md new file mode 100644 index 0000000..2ba7c12 --- /dev/null +++ b/misc/website/docs/architecture/security.md @@ -0,0 +1,19 @@ +--- +sidebar_position: 3 +title: Security Considerations +--- + +# Security Considerations + +Our code is continuously scanned using [Checkov](https://www.checkov.io/5.Policy%20Index/kubernetes.html). The following security considerations are documented for transparency: + +| Check | Details | Reason | +|-------|---------|--------| +| CKV_TF_1 | Ensure Terraform module sources use a commit hash | For easy experimentation, we set version of module instead of a commit hash. Consider implementing a commit hash in production. [Read more](https://medium.com/boostsecurity/erosion-of-trust-unmasking-supply-chain-vulnerabilities-in-the-terraform-registry-2af48a7eb2) | +| CKV2_K8S_6 | Minimize pods without NetworkPolicy | All Pod-to-Pod communication is allowed for experimentation. Amazon VPC CNI supports [Kubernetes Network Policies](https://aws.amazon.com/blogs/containers/amazon-vpc-cni-now-supports-kubernetes-network-policies/) for production. | +| CKV_K8S_8 | Liveness Probe Should be Configured | No health checks for experimentation. Implement [health checks](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/) in production. | +| CKV_K8S_9 | Readiness Probe Should be Configured | Same as above. | +| CKV_K8S_22 | Use read-only filesystem where possible | Exception for workloads requiring R/W. [Configure read-only root](https://docs.aws.amazon.com/eks/latest/best-practices/pod-security.html#_configure_your_images_with_read_only_root_file_system). | +| CKV_K8S_23 | Minimize root containers | Default root containers for demo compatibility. For production, use `runAsNonRoot: true`. | +| CKV_K8S_37 | Minimize containers with capabilities | Exception for workloads requiring added capability. See [capabilities guidance](https://docs.aws.amazon.com/eks/latest/best-practices/pod-security.html#_linux_capabilities). | +| CKV_K8S_40 | Run as high UID | Public container images used as-is. See [how to define UID](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-pod). | diff --git a/misc/website/docs/architecture/tagging.md b/misc/website/docs/architecture/tagging.md new file mode 100644 index 0000000..80cfcde --- /dev/null +++ b/misc/website/docs/architecture/tagging.md @@ -0,0 +1,67 @@ +--- +sidebar_position: 1 +title: 5-Layer Tagging +--- + +# EKS Auto Mode — 5-Layer Tagging + +## The problem + +Terraform `provider "aws" { default_tags {} }` only reaches resources the provider creates directly. K8s controllers (Auto Mode's built-in Karpenter, EBS CSI, ALB controller) call AWS APIs under the cluster IAM role, completely outside Terraform's visibility. Without explicit per-controller configuration, EC2 instances, EBS volumes, ENIs, ALBs, and security groups all come up untagged. + +## 5-layer pattern + +1. **Provider `default_tags`** — covers all TF-direct resources (VPC, subnets, EKS cluster, IAM roles, KMS, S3, Lambda, ACM, etc.). + - Code: `terraform/main.tf` provider block. + - Gotcha: does NOT cascade to k8s-controller-created resources. Also blocked by `lifecycle { ignore_changes = [tags] }`. + +2. **EKS `cluster_tags`** — tags the EKS-managed primary security group via `aws_ec2_tag` in the eks module (provider `default_tags` cannot reach it). + - Code: `cluster_tags` input on the `module.eks` block in `terraform/eks.tf`. + - Gotcha: must be passed as `cluster_tags`, not `tags`. The primary SG is created by the EKS service, not TF. + +3. **Custom NodeClass `spec.tags`** — tags EC2 instances, root EBS volumes, and ENIs launched by Auto Mode's Karpenter. + - Code: `nodepool-templates/*.yaml.tpl` (each template includes `spec.tags: ${indent(4, yamlencode(tags))}`). + - **WARNING: never patch the managed `default` NodeClass.** EKS's reconciler silently reverts custom fields within minutes. Always create a named custom NodeClass you own. + - NodePools must reference the custom NodeClass via `nodeClassRef.name`. + +4. **StorageClass `tagSpecification_N`** — tags EBS volumes created by PVCs. + - Code: `terraform/tagging.tf` (`kubectl_manifest.storageclass_ebs`). + - Gotcha: StorageClass `parameters` is immutable. Updates require delete + recreate. Existing PVCs keep their original (untagged) volumes. + +5. **IngressClassParams `spec.tags`** — tags ALBs, target groups, listeners, and listener-rule SGs created by Auto Mode's built-in ALB controller. + - Code: `terraform/ingressclass.tf` (`kubectl_manifest.ingressclassparams_*`). + - Gotcha: per-IngressClassParams, so tag each one if you have multiple (internal vs internet-facing). + +## IAM tagging policy + +**Load-bearing.** Without it, layers 3-5 silently fail. + +Auto Mode's managed policies (`AmazonEKSComputePolicy`, `AmazonEKSBlockStoragePolicy`, `AmazonEKSLoadBalancingPolicy`, `AmazonEKSNetworkingPolicy`) use `ForAllValues:StringEquals "aws:TagKeys"` to allowlist only `eks:*`, `kubernetes.io/*`, `karpenter.sh/*` tag keys. Any request carrying a custom key (e.g. `auto-delete`) falls outside the allowlist, so the managed Allow does not match and the call is DENIED (`UnauthorizedOperation`). + +Fix: the EKS Terraform module (v20.31+) handles this automatically via `enable_auto_mode_custom_tags = true` (default). It attaches a managed policy to the cluster role with the same actions scoped via `aws:RequestTag/eks:eks-cluster-name = ${aws:PrincipalTag/eks:eks-cluster-name}`. + +- Code: `terraform/eks.tf` (`enable_auto_mode_custom_tags = true` on the module block) +- [AWS docs: cluster IAM role tag propagation](https://docs.aws.amazon.com/eks/latest/userguide/auto-cluster-iam-role.html#tag-prop) + +## Known gap + +**LoadBalancer-type Services** (not Ingress) need the annotation `service.beta.kubernetes.io/aws-load-balancer-additional-resource-tags` set per-Service. There is no cluster-wide default for this. + +## `default_tags` does NOT reach + +- EC2 instances launched by Karpenter/Auto Mode +- EBS volumes created by the EBS CSI driver (PVC-provisioned) +- ENIs created by the VPC CNI +- ALBs/NLBs created by the ALB controller +- Target groups, listeners, listener rules +- Security groups created by the ALB controller or EKS networking +- EKS primary security group (use `cluster_tags` / `aws_ec2_tag`) +- EFS access points created by the EFS CSI driver +- Shield protections created by the ALB controller + +## Debugging: tag not landing? + +1. Check CloudTrail for `ErrorCode=UnauthorizedOperation` on RunInstances/CreateVolume — means IAM policy is missing. +2. Check if someone patched the managed `default` NodeClass — it reverts silently. +3. Verify StorageClass was recreated (parameters are immutable, `kubectl apply` on changed params errors). +4. For existing resources: tags only apply to future creates. Retag imperatively with `aws ec2 create-tags` / `aws elbv2 add-tags`. diff --git a/misc/website/docs/contributing.md b/misc/website/docs/contributing.md new file mode 100644 index 0000000..ee9cd4e --- /dev/null +++ b/misc/website/docs/contributing.md @@ -0,0 +1,26 @@ +--- +sidebar_position: 10 +title: Contributing +--- + +# Contributing + +Contributions welcome! Please read our [Contributing Guidelines](https://github.com/aws-samples/sample-aws-eks-auto-mode/blob/main/CONTRIBUTING.md) and [Code of Conduct](https://github.com/aws-samples/sample-aws-eks-auto-mode/blob/main/CODE_OF_CONDUCT.md). + +## Adding a new example + +1. Create a directory under `examples//` +2. Add a `README.md` explaining the "why" and "how" +3. Include deployable manifests (`.yaml` or `.yaml.tpl` for templated) +4. Add a row to the examples table in the root `README.md` +5. Copy the README content into `misc/website/docs/examples/.md` with Docusaurus frontmatter + +## Local docs development + +```bash +cd misc/website +npm install +npm start +``` + +The site opens at `http://localhost:3000/sample-aws-eks-auto-mode/`. diff --git a/misc/website/docs/examples/batch-jobs.md b/misc/website/docs/examples/batch-jobs.md new file mode 100644 index 0000000..dddd6d2 --- /dev/null +++ b/misc/website/docs/examples/batch-jobs.md @@ -0,0 +1,85 @@ +--- +sidebar_position: 9 +title: "Batch Jobs: Protecting Long-Running Workloads from Disruption" +--- + +# Batch Jobs: Protecting Long-Running Workloads from Disruption + +## The problem + +Karpenter (and EKS Auto Mode) continuously consolidates underutilized nodes. This is great for cost optimization, but catastrophic for long-running batch jobs. A 6-hour ML training run evicted at hour 5 wastes 5 hours of GPU compute. An ETL pipeline disrupted mid-write can leave data in an inconsistent state. + +Without protection, consolidation treats your 8-hour training job the same as a stateless web server -- just another pod to reschedule. + +## How `karpenter.sh/do-not-disrupt` works + +Adding this annotation to a pod's metadata tells Auto Mode: "do not voluntarily evict this pod for consolidation or drift remediation." + +```yaml +metadata: + annotations: + karpenter.sh/do-not-disrupt: "true" +``` + +When this annotation is present on any pod running on a node, that entire node becomes protected from voluntary disruption. The node will not be consolidated, drifted, or removed for emptiness as long as the annotated pod is running. + +## Scope of protection + +| Disruption Type | Protected? | Example | +|----------------|-----------|---------| +| Consolidation (underutilized) | Yes | Karpenter wants to bin-pack pods onto fewer nodes | +| Drift remediation | Yes | AMI updated, Karpenter wants to roll nodes | +| Empty node removal | Yes | All other pods drained, but annotated pod remains | +| Spot interruption | **No** | AWS reclaims the instance with 2-min warning | +| Node health failure | **No** | EC2 status check fails | +| Manual `kubectl drain` | **No** | Human or automation explicitly drains | + +**Key insight**: this protects against the scheduler's optimization decisions, not against infrastructure failures. For Spot protection, use on-demand instances. For health failures, implement checkpointing. + +## Why annotation vs taint + +These solve different problems: + +- **Taints** control which pods CAN schedule onto a node (admission control) +- **do-not-disrupt** controls whether a node with this pod CAN be consolidated (eviction control) + +A GPU taint prevents CPU pods from landing on GPU nodes. `do-not-disrupt` prevents Karpenter from evicting your training job to consolidate that GPU node. + +## When to use + +- ML training jobs (hours to days) +- ETL pipelines with expensive restart costs +- Video transcoding (long-running, stateful progress) +- Database migrations or backfills +- Any batch workload where: **restart cost > idle node cost** + +## Deploy + +```bash +kubectl apply -f batch-training-job.yaml + +# Verify the job is running +kubectl get jobs -n batch-jobs +kubectl get pods -n batch-jobs -o wide +``` + +## What to observe + +```bash +# Confirm the annotation is on the running pod +kubectl get pod -n batch-jobs -l app=ml-training -o jsonpath='{.items[0].metadata.annotations}' + +# Watch karpenter logs -- you should see "cannot disrupt" messages for this node +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -f | grep "cannot disrupt\|do-not-disrupt" + +# Identify which node the job landed on +NODE=$(kubectl get pod -n batch-jobs -l app=ml-training -o jsonpath='{.items[0].spec.nodeName}') +echo "Protected node: $NODE" + +# Verify that node is NOT being considered for consolidation +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "$NODE" + +# Once the job completes, the annotation disappears with the pod. +# The node becomes eligible for consolidation again. +kubectl get nodes -w # Watch the node get consolidated after job completion +``` diff --git a/misc/website/docs/examples/capacity-reservation.md b/misc/website/docs/examples/capacity-reservation.md new file mode 100644 index 0000000..22c60fb --- /dev/null +++ b/misc/website/docs/examples/capacity-reservation.md @@ -0,0 +1,148 @@ +--- +sidebar_position: 7 +title: "On-Demand Capacity Reservation (ODCR) Targeting in EKS Auto Mode" +--- + +# On-Demand Capacity Reservation (ODCR) Targeting in EKS Auto Mode + +## What are ODCRs? + +On-Demand Capacity Reservations (ODCRs) let you reserve compute capacity in a specific Availability Zone for a specific instance type. Once created, the capacity is held for you regardless of whether any instances are running against it. You pay the on-demand rate for the reserved capacity whether it is used or not, so the goal is to ensure your workloads actually land on the reservation rather than launching as regular on-demand instances beside it. + +ODCRs are not the same as Reserved Instances or Savings Plans. Those are billing constructs that apply discounts retroactively. An ODCR is a physical capacity guarantee: the hosts are allocated and waiting for you. + +## Why This Matters for ML/GPU Workloads + +GPU instance families (p5, g6e, inf2, trn1) are frequently capacity-constrained in popular regions. You may submit a RunInstances call and receive an InsufficientInstanceCapacity error because the AZ is out of that type. + +ODCRs solve this by pre-allocating the capacity. However, if your Karpenter/Auto Mode NodeClass does not explicitly target the reservation, launched instances will consume regular on-demand capacity and leave the ODCR idle (still costing you money). Correctly configuring `capacityReservationSelectorTerms` ensures nodes preferentially land on your reserved capacity. + +Common scenarios: + +- Multi-day distributed training jobs on p5.48xlarge +- Batch inference pipelines with predictable GPU demand +- Real-time inference with guaranteed baseline capacity +- Compliance requirements mandating dedicated or reserved tenancy + +## How `capacityReservationSelectorTerms` Works + +The NodeClass field `capacityReservationSelectorTerms` tells Auto Mode which ODCRs to target when launching nodes. There are three targeting strategies: + +### 1. Target by Reservation ID (most specific) + +```yaml +capacityReservationSelectorTerms: + - id: cr-0a1b2c3d4e5f67890 +``` + +Use this when you have a single known reservation and want deterministic placement. + +### 2. Target by Tags (flexible, recommended) + +```yaml +capacityReservationSelectorTerms: + - tags: + purpose: ml-training + team: platform +``` + +Use this when you manage multiple reservations with a tagging convention. As you create or retire ODCRs, the NodeClass automatically picks up matching ones without manifest changes. + +### 3. Target by Owner (for shared reservations) + +```yaml +capacityReservationSelectorTerms: + - owner: 123456789012 +``` + +Use this when another account shares ODCRs with you via AWS Resource Access Manager (RAM). + +You can combine multiple terms; Auto Mode evaluates them in order and uses the first reservation with available capacity. + +## Fallback Behavior + +If all matching ODCRs are fully utilized (every slot occupied by a running instance), Auto Mode falls back to launching regular on-demand instances. Your workloads still schedule and run; they simply do not benefit from the reservation guarantee. + +This means: + +- Pods are never stuck Pending solely because a reservation is full. +- You do not need separate "overflow" NodePools for the non-ODCR case. +- The same NodePool handles both reserved and unreserved launches transparently. + +Monitor the `UsedInstanceCount` vs `TotalInstanceCount` in the EC2 Capacity Reservations console to see whether your ODCRs are being utilized. + +## When to Use + +| Scenario | Why ODCR helps | +|----------|---------------| +| GPU training jobs (multi-hour/day) | Guarantees capacity won't be reclaimed mid-job | +| Batch inference with known parallelism | Ensures all workers launch simultaneously | +| Real-time inference baseline | Baseline capacity is always available; burst goes on-demand | +| Compliance / dedicated tenancy | Some regulations require pre-allocated, non-shared capacity | +| Event-driven spikes (launches, demos) | Reserve ahead, release after the event | + +## Prerequisites + +1. **An existing ODCR** in the target AZ for the instance type you need. + Create one via the EC2 console or CLI: + ``` + aws ec2 create-capacity-reservation --instance-type g6e.xlarge --instance-platform Linux/UNIX --availability-zone us-west-2a --instance-count 4 --tag-specifications 'ResourceType=capacity-reservation,Tags=[{Key=purpose,Value=ml-training}]' + ``` + See [AWS docs: Create a Capacity Reservation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-reservations.html#capacity-reservations-create) for full options. + +2. **Appropriate IAM permissions** on the node role to describe and use capacity reservations: + - `ec2:DescribeCapacityReservations` + - `ec2:RunInstances` with the reservation target + +3. **Matching AZ and instance type** between the ODCR and the NodePool requirements. Auto Mode will not launch a g6e instance into a p5 reservation. + +4. **Tags on the ODCR** if using tag-based selector terms (recommended for flexibility). + +## Deploy + +1. Render the template with your cluster values: + ``` + terraform output -raw odcr_nodepool_manifest > odcr-nodepool.yaml + ``` + Or substitute the variables manually in `odcr-nodepool.yaml.tpl`. + +2. Apply to your cluster: + ``` + kubectl apply -f odcr-nodepool.yaml + ``` + +3. Launch a GPU workload that tolerates the `nvidia.com/gpu` taint: + ```yaml + tolerations: + - key: "nvidia.com/gpu" + operator: Equal + value: "true" + effect: NoSchedule + resources: + limits: + nvidia.com/gpu: 1 + ``` + +## What to Observe + +1. **EC2 Console > Capacity Reservations**: Watch `Used instance count` increase as Auto Mode launches nodes into the reservation. + +2. **Node labels**: Nodes launched into an ODCR carry standard EC2 metadata. Check instance details: + ``` + aws ec2 describe-instances --instance-ids --query 'Reservations[].Instances[].CapacityReservationId' + ``` + +3. **NodePool counters**: Verify the NodePool's resource usage is increasing: + ``` + kubectl get nodepool odcr-gpu-nodepool -o yaml | grep -A5 status + ``` + +4. **Fallback scenario**: If you scale beyond the reservation size, additional nodes will launch as regular on-demand. The `CapacityReservationId` field will be empty on those instances. + +## Cleanup + +To release ODCR capacity when no longer needed: + +1. Scale down or delete workloads using the GPU taint. +2. Delete the NodePool and NodeClass: `kubectl delete nodepool odcr-gpu-nodepool && kubectl delete nodeclass odcr-gpu-nodeclass` +3. Cancel the capacity reservation: `aws ec2 cancel-capacity-reservation --capacity-reservation-id cr-0a1b2c3d4e5f67890` diff --git a/misc/website/docs/examples/cost-optimization.md b/misc/website/docs/examples/cost-optimization.md new file mode 100644 index 0000000..65f2020 --- /dev/null +++ b/misc/website/docs/examples/cost-optimization.md @@ -0,0 +1,127 @@ +--- +sidebar_position: 5 +title: "Cost Optimization Patterns for EKS Auto Mode" +--- + +# Cost Optimization Patterns for EKS Auto Mode + +This example demonstrates two key cost optimization patterns: **OD/Spot mixed capacity** and **overprovision headroom via pause pods**. + +--- + +## Why OD/Spot Split Matters + +EC2 Spot instances are 60-90% cheaper than On-Demand, but AWS can reclaim them with 2 minutes notice. For production workloads you need a mix: + +- **On-Demand** provides a stable baseline that won't disappear mid-request. +- **Spot** provides cheap burst capacity for stateless, fault-tolerant work. + +EKS Auto Mode (via Karpenter) labels nodes with `karpenter.sh/capacity-type: on-demand` or `karpenter.sh/capacity-type: spot`. You use topology spread constraints to distribute pods across both capacity types evenly. + +--- + +## How Topology Spread Constraints Work + +```yaml +topologySpreadConstraints: + - maxSkew: 1 + topologyKey: karpenter.sh/capacity-type + whenUnsatisfiable: DoNotSchedule + labelSelector: + matchLabels: + app: web-mixed-capacity +``` + +| Field | Meaning | +|-------|---------| +| `maxSkew` | Maximum allowed difference in pod count between any two topology domains. A skew of 1 means "at most 1 pod difference between OD and Spot." | +| `topologyKey` | The node label that defines topology domains. Using `karpenter.sh/capacity-type` creates two domains: `on-demand` and `spot`. | +| `whenUnsatisfiable` | `DoNotSchedule` blocks new pods if they'd violate the skew (hard constraint). `ScheduleAnyway` is a soft preference. | +| `labelSelector` | Which pods count toward the spread calculation. Must match the pod's own labels. | + +With 6 replicas and maxSkew 1, you get 3 pods on OD nodes and 3 on Spot nodes. If a Spot node is reclaimed, you still have 3 OD pods serving traffic while replacements schedule. + +--- + +## What Overprovision / Headroom Is + +When a new pod arrives and no node has capacity, EKS Auto Mode launches a new node. That takes 1-2 minutes. For latency-sensitive scale-out, that delay is unacceptable. + +**Overprovision** solves this by keeping spare capacity pre-warmed. You run low-priority "pause" pods that reserve CPU and memory on nodes but do nothing. When a real workload arrives, the scheduler **preempts** the pause pods instantly, giving the real pod immediate access to already-running node capacity. + +The pause pods use the `registry.k8s.io/pause:3.9` image — a 500KB container that literally does nothing except hold a resource reservation. + +--- + +## How PriorityClasses and Preemption Work Together + +``` +PriorityClass "pause-pods" → value: -1 (lowest) +Default priority → value: 0 (all normal workloads) +``` + +The scheduler always prefers higher-priority pods. When a real pod (priority 0) cannot be scheduled due to lack of resources, the scheduler looks for lower-priority pods it can evict. The pause pods at priority -1 are always the first victims. + +The flow: +1. Pause pods hold 3 x (1 CPU + 1Gi memory) of headroom on nodes. +2. A real deployment scales up and needs resources. +3. Scheduler evicts pause pods (instant — `terminationGracePeriodSeconds: 0`). +4. Real pods land immediately on the freed capacity. +5. The now-pending pause pods trigger new node creation in the background, restoring headroom. + +--- + +## When to Use Each Pattern + +| Pattern | Use case | +|---------|----------| +| **OD/Spot split** | Stateless HTTP services, batch processors, queue workers — anything that tolerates pod replacement gracefully. | +| **Overprovision headroom** | Latency-sensitive scale-out where waiting 1-2 min for a node is unacceptable: real-time APIs, gaming backends, autoscaled inference endpoints. | + +You can combine both — run pause pods on Spot capacity so your headroom is cheap, while real workloads spread across OD and Spot. + +--- + +## Deploy + +```bash +kubectl apply -f examples/cost-optimization/mixed-od-spot-deployment.yaml +kubectl apply -f examples/cost-optimization/overprovision-pause-pods.yaml +``` + +--- + +## What to Observe + +**For the OD/Spot split deployment:** + +```bash +# Check that pods spread across capacity types +kubectl get pods -n cost-optimization -l app=web-mixed-capacity -o wide + +# Verify nodes have different capacity types +kubectl get nodes -L karpenter.sh/capacity-type + +# Count pods per capacity type +kubectl get pods -n cost-optimization -l app=web-mixed-capacity -o json | \ + jq -r '.items[].spec.nodeName' | \ + xargs -I{} kubectl get node {} -o jsonpath='{.metadata.labels.karpenter\.sh/capacity-type}' | \ + sort | uniq -c +``` + +**For overprovision/headroom:** + +```bash +# Verify pause pods are running and holding resources +kubectl get pods -n cost-optimization -l app=overprovision + +# Check their priority class +kubectl get pods -n cost-optimization -l app=overprovision -o jsonpath='{.items[0].spec.priorityClassName}' + +# Watch preemption in action — scale a real workload and observe events +kubectl get events -n cost-optimization --field-selector reason=Preempted -w + +# Measure scheduling latency (time from creation to running) +kubectl get pods -n cost-optimization -o json | \ + jq '.items[] | select(.status.phase=="Running") | .metadata.name + ": " + (.status.conditions[] | select(.type=="PodScheduled") | .lastTransitionTime)' +``` diff --git a/misc/website/docs/examples/disruption-budgets.md b/misc/website/docs/examples/disruption-budgets.md new file mode 100644 index 0000000..340bc3a --- /dev/null +++ b/misc/website/docs/examples/disruption-budgets.md @@ -0,0 +1,115 @@ +--- +sidebar_position: 10 +title: "Disruption Budgets" +--- + +# Disruption Budgets + +## What disruption budgets are + +Disruption budgets limit how many nodes EKS Auto Mode can voluntarily disrupt at once. Without them, a consolidation decision can drain multiple nodes simultaneously, causing cascading failures as pods compete for remaining capacity. + +They are defined in `spec.disruption.budgets[]` on a NodePool and act as a throttle on voluntary disruption velocity. + +## Types of budgets + +### Percentage-based + +```yaml +budgets: + - nodes: "10%" +``` + +At most 10% of nodes managed by this pool can be disrupting at any time. Scales with your fleet -- 100 nodes means max 10 disrupting simultaneously. + +### Count-based + +```yaml +budgets: + - nodes: "3" +``` + +At most 3 nodes disrupting at once, regardless of fleet size. Use for small pools where percentages don't make sense. + +### Zero budgets with schedules (maintenance windows) + +```yaml +budgets: + - nodes: "0" + schedule: "0 9 * * 1-5" + duration: 8h +``` + +Block ALL voluntary disruption during business hours (Mon-Fri 09:00-17:00 UTC). Disruption only happens outside this window. This gives you change-window discipline without manual intervention. + +### Reason-specific budgets + +```yaml +budgets: + - nodes: "0" + schedule: "0 9 * * 1-5" + duration: 8h + reasons: + - Drifted + - Underutilized +``` + +Scope the budget to specific disruption reasons. This example blocks drift remediation and consolidation during business hours, but still allows empty node removal (cost savings with zero risk). + +Valid reasons: `Drifted`, `Underutilized`, `Empty`. + +## How overlapping budgets work + +When multiple budgets match the current time and disruption reason, the **most restrictive wins**. This lets you layer policies: + +1. Baseline: `nodes: "10%"` (always active, caps total disruption) +2. Business hours: `nodes: "0"` for `Underutilized` and `Drifted` (blocks risky disruptions during peak) +3. Maintenance window: `nodes: "33%"` for `Drifted` (opens a wider window on weekends for drift remediation) + +At 10am Monday, budgets 1 and 2 both apply. Budget 2 says "0" for Underutilized, so no consolidation happens even though budget 1 would allow 10%. + +At 2am Saturday, budgets 1 and 3 apply. Budget 3 says "33%" for Drifted, but budget 1 says "10%" for everything. The 10% cap wins because it is more restrictive. + +## Schedule format + +Schedules use standard cron syntax (5 fields) with a `duration` that defines how long the budget is active after the cron trigger fires. + +``` +schedule: "0 9 * * 1-5" # Fire at 09:00 UTC Monday-Friday +duration: 8h # Active for 8 hours (until 17:00 UTC) +``` + +If no `schedule` and `duration` are set, the budget is always active. + +## When to use + +- **Production clusters** where uncontrolled consolidation causes request failures +- **Compliance environments** requiring change-window discipline +- **Mixed workloads** where some disruption reasons are safe (Empty) but others are risky (Underutilized) +- **Large fleets** where simultaneous node replacement would exhaust IP addresses or ENIs +- **Stateful workloads** where PV reattachment takes time and parallel disruption causes data unavailability + +## Deploy + +```bash +kubectl apply -f advanced-budgets-nodepool.yaml + +# Verify the disruption policy +kubectl get nodepool production-nodepool -o yaml | grep -A 30 disruption +``` + +## What to observe + +```bash +# Check current disruption budget state +kubectl get nodepool production-nodepool -o jsonpath='{.status.disruption}' + +# Watch karpenter logs for budget enforcement +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -f | grep "budget\|disruption" + +# During business hours, verify no consolidation occurs +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter | grep "blocked by budget" + +# During maintenance window, verify drift remediation proceeds +kubectl get nodes --sort-by=.metadata.creationTimestamp +``` diff --git a/misc/website/docs/examples/gpu.md b/misc/website/docs/examples/gpu.md new file mode 100644 index 0000000..50e4ef5 --- /dev/null +++ b/misc/website/docs/examples/gpu.md @@ -0,0 +1,235 @@ +--- +sidebar_position: 3 +title: GPU Workloads on EKS Auto Mode +--- + +# GPU Workloads on EKS Auto Mode + +## Table of Contents +- [Overview](#overview) +- [Architecture](#architecture) +- [Implementation Steps](#implementation-steps) +- [Cleanup](#cleanup) +- [Troubleshooting](#troubleshooting) + +## Overview +[NVIDIA GPUs on Amazon EC2](https://aws.amazon.com/ec2/instance-types/#Accelerated_Computing) supercharge your workloads with powerful GPU acceleration. Key benefits include: + +🚀 **High Performance Computing** +- GPU accelerators from g3, g4, g5, g6, p3, and p4 families +- Optimized for machine learning and graphics workloads +- Ideal for running large language models + +🤖 **AI/ML Capabilities** +- Perfect for GenAI model deployment +- Supports complex deep learning tasks +- Accelerated model inference + +⚙️ **Flexible Configuration** +- Customizable instance types +- Scalable GPU resources +- EKS Auto Mode integration + +This example demonstrates deploying a GenAI model ([Qwen 3 32b fp8](https://huggingface.co/Qwen/Qwen3-32B-FP8)) on EKS Auto Mode. + +> ⚠️ **Prerequisites**: +> - You must have a Hugging Face account with an access token! +> - **GPU Instance Availability**: Many AWS accounts have a default service quota of 0 for p* and g* GPU instance types. You may need to request a quota increase through the AWS Service Quotas console before deploying GPU workloads. This process can take 24-48 hours for approval. + +## Architecture +This example showcases GPU-accelerated workloads on EKS Auto Mode using the following components: + +### 🖥️ Instance Types +- **Default**: G5, G6 or G6e instances (optimized for ML workloads) +- **Customization**: Available in [gpu-nodepool.yaml.tpl](../../nodepool-templates/gpu-nodepool.yaml.tpl) + +### 🔧 Key Components +📦 **Infrastructure** +- NodePool and NodeClass for GPU workload management +- Application Load Balancer (Ingress) for HTTP access — internal-scheme by default, opt-in `internet-facing` + HTTPS via `var.base_domain` + +🧠 **AI Components** +- Hugging Face model deployment ([Qwen 3 32b fp8](https://huggingface.co/Qwen/Qwen3-32B-FP8)) +- Interactive Web UI for model interaction + +## Implementation Steps + +### 1. Get Hugging Face Access Token +Create a Hugging Face account and generate a FINEGRAINED [Access Token](https://huggingface.co/settings/tokens) + +### 2. Setup EKS Auto Mode Cluster +Deploy the cluster using Terraform: +```bash +cd sample-aws-eks-auto-mode/terraform +terraform init +terraform apply -auto-approve +$(terraform output -raw configure_kubectl) +``` + +### 3. Deploy GPU NodePool +Deploy the NodePool that will manage our GPU instances: + +```bash +cd ../nodepools +kubectl apply -f gpu-nodepool.yaml +``` + +> ⚠️ The GPU NodePool applies the following taint to ensure only GPU-compatible workloads are scheduled on these nodes: +> +> ```yaml +> taints: +> - key: "nvidia.com/gpu" +> value: "true" +> effect: "NoSchedule" # Prevents non-GPU pods from scheduling +> ``` +> +> Any pods that need to run on GPU nodes must include matching tolerations in their specifications. + +### 4. Configure Namespace and Secrets + +1. **Create Namespace**: +```bash +cd ../examples/gpu +kubectl apply -f namespace.yaml +``` + +2. **Add Hugging Face Token**: +```bash +# Replace with your token +kubectl create secret generic hf-secret \ + -n vllm-inference \ + --from-literal=hf_api_token= +``` + +### 5. Deploy Model and UI + +1. **Deploy the Model**: +Following command will deploy Qwen3 32b (fp8). We also have another manifest file that allows you to deploy [Deepseek](vllm-deepseek-gpu.yaml) instead. + +```bash +kubectl apply -f model-qwen3-32b-fp8.yaml +``` + +> ✅ The model deployment includes the required toleration to run on GPU nodes: +> +> ```yaml +> tolerations: +> - key: "nvidia.com/gpu" # Matches the GPU node taint +> value: "true" +> effect: "NoSchedule" # Allows scheduling on tainted nodes +> ``` +> +> This toleration enables the pods to be scheduled on our GPU-enabled instances. + +2. **Deploy the Web UI**: +```bash +kubectl apply -f open-webui.yaml +``` + +### 6. Deploy the Service and Ingress +Apply the ClusterIP Service + ALB Ingress that front the Web UI: + +```bash +kubectl apply -f lb-service.yaml +``` + +> 📘 The manifest provisions a `ClusterIP` Service `open-webui-service` (port 80 → 8080) and an Ingress `open-webui-ingress` using the cluster-wide `alb` IngressClass. By default the ALB scheme is `internal` (VPC-only) — no public endpoint is created. + +### 7. Access the Application + +By default, this example exposes its UI via an **internal ALB** — reachable from inside the VPC only. To access it from your laptop, use `kubectl port-forward`: + +```bash +kubectl port-forward -n vllm-inference svc/open-webui-service 8080:80 +# then open http://localhost:8080 +``` + +If you want to inspect the ALB DNS name directly (e.g. from a bastion or VPN): + +```bash +kubectl get ingress open-webui-ingress \ + -o jsonpath='{.status.loadBalancer.ingress[0].hostname}' \ + -n vllm-inference +``` + +To expose the UI publicly over HTTPS, deploy the Terraform stack with `var.base_domain` set to a public Route53 zone you own (see top-level [README](../../README.md#-public-exposure-opt-in)). The example will be reachable at `https://gpu.` once external-dns publishes the record. + +> ⚠️ **Select a Model**: If you are unable to select a model, it means the model is still being downloaded and is not yet being served by our inferencing server pod. Just refresh until you see a model available. + + +## Cleanup + +🧹 Follow these steps to clean up all resources: + +### 1. Remove Kubernetes Resources +First, remove the application components and node pool: + +```bash +# Remove application components +kubectl delete -f lb-service.yaml +kubectl delete -f open-webui.yaml +kubectl delete -f model-qwen3-32b-fp8.yaml +kubectl delete -f namespace.yaml + +# Remove GPU node pool +kubectl delete -f ../../nodepools/gpu-nodepool.yaml +``` + +### 2. Remove Cluster (Optional) +If you're done with the entire cluster: + +```bash +# Navigate to Terraform directory +cd ../../terraform + +# Initialize and destroy infrastructure +terraform init +terraform destroy --auto-approve +``` + +> ⚠️ **Warning**: This will delete the entire EKS cluster and all associated resources. Make sure you want to proceed. + +> For a comprehensive teardown that also cleans up orphaned AWS resources (load balancers, volumes, ENIs, etc.), use `./scripts/cleanup.sh` from the repo root. See the [root README](../../README.md#cleanup) for details. + +## Troubleshooting + +🔧 Common issues and their solutions: + +### 🎯 Model Deployment Issues +1. **GPU Node Provisioning** + - Verify nodes are properly labeled for GPU + - Check node status with `kubectl get nodes` + - Ensure GPU drivers are initialized + +2. **Model Initialization** + - Check pod logs for startup errors: + ```bash + kubectl logs -n vllm-inference deployment/qwen3-32b-fp8 + ``` + - Verify Hugging Face token is valid + +### 🔄 Load Balancer Issues +1. **Ingress / ALB Status** + - Check Ingress provisioning: + ```bash + kubectl describe ingress open-webui-ingress -n vllm-inference + ``` + - Check ALB controller logs: + ```bash + kubectl logs -n kube-system deployment/aws-load-balancer-controller + ``` + - Verify the ALB hostname resolves and target group health is green + +### 💻 Resource Constraints +1. **GPU Capacity** + - Ensure sufficient GPU quota in your AWS account + - Monitor GPU utilization: + ```bash + kubectl describe node + ``` + - Check for pod scheduling events: + ```bash + kubectl get events -n vllm-inference + ``` + +> 💡 **Tip**: Always check pod logs and events first when troubleshooting deployment issues. diff --git a/misc/website/docs/examples/graviton.md b/misc/website/docs/examples/graviton.md new file mode 100644 index 0000000..5513e15 --- /dev/null +++ b/misc/website/docs/examples/graviton.md @@ -0,0 +1,181 @@ +--- +sidebar_position: 1 +title: "Graviton Workloads on EKS Auto Mode" +--- + +# Graviton Workloads on EKS Auto Mode + +## Table of Contents +- [Overview](#overview) +- [Architecture](#architecture) +- [Implementation Steps](#implementation-steps) +- [Cleanup](#cleanup) +- [Troubleshooting](#troubleshooting) + +## Overview +[AWS Graviton](https://aws.amazon.com/ec2/graviton/) processors deliver the best price performance for your cloud workloads running on Amazon EC2. Key benefits include: + +💰 **Cost Optimization** +- Up to 40% better price performance over comparable x86-based instances +- Pay only for the compute resources you use + +⚡ **Performance** +- Custom-built ARM-based processors by AWS +- Optimized for cloud-native applications + +🔒 **Requirements** +- Applications must be ARM64 compatible +- Proper configuration of node taints and tolerations + +## Architecture +This example demonstrates how to run Graviton workloads on EKS Auto Mode by configuring Karpenter to provision ARM64-compatible nodes. + +**Key Components**: +📄 **NodePool Template** +- Defines Graviton instance requirements +- Available [here](../../nodepool-templates/graviton-nodepool.yaml.tpl) + +🔄 **Load Balancer** +- Application Load Balancer (ALB) +- Exposes the application to external traffic + +🎮 **Sample Application** +- 2048 game (sliding tile puzzle) +- ARM64-compatible container image + +## Implementation Steps + +### 1. Setup EKS Auto Mode Cluster +First, we'll set up our EKS cluster using Terraform: + +```bash +cd sample-aws-eks-auto-mode/terraform + +terraform init +terraform apply -auto-approve + +$(terraform output -raw configure_kubectl) +``` + +### 2. Deploy Graviton NodePool +Deploy the NodePool that will manage our Graviton instances: + +```bash +cd ../nodepools + +kubectl apply -f graviton-nodepool.yaml +``` + +> ⚠️ The Graviton NodePool applies the following taint to ensure only ARM64-compatible workloads are scheduled on these nodes: +> +> ```yaml +> taints: +> - key: "arm64" +> value: "true" +> effect: "NoSchedule" # Prevents non-ARM64 pods from scheduling +> ``` +> +> Any pods that need to run on Graviton nodes must include matching tolerations in their specifications. This ensures workload compatibility with the ARM64 architecture. + +### 3. Deploy the 2048 Game +Deploy our ARM64-compatible 2048 game application: + +```bash +cd ../examples/graviton + +kubectl apply -f game-2048.yaml +``` + + +> ✅ The 2048 game deployment includes the required toleration to run on Graviton nodes: +> +> ```yaml +> tolerations: +> - key: "arm64" # Matches the Graviton node taint +> value: "true" +> effect: "NoSchedule" # Allows scheduling on tainted nodes +> ``` +> +> This toleration enables the pods to be scheduled on our ARM64 Graviton instances. + +### 4. Configure Load Balancer +Set up the Application Load Balancer using Ingress: + +```bash +kubectl apply -f 2048-ingress.yaml +``` + +### 5. Access the Application + +By default, this example exposes its UI via an **internal ALB** — reachable from inside the VPC only. To access it from your laptop, use `kubectl port-forward`: + +```bash +kubectl port-forward -n game-2048 svc/service-2048 8080:80 +# then open http://localhost:8080 +``` + +If you want to inspect the ALB DNS name directly (e.g. from a bastion or VPN): + +```bash +kubectl get ingress ingress-2048 \ + -o jsonpath='{.status.loadBalancer.ingress[0].hostname}' \ + -n game-2048 +``` + +To expose the UI publicly over HTTPS, deploy the Terraform stack with `var.base_domain` set to a public Route53 zone you own (see top-level [README](../../README.md#-public-exposure-opt-in)). The example will be reachable at `https://2048-graviton.` once external-dns publishes the record. 🎮 + +## Cleanup + +🧹 Follow these steps to clean up all resources: + +### 1. Remove Kubernetes Resources +First, remove the application and node pool: + +```bash +# Remove application components +kubectl delete -f 2048-ingress.yaml +kubectl delete -f game-2048.yaml + +# Remove Graviton node pool +kubectl delete -f ../../nodepools/graviton-nodepool.yaml +``` + +### 2. Remove Cluster (Optional) +If you're done with the entire cluster: + +```bash +# Navigate to Terraform directory +cd ../../terraform + +# Initialize and destroy infrastructure +terraform init +terraform destroy --auto-approve +``` + +> ⚠️ **Warning**: This will delete the entire EKS cluster and all associated resources. Make sure you want to proceed. + +> For a comprehensive teardown that also cleans up orphaned AWS resources (load balancers, volumes, ENIs, etc.), use `./scripts/cleanup.sh` from the repo root. See the [root README](../../README.md#cleanup) for details. + +## Troubleshooting + +🔧 Common issues and their solutions: + +### Ingress Issues +If your ALB ingress isn't working properly: + +1. **Stuck Ingress Deletion** +```bash +# Remove finalizers if ingress is stuck +kubectl -n game-2048 patch ingress ingress-2048 \ + -p '{"metadata":{"finalizers":null}}' \ + --type=merge +``` + +2. **ALB Controller Issues** +```bash +# Check ALB controller logs for errors +kubectl logs -n kube-system \ + deployment/aws-load-balancer-controller +``` + +> 💡 **Tip**: Always verify the ALB security group configuration if you're having connectivity issues. diff --git a/misc/website/docs/examples/index.md b/misc/website/docs/examples/index.md new file mode 100644 index 0000000..6d15325 --- /dev/null +++ b/misc/website/docs/examples/index.md @@ -0,0 +1,44 @@ +--- +sidebar_position: 3 +title: Examples +--- + +# Examples + +Each example is a self-contained deployable pattern with its own README explaining the "why" alongside the "how." Deploy the base cluster once, then apply individual examples to explore. + +## Compute Patterns + +| Example | Description | +|---------|-------------| +| [Graviton](./graviton) | ARM64 workloads on cost-effective Graviton instances | +| [Spot](./spot) | Fault-tolerant workloads on EC2 Spot with diverse instance families | +| [GPU](./gpu) | GPU-accelerated ML inference (Qwen 3 on NVIDIA GPUs) | +| [Neuron](./neuron) | ML inference on AWS Inferentia2 (DeepSeek-R1 served by vLLM) | + +## Cost Optimization + +| Example | Description | +|---------|-------------| +| [Cost Optimization](./cost-optimization) | OD/Spot mixed pools with weighted priorities and pause-pod overprovision | + +## Advanced Scheduling + +| Example | Description | +|---------|-------------| +| [Capacity Reservation](./capacity-reservation) | Pin workloads to On-Demand Capacity Reservations (ODCRs) | +| [Static Capacity](./static-capacity) | Fixed fleet of always-on nodes using `spec.replicas` | +| [Batch Jobs](./batch-jobs) | Protect long-running jobs from eviction with `do-not-disrupt` | +| [Disruption Budgets](./disruption-budgets) | Limit simultaneous node drains during consolidation | + +## Autoscaling + +| Example | Description | +|---------|-------------| +| [Pod Autoscaling](./pod-autoscaling) | HPA for CPU-based scaling + KEDA for event-driven scaling | + +## Observability + +| Example | Description | +|---------|-------------| +| [Observability](./observability) | CloudWatch Container Insights integration | diff --git a/misc/website/docs/examples/neuron.md b/misc/website/docs/examples/neuron.md new file mode 100644 index 0000000..aff3b79 --- /dev/null +++ b/misc/website/docs/examples/neuron.md @@ -0,0 +1,209 @@ +--- +sidebar_position: 4 +title: Neuron Workloads on EKS Auto Mode +--- + +# Neuron Workloads on EKS Auto Mode + +## Table of Contents +- [Overview](#overview) +- [Architecture](#architecture) +- [Implementation Steps](#implementation-steps) +- [Cleanup](#cleanup) +- [Troubleshooting](#troubleshooting) + +## Overview +[AWS Inferentia2](https://aws.amazon.com/machine-learning/inferentia/) accelerates machine learning inference workloads with custom-built chips. Key benefits include: + +🚀 **High Performance Inference** +- Purpose-built ML acceleration +- Optimized for transformer models +- Cost-effective alternative to GPUs + +🎯 **ML Model Support** +- Supports popular ML frameworks +- Optimized for transformer architectures +- OpenAI-compatible serving via [vLLM](https://docs.vllm.ai/) + +This example demonstrates deploying [DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) on EKS Auto Mode using vLLM on Inferentia2. The container image ships a pre-compiled Neuron artifact, so no separate compile job is needed. + +The manifest is adapted from [aws-samples/sample-genai-on-eks-starter-kit](https://github.com/aws-samples/sample-genai-on-eks-starter-kit/blob/main/components/llm-model/vllm/model-deepseek-r1-qwen3-8b-neuron.template.yaml). + +## Architecture +This example showcases Inferentia2-accelerated workloads using the following components: + +### 🖥️ Instance Types +- **Default**: inf2 instances (optimized for ML inference) +- **Customization**: Available in [neuron-nodepool.yaml.tpl](../../nodepool-templates/neuron-nodepool.yaml.tpl) + +### 🔧 Key Components +📦 **Infrastructure** +- NodePool and NodeClass for Neuron workload management +- Application Load Balancer (Ingress) for HTTP access — internal-scheme by default, opt-in `internet-facing` + HTTPS via `var.base_domain` + +🧠 **ML Components** +- vLLM serving DeepSeek-R1-Qwen3-8B on 2 Neuron cores (`tensor-parallel-size=2`) +- OpenAI-compatible chat completions endpoint +- DeepSeek-R1 reasoning parser enabled + +## Implementation Steps + +### 1. Setup EKS Auto Mode Cluster +Deploy the cluster using Terraform: +```bash +cd sample-aws-eks-auto-mode/terraform +terraform init +terraform apply -auto-approve +$(terraform output -raw configure_kubectl) +``` + +### 2. Deploy Neuron NodePool +Deploy the NodePool that will manage our Inferentia2 instances: + +```bash +cd ../nodepools +kubectl apply -f neuron-nodepool.yaml +``` + +> ⚠️ The Neuron NodePool applies the following taint to ensure only Neuron-compatible workloads are scheduled on these nodes: +> +> ```yaml +> taints: +> - key: "aws.amazon.com/neuron" +> value: "true" +> effect: "NoSchedule" # Prevents non-Neuron pods from scheduling +> ``` +> +> Any pods that need to run on Neuron nodes must include matching tolerations in their specifications. + +### 3. Deploy the vLLM Model + +```bash +cd ../examples/neuron +kubectl apply -f vllm-deployment.yaml +``` + +> ✅ The Deployment includes the toleration and `aws.amazon.com/neuroncore: 2` resource request that drive scheduling onto a Neuron node: +> +> ```yaml +> tolerations: +> - key: aws.amazon.com/neuron +> operator: Exists +> effect: NoSchedule +> resources: +> requests: +> aws.amazon.com/neuroncore: 2 +> limits: +> aws.amazon.com/neuroncore: 2 +> ``` + +Wait for the pod to schedule onto a Neuron node and report `Running`: + +```bash +kubectl -n vllm-neuron get pods -w +``` + +Once the pod is `Running`, vLLM still needs a minute or two to load the model. Tail the logs and wait for `Application startup complete`: + +```bash +kubectl -n vllm-neuron logs -f deployment/deepseek-r1-qwen3-8b-neuron +``` + +> 📘 The manifest provisions a `ClusterIP` Service `deepseek-r1-qwen3-8b-neuron` (port 80 → 8000) and an Ingress `deepseek-r1-qwen3-8b-neuron` using the cluster-wide `alb` IngressClass. By default the ALB scheme is `internal` (VPC-only) — no public endpoint is created. + +### 4. Test the Endpoint + +By default, this example exposes its API via an **internal ALB** — reachable from inside the VPC only. To call it from your laptop, port-forward the Service: + +```bash +kubectl -n vllm-neuron port-forward svc/deepseek-r1-qwen3-8b-neuron 8000:80 +``` + +Then hit the OpenAI-compatible chat completions endpoint: + +```bash +curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"deepseek-r1-qwen3-8b-neuron","messages":[{"role":"user","content":"Why is the sky blue?"}]}' +``` + +If you want to inspect the ALB DNS name directly (e.g. from a bastion or VPN): + +```bash +kubectl get ingress deepseek-r1-qwen3-8b-neuron -n vllm-neuron \ + -o jsonpath='{.status.loadBalancer.ingress[0].hostname}' +``` + +To expose the API publicly over HTTPS, deploy the Terraform stack with `var.base_domain` set to a public Route53 zone you own (see top-level [README](../../README.md#-public-exposure-opt-in)). The example will be reachable at `https://neuron.` once external-dns publishes the record. + +## Cleanup + +🧹 Follow these steps to clean up all resources: + +### 1. Remove Kubernetes Resources +First, remove the application components and node pool: + +```bash +# Remove application components +kubectl delete -f vllm-deployment.yaml + +# Remove Neuron node pool +kubectl delete -f ../../nodepools/neuron-nodepool.yaml +``` + +### 2. Remove Cluster (Optional) +If you're done with the entire cluster: + +```bash +cd ../../terraform +terraform destroy --auto-approve +``` + +> For a comprehensive teardown that also cleans up orphaned AWS resources (load balancers, volumes, ENIs, etc.), use `./scripts/cleanup.sh` from the repo root. See the [root README](../../README.md#cleanup) for details. + +## Troubleshooting + +🔧 Common issues and their solutions: + +### 🎯 Pod Scheduling Issues +1. **Pod stuck in `Pending`** + - Check scheduling events: + ```bash + kubectl -n vllm-neuron describe pod -l app=deepseek-r1-qwen3-8b-neuron + ``` + - Confirm a Neuron node was provisioned by the NodePool: + ```bash + kubectl get nodes -l karpenter.sh/nodepool=neuron + ``` + +2. **Insufficient Neuron capacity** + - Ensure sufficient inf2 quota in your AWS account + - Check pod scheduling events: + ```bash + kubectl -n vllm-neuron get events --sort-by=.lastTimestamp + ``` + +### 🧠 Model Serving Issues +1. **Pod logs** + - Tail vLLM startup and request logs: + ```bash + kubectl -n vllm-neuron logs -f deployment/deepseek-r1-qwen3-8b-neuron + ``` + +2. **Neuron runtime status** + - Inspect Neuron devices on the pod: + ```bash + kubectl -n vllm-neuron exec -it deployment/deepseek-r1-qwen3-8b-neuron -- neuron-ls + ``` + +### 🔄 Load Balancer Issues +1. **Ingress / ALB Status** + - Check Ingress provisioning: + ```bash + kubectl -n vllm-neuron describe ingress deepseek-r1-qwen3-8b-neuron + ``` + - Check ALB controller logs: + ```bash + kubectl -n kube-system logs deployment/aws-load-balancer-controller + ``` + - Verify the ALB hostname resolves and target group health is green + +> 💡 **Tip**: Always check pod logs and events first when troubleshooting deployment issues. diff --git a/misc/website/docs/examples/observability.md b/misc/website/docs/examples/observability.md new file mode 100644 index 0000000..286c244 --- /dev/null +++ b/misc/website/docs/examples/observability.md @@ -0,0 +1,173 @@ +--- +sidebar_position: 12 +title: Observability with CloudWatch Container Insights +--- + +# Observability with CloudWatch Container Insights + +This guide explains how EKS Auto Mode integrates with Amazon CloudWatch Container Insights to provide full-stack observability for your cluster without managing any monitoring infrastructure yourself. + +## What Container Insights Provides + +CloudWatch Container Insights delivers observability across three pillars: + +**Metrics** — CPU, memory, network, and disk utilization at every level of the hierarchy: cluster, node, pod, and individual container. These metrics are collected at 1-minute intervals and stored in the `ContainerInsights` CloudWatch metrics namespace. + +**Logs** — Pod stdout/stderr logs are shipped to CloudWatch Logs automatically. This gives you a centralized, searchable log store without deploying a separate logging stack. + +**Application Signals (Distributed Tracing)** — Auto-instrumentation for Java, Python, Node.js, and .NET applications. When enabled, traces flow to CloudWatch Application Signals, giving you service maps, latency percentiles, and error rates across microservices. + +## How It Works in Auto Mode + +EKS Auto Mode manages the node lifecycle (provisioning, scaling, patching, termination). The `amazon-cloudwatch-observability` EKS addon handles the observability plane: + +1. The addon deploys a **CloudWatch agent DaemonSet** on every node. This agent collects container-level metrics and forwards them to CloudWatch Metrics. +2. A **Fluent Bit sidecar** captures pod logs and streams them to CloudWatch Logs. +3. An **OpenTelemetry collector** (optional, via Application Signals) collects traces and sends them to X-Ray/Application Signals. + +Because Auto Mode manages nodes and the addon manages observability, you get a fully hands-off monitoring stack. No Helm charts to maintain, no Prometheus to scale, no Fluentd config files to debug. + +## What You Get Out of the Box + +Once enabled, the following are created automatically: + +### CloudWatch Log Groups + +| Log Group | Contents | +|-----------|----------| +| `/aws/containerinsights//application` | Pod stdout/stderr logs | +| `/aws/containerinsights//performance` | Cadvisor and kubelet metrics in structured JSON | +| `/aws/containerinsights//dataplane` | Kubelet, kube-proxy, and container runtime logs | +| `/aws/containerinsights//host` | Node-level system logs | + +### CloudWatch Metrics Namespace + +All metrics land in `ContainerInsights` with dimensions for ClusterName, Namespace, PodName, and ContainerName. Key metrics include: + +- `pod_cpu_utilization`, `pod_memory_utilization` +- `node_cpu_utilization`, `node_memory_utilization` +- `pod_network_rx_bytes`, `pod_network_tx_bytes` +- `cluster_node_count`, `cluster_failed_node_count` + +### Container Insights Dashboard + +The CloudWatch console provides a pre-built Container Insights dashboard at: + +``` +CloudWatch > Container Insights > Performance monitoring +``` + +This shows top resource consumers, pod restart trends, and node capacity in a single pane. + +## Cost Awareness + +CloudWatch Container Insights is metered. Key cost drivers: + +| Dimension | Cost Driver | Mitigation | +|-----------|-------------|------------| +| **Metrics** | Custom metrics at $0.30/metric/month (first 10k) | Metrics are per pod/container; large clusters generate many | +| **Logs** | Ingestion at $0.50/GB + storage at $0.03/GB/month | Set log retention policies (default is never-expire) | +| **Traces** | $1.00 per million traces sampled | Use sampling rules to reduce volume | + +**Recommendations by environment:** + +- **Dev/Staging** — Enable with defaults. Cost is minimal for small clusters. Good learning environment. +- **Production** — Set CloudWatch Logs retention to 30 days. Use metric filters to drop noisy metrics. Configure trace sampling at 5-10% for high-throughput services. +- **Cost-sensitive** — Consider enabling metrics only (disable logs/traces) or use the Enhanced Observability tier selectively. + +Set log retention via AWS CLI after deployment: + +```bash +aws logs put-retention-policy --log-group-name /aws/containerinsights//application --retention-in-days 30 +``` + +## Application Signals (Distributed Tracing) + +Application Signals provides auto-instrumentation without code changes. To enable tracing for a workload, add an annotation to your pod spec: + +**Java:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-java: "true" +``` + +**Python:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-python: "true" +``` + +**Node.js:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-nodejs: "true" +``` + +**.NET:** +```yaml +metadata: + annotations: + instrumentation.opentelemetry.io/inject-dotnet: "true" +``` + +Once annotated, pods are automatically instrumented on next restart. Traces appear in: + +``` +CloudWatch > Application Signals > Services +``` + +You get service maps, latency histograms, error rates, and dependency graphs with zero code changes. + +--- + +## Enable + +```bash +terraform apply -var='enable_observability=true' +``` + +This creates the `amazon-cloudwatch-observability` EKS addon and attaches the `CloudWatchAgentServerPolicy` to the node IAM role. + +## Verify + +Check that the CloudWatch agent pods are running: + +```bash +kubectl get pods -n amazon-cloudwatch +``` + +Expected output shows `amazon-cloudwatch-observability-controller-manager` and `cloudwatch-agent` DaemonSet pods (one per node). + +Confirm metrics are flowing: + +```bash +aws cloudwatch list-metrics --namespace ContainerInsights --dimensions Name=ClusterName,Value= --region +``` + +## Explore + +Once deployed, explore these CloudWatch console paths: + +- **Container Insights dashboard:** `CloudWatch > Container Insights > Performance monitoring` +- **Pod logs:** `CloudWatch > Logs > Log groups > /aws/containerinsights//application` +- **Application Signals:** `CloudWatch > Application Signals > Services` +- **Metrics explorer:** `CloudWatch > Metrics > ContainerInsights` + +Direct console URL (also available as a Terraform output): + +``` +https://.console.aws.amazon.com/cloudwatch/home?region=#container-insights:infrastructure +``` + +## Disable + +To remove the observability addon and stop incurring costs: + +```bash +terraform apply -var='enable_observability=false' +``` + +This removes the addon and detaches the IAM policy. Existing logs and metrics in CloudWatch are retained according to their retention settings. diff --git a/misc/website/docs/examples/pod-autoscaling.md b/misc/website/docs/examples/pod-autoscaling.md new file mode 100644 index 0000000..4fae62b --- /dev/null +++ b/misc/website/docs/examples/pod-autoscaling.md @@ -0,0 +1,473 @@ +--- +sidebar_position: 11 +title: Pod Autoscaling on EKS Auto Mode +--- + +# Pod Autoscaling on EKS Auto Mode + +## Table of Contents +- [Overview](#overview) +- [Part 1: Horizontal Pod Autoscaler (HPA)](#part-1-horizontal-pod-autoscaler-hpa) + - [HPA Architecture](#hpa-architecture) + - [HPA Implementation Steps](#hpa-implementation-steps) + - [HPA Cleanup](#hpa-cleanup) +- [Part 2: KEDA Event-Driven Autoscaling](#part-2-keda-event-driven-autoscaling) +- [Troubleshooting](#troubleshooting) + +## Overview +Pod autoscaling is essential for maintaining optimal performance and cost efficiency in Kubernetes clusters. This example demonstrates two complementary approaches: + +📊 **Horizontal Pod Autoscaler (HPA)** +- CPU and memory-based scaling +- Built-in Kubernetes functionality +- Ideal for traditional web applications + +🎯 **KEDA (Kubernetes Event-Driven Autoscaling)** +- Event-driven scaling based on external metrics +- Supports 60+ scalers (SQS, Kafka, Redis, etc.) +- Perfect for event-driven and batch workloads + +⚡ **Key Benefits** +- Automatic scaling based on demand +- Cost optimization through right-sizing +- Improved application performance and availability + +## Part 1: Horizontal Pod Autoscaler (HPA) + +### HPA Architecture +The HPA automatically scales the number of pods in a deployment based on observed CPU utilization, memory usage, or custom metrics. + +**How it works**: +1. 📈 **Metrics Collection**: Metrics Server collects resource usage from pods +2. 🔍 **Evaluation**: HPA controller evaluates metrics against target thresholds +3. ⚖️ **Scaling Decision**: Calculates desired replica count based on current vs target metrics +4. 🔄 **Pod Adjustment**: Updates deployment replica count to match demand + +**Key Components**: +- **Metrics Server**: Collects resource metrics from kubelets +- **HPA Controller**: Makes scaling decisions based on metrics +- **Target Deployment**: The workload being scaled +- **Load Generator**: Simulates traffic to trigger scaling + +### HPA Implementation Steps + +#### 1. Setup EKS Auto Mode Cluster +First, deploy your EKS cluster using Terraform: + +```bash +cd sample-aws-eks-auto-mode/terraform + +terraform init +terraform apply -auto-approve + +$(terraform output -raw configure_kubectl) +``` + +#### 2. Install Metrics Server +The HPA requires the Metrics Server to collect resource metrics from pods: + +```bash +kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml +``` + +> 📘 **Note**: The Metrics Server collects resource metrics from kubelets and exposes them through the Kubernetes API. It may take a minute or two to become ready. + +Verify the Metrics Server is running: +```bash +kubectl get pods -n kube-system | grep metrics-server +``` + +#### 3. Deploy the Sample Application +Deploy a PHP Apache server that will serve as our scaling target: + +```bash +cd ../examples/pod-autoscaling/hpa + +kubectl apply -f php-apache.yaml +``` + +> ✅ **Application Details**: The php-apache deployment includes: +> - **CPU Request**: 200m (200 millicores) +> - **CPU Limit**: 500m (500 millicores) +> - **Container**: `registry.k8s.io/hpa-example` - a simple PHP server +> - **Service**: Exposes the application on port 80 + +#### 4. Create the HorizontalPodAutoscaler +Create an HPA that maintains between 1 and 10 replicas based on CPU utilization: + +```bash +kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10 +``` + +Alternatively, you can use the declarative approach: + +```bash +kubectl apply -f hpa.yaml +``` + +#### 5. Verify HPA Status +Check the current status of the HPA: + +```bash +kubectl get hpa +``` + +Expected output: +``` +NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE +php-apache Deployment/php-apache/scale 0% / 50% 1 10 1 18s +``` + +> 📘 **Note**: The current CPU consumption shows 0% because there's no load on the server yet. + +#### 6. Generate Load to Trigger Scaling +Start a load generator to increase CPU utilization: + +```bash +# Run this in a separate terminal +kubectl run -i --tty load-generator --rm --image=busybox:1.28 --restart=Never -- /bin/sh -c "while sleep 0.01; do wget -q -O- http://php-apache; done" +``` + +#### 7. Watch the Scaling in Action +In another terminal, monitor the HPA scaling behavior: + +```bash +# Press Ctrl+C to stop watching when ready +kubectl get hpa php-apache --watch +``` + +Within a minute, you should see increased CPU load: +``` +NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE +php-apache Deployment/php-apache/scale 305% / 50% 1 10 1 3m +``` + +Then observe the replica count increase (will stabilize between 6-8): +``` +NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE +php-apache Deployment/php-apache/scale 305% / 50% 1 10 8 3m +``` + +Verify the deployment scaling: +```bash +kubectl get deployment php-apache +``` + +Expected output: +``` +NAME READY UP-TO-DATE AVAILABLE AGE +php-apache 8/8 8 8 19m +``` + +#### 8. Stop Load Generation and Observe Scale-Down +Stop the load generator by pressing `Ctrl+C` in the load generator terminal. + +Monitor the scale-down process: +```bash +kubectl get hpa php-apache --watch +``` + +After a few minutes, you'll see the CPU utilization drop and replicas scale back down: +``` +NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE +php-apache Deployment/php-apache/scale 0% / 50% 1 10 1 11m +``` + +> ⏱️ **Scaling Timing**: +> - **Scale-up**: Typically occurs within 1-2 minutes of increased load +> - **Scale-down**: Takes 5-10 minutes to ensure stability before reducing replicas + +### HPA Cleanup + +Remove the HPA resources: + +```bash +kubectl delete pod load-generator + +# Remove the HPA +kubectl delete hpa php-apache + +# Remove the application +kubectl delete -f php-apache.yaml +``` + +> 📚 **Attribution**: This HPA demo was adapted from the [Kubernetes HorizontalPodAutoscaler Walkthrough](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/). + +## Part 2: KEDA Event-Driven Autoscaling + +### KEDA Architecture +[KEDA (Kubernetes Event-Driven Autoscaling)](https://keda.sh/) extends Kubernetes with event-driven autoscaling capabilities beyond traditional CPU/memory metrics. Key benefits include: + +🎯 **Event-Driven Scaling** +- Scale based on external metrics (SQS queue depth, Kafka lag, etc.) +- Support for 60+ scalers including AWS services +- Zero-to-N and N-to-zero scaling capabilities + +🚀 **Advanced Capabilities** +- Custom metrics from external systems +- Integration with Horizontal Pod Autoscaler +- Seamless integration with Karpenter for node-level scaling + +⚡ **Perfect for Modern Workloads** +- Batch processing jobs +- Event-driven microservices +- AI/ML inference workloads +- Queue-based processing systems + +This example demonstrates KEDA scaling a GPU-based AI model inference workload based on Amazon SQS queue depth. + +**How it works**: +1. 📨 **Message Queue**: SQS receives inference requests +2. 📊 **KEDA Monitoring**: ScaledObject monitors queue depth +3. 🔄 **Scaling Decision**: KEDA scales pods based on queue metrics +4. 🤖 **GPU Processing**: Scaled pods process inference requests +5. 📉 **Scale Down**: Pods scale to zero when queue is empty + +**Key Components**: +- **KEDA Controller**: Manages event-driven scaling +- **ScaledObject**: Defines scaling behavior and triggers +- **SQS Queue**: Message queue for inference requests +- **GPU Inference Pods**: AI model serving containers +- **Karpenter Integration**: Automatic node provisioning for GPU workloads + +### KEDA Implementation Steps + +> ⚠️ **Prerequisites**: +> - **GPU Instance Availability**: Ensure you have sufficient GPU quota for your AWS account +> - **Helm**: Required for KEDA installation + +#### 1. Setup AWS Infrastructure +Deploy the required AWS resources (SQS queue, IAM roles) using Terraform: + +```bash +# Navigate to KEDA terraform directory (assuming you're in HPA folder) +cd ../keda/terraform + +# Initialize and deploy AWS resources +terraform init +terraform apply -auto-approve +``` + +> 📦 **AWS Resources Created**: +> - **SQS Queue**: For inference request messages +> - **IAM Roles**: Service accounts for KEDA and SQS access +> - **IAM Policies**: Permissions for queue operations + +#### 2. Configure Kubernetes Resources +Set up the necessary namespaces and service accounts: + +```bash +# Navigate back to KEDA directory +cd .. + +# Create namespaces and service accounts +kubectl apply -f namespace.yaml +kubectl apply -f keda-service-account.yaml +kubectl apply -f vllm-qwen3/namespace.yaml +kubectl apply -f sqs-reader-service-account.yaml +``` + +> ✅ **Service Account Details**: +> - **KEDA Service Account**: Allows KEDA to read SQS metrics +> - **SQS Reader Service Account**: Enables pods to consume SQS messages +> - **IAM Role Annotations**: Links Kubernetes service accounts to AWS IAM roles + +#### 3. Install KEDA with Helm +Deploy KEDA controller with custom configuration: + +```bash +# Add KEDA Helm repository +helm repo add kedacore https://kedacore.github.io/charts +helm repo update + +# Install KEDA with custom values +helm install keda kedacore/keda \ + --namespace keda \ + --version 2.17.0 \ + --values keda-helm-values.yaml +``` + +Verify KEDA installation: +```bash +kubectl get pods -n keda +``` + +Expected output: +``` +NAME READY STATUS RESTARTS AGE +keda-admission-webhooks-xxx 1/1 Running 0 2m +keda-operator-xxx 1/1 Running 0 2m +keda-operator-metrics-apiserver-xxx 1/1 Running 0 2m +``` + +> 📘 **KEDA Components**: +> - **Operator**: Main KEDA controller managing ScaledObjects +> - **Metrics API Server**: Exposes custom metrics to HPA +> - **Admission Webhooks**: Validates KEDA resource configurations + +#### 4. Deploy GPU NodePool +Ensure GPU nodes are available for the AI workload: + +```bash +# Deploy GPU-enabled NodePool +kubectl apply -f ../../../nodepools/gpu-nodepool.yaml +``` + +> ⚠️ **GPU Node Configuration**: The NodePool includes: +> - **Instance Types**: G5, G6, or G6e instances optimized for ML workloads +> - **Taints**: `nvidia.com/gpu=true:NoSchedule` to ensure only GPU workloads are scheduled +> - **Labels**: Proper GPU node identification for workload placement + +#### 5. Deploy AI Model with SQS Consumer +Deploy the GPU-based inference workload that will be scaled by KEDA: + +```bash +kubectl apply -f vllm-qwen3/model-qwen3-4b-fp8-with-sqs.yaml +``` + +> 🤖 **Model Details**: +> - **Model**: Qwen3-4B-FP8 optimized for GPU inference +> - **SQS Integration**: Built-in consumer for processing queue messages +> - **GPU Tolerations**: Configured to run on GPU-tainted nodes +> - **Resource Requests**: Optimized for efficient GPU utilization + +Verify the deployment: +```bash +kubectl get pods -n vllm +kubectl get deployments -n vllm +``` + +Initially, you should see 0 replicas since there are no messages in the queue. + +#### 6. Deploy KEDA ScaledObject +Create the ScaledObject that defines the scaling behavior: + +```bash +kubectl apply -f scaledObject.yaml +``` + +> 📊 **Scaling Configuration**: +> - **Trigger**: SQS queue depth +> - **Target**: 5 messages per pod +> - **Min Replicas**: 0 (scale to zero when idle) +> - **Max Replicas**: 10 (adjust based on your needs) +> - **Cooldown**: Prevents rapid scaling oscillations + +Verify the ScaledObject: +```bash +kubectl get scaledobject -n vllm +``` + +Expected output: +``` +NAME SCALETARGETKIND SCALETARGETNAME MIN MAX READY ACTIVE FALLBACK PAUSED TRIGGERS AUTHENTICATIONS AGE +model-qwen3-4b-fp8-scaler apps/v1.Deployment model-qwen3-4b-fp8 0 10 True False Unknown Unknown aws-sqs-queue 6s +``` + +#### 7. Test the Scaling Behavior +Generate test messages to trigger scaling: + +```bash +# Deploy job that generates 50 inference requests +kubectl apply -f prompt-generator-job.yaml +``` + +> 🧪 **Test Scenario**: The prompt generator creates 50 sample inference requests in the SQS queue, simulating real-world load. + +#### 8. Monitor the Scaling Process +Watch the scaling behavior in real-time: + +1. **Check SQS Queue Depth**: +Wait for job to finish sending messages first. + +```bash +cd terraform +QUEUE_URL=$(terraform output -raw sqs_url) +aws sqs get-queue-attributes \ + --queue-url $QUEUE_URL \ + --attribute-names ApproximateNumberOfMessages +``` + +2. **Monitor ScaledObject Status**: +```bash +kubectl describe scaledobject model-qwen3-4b-fp8-scaler -n vllm +``` + +3. **Watch Deployment Scaling**: +```bash +kubectl get deployment model-qwen3-4b-fp8 -n vllm --watch +``` + +4. **Monitor Pod Creation**: +```bash +kubectl get pods -n vllm --watch +``` + +> ⏱️ **Expected Timeline**: +> - **0-1 min**: Messages appear in SQS queue (50 messages) +> - **1-2 min**: KEDA detects queue depth and triggers scaling +> - **2-5 min**: GPU nodes provision and pods start (model download begins) +> - **5-6 min**: Pods become ready and start consuming messages +> - **6-7 min**: All messages processed, queue becomes empty +> - **7-8 min**: Pods scale down to zero after cooldown period + +#### 9. Observe the Processing +Monitor the actual inference processing: + +1. **Check Pod Logs** (model initialization): +```bash +kubectl logs -n vllm deployment/model-qwen3-4b-fp8 -f +``` + +2. **Monitor Message Processing**: +```bash +# Watch queue depth decrease as messages are processed +watch "aws sqs get-queue-attributes --queue-url $QUEUE_URL --attribute-names ApproximateNumberOfMessages" +``` + +> 🎯 **Success Indicators**: +> - Queue depth increases to 50, then decreases to 0 +> - Deployment scales from 0 to multiple replicas, then back to 0 +> - Pod logs show model loading and inference processing +> - Processing completes within expected timeframe + +### KEDA Cleanup + +🧹 Follow these steps to clean up all KEDA resources: + +#### 1. Remove Application Resources +```bash +# Remove test job and scaling resources +kubectl delete job prompt-generator -n keda --ignore-not-found +kubectl delete -f scaledObject.yaml --ignore-not-found +kubectl delete -f vllm-qwen3/model-qwen3-4b-fp8-with-sqs.yaml --ignore-not-found + +# Remove GPU NodePool created in step 4 +kubectl delete -f ../../../nodepools/gpu-nodepool.yaml --ignore-not-found +``` + +#### 2. Uninstall KEDA +```bash +# Remove KEDA Helm installation +helm uninstall keda -n keda +``` + +#### 3. Remove Kubernetes Resources +```bash +# Clean up service accounts and namespaces +kubectl delete -f sqs-reader-service-account.yaml --ignore-not-found +kubectl delete -f keda-service-account.yaml --ignore-not-found +kubectl delete namespace keda --ignore-not-found +kubectl delete namespace vllm --ignore-not-found +``` + +#### 4. Destroy AWS Infrastructure +```bash +# Remove AWS resources (SQS, IAM roles) +cd terraform +terraform destroy -auto-approve +``` + +> ⚠️ **Warning**: This will remove all AWS resources created for the KEDA demo, including the SQS queue and IAM roles. + +> For a comprehensive teardown that also cleans up orphaned AWS resources (load balancers, volumes, ENIs, etc.), use `./scripts/cleanup.sh` from the repo root. See the [root README](../../README.md#cleanup) for details. diff --git a/misc/website/docs/examples/spot.md b/misc/website/docs/examples/spot.md new file mode 100644 index 0000000..6c2bc21 --- /dev/null +++ b/misc/website/docs/examples/spot.md @@ -0,0 +1,206 @@ +--- +sidebar_position: 2 +title: Spot Workloads on EKS Auto Mode +--- + +# Spot Workloads on EKS Auto Mode + +## Table of Contents +- [Overview](#overview) +- [Architecture](#architecture) +- [Implementation Steps](#implementation-steps) +- [Cleanup](#cleanup) +- [Troubleshooting](#troubleshooting) + +## Overview +[Amazon EC2 Spot Instances](https://aws.amazon.com/ec2/spot/) let you take advantage of unused EC2 capacity at steep discounts. Key benefits include: + +💰 **Cost Optimization** +- Up to 90% cost savings compared to On-Demand instances +- Ideal for fault-tolerant, flexible workloads +- Pay only for what you use + +⚡ **Scalability** +- Access to large-scale compute capacity +- Perfect for batch processing and stateless applications +- Automatic capacity rebalancing + +🔄 **Flexibility** +- Mix of instance types and sizes +- Automatic instance selection based on availability +- Graceful interruption handling + +## Architecture +This example demonstrates how to run workloads on Spot instances in EKS Auto Mode using Karpenter's spot instance management capabilities. + +**Key Components**: +📄 **NodePool Template** +- Defines Spot instance requirements +- Available [here](../../nodepool-templates/spot-nodepool.yaml.tpl) +- Supports c, m, and r instance families +- ARM64 architecture for cost efficiency + +🔄 **Load Balancer** +- Application Load Balancer (ALB) +- Exposes the application to external traffic + +🎮 **Sample Application** +- 2048 game (sliding tile puzzle) +- Stateless application ideal for spot instances + +## Implementation Steps + +### 1. Setup EKS Auto Mode Cluster +First, we'll set up our EKS cluster using Terraform: + +```bash +cd sample-aws-eks-auto-mode/terraform + +terraform init +terraform apply -auto-approve + +$(terraform output -raw configure_kubectl) +``` + +### 2. Deploy Spot NodePool +Deploy the NodePool that will manage our Spot instances: + +```bash +cd ../nodepools + +kubectl apply -f spot-nodepool.yaml +``` + +> ⚠️ The Spot NodePool applies the following taint to ensure workloads are spot-aware: +> +> ```yaml +> taints: +> - key: "spot" +> value: "true" +> effect: "NoSchedule" # Prevents non-spot-aware pods from scheduling +> ``` +> +> Any pods that need to run on Spot nodes must include matching tolerations in their specifications. This ensures workloads are designed to handle spot instance interruptions. + +### 3. Deploy the 2048 Game +Deploy our spot-compatible 2048 game application: + +```bash +cd ../examples/spot + +kubectl apply -f game-2048.yaml +``` + +> ✅ The 2048 game deployment includes the required configuration for Spot instances: +> +> ```yaml +> tolerations: +> - key: "spot" # Matches the Spot node taint +> value: "true" +> effect: "NoSchedule" # Allows scheduling on tainted nodes +> +> nodeSelector: +> karpenter.sh/capacity-type: spot # Ensures pods run on spot instances +> ``` +> +> This configuration ensures the pods can run on Spot instances and are scheduled appropriately. + +### 4. Configure Load Balancer +Set up the Application Load Balancer using Ingress: + +```bash +kubectl apply -f 2048-ingress.yaml +``` + +### 5. Access the Application + +By default, this example exposes its UI via an **internal ALB** — reachable from inside the VPC only. To access it from your laptop, use `kubectl port-forward`: + +```bash +kubectl port-forward -n game-2048-spot svc/service-2048 8080:80 +# then open http://localhost:8080 +``` + +If you want to inspect the ALB DNS name directly (e.g. from a bastion or VPN): + +```bash +kubectl get ingress ingress-2048 \ + -o jsonpath='{.status.loadBalancer.ingress[0].hostname}' \ + -n game-2048-spot +``` + +To expose the UI publicly over HTTPS, deploy the Terraform stack with `var.base_domain` set to a public Route53 zone you own (see top-level [README](../../README.md#-public-exposure-opt-in)). The example will be reachable at `https://2048-spot.` once external-dns publishes the record. 🎮 + +## Cleanup + +🧹 Follow these steps to clean up all resources: + +### 1. Remove Kubernetes Resources +First, remove the application and node pool: + +```bash +# Remove application components +kubectl delete -f 2048-ingress.yaml +kubectl delete -f game-2048.yaml + +# Remove Spot node pool +kubectl delete -f ../../nodepools/spot-nodepool.yaml +``` + +### 2. Remove Cluster (Optional) +If you're done with the entire cluster: + +```bash +# Navigate to Terraform directory +cd ../../terraform + +# Initialize and destroy infrastructure +terraform init +terraform destroy --auto-approve +``` + +> ⚠️ **Warning**: This will delete the entire EKS cluster and all associated resources. Make sure you want to proceed. + +> For a comprehensive teardown that also cleans up orphaned AWS resources (load balancers, volumes, ENIs, etc.), use `./scripts/cleanup.sh` from the repo root. See the [root README](../../README.md#cleanup) for details. + +## Troubleshooting + +🔧 Common issues and their solutions: + +### 🎯 Spot Instance Issues +1. **Capacity Unavailability** + - Monitor instance capacity with AWS CLI: + ```bash + aws ec2 describe-spot-instance-requests \ + --filters "Name=status-code,Values=capacity-not-available" + ``` + - Check NodePool events: + ```bash + kubectl describe nodepool spot-nodepool + ``` + +2. **Instance Interruptions** + - Monitor interruption events: + ```bash + kubectl get events --field-selector reason=SpotInterruption + ``` + - Review pod eviction status: + ```bash + kubectl get pods -n game-2048 -o wide + ``` + +### 🔄 Load Balancer Issues +1. **ALB Configuration** + ```bash + # Check ALB controller logs + kubectl logs -n kube-system \ + deployment/aws-load-balancer-controller + ``` + +2. **Ingress Status** + ```bash + # Check ingress status + kubectl describe ingress ingress-2048 -n game-2048 + ``` + +> 💡 **Tip**: Use `kubectl get events` to monitor spot instance lifecycle events and pod rescheduling. diff --git a/misc/website/docs/examples/static-capacity.md b/misc/website/docs/examples/static-capacity.md new file mode 100644 index 0000000..d655d24 --- /dev/null +++ b/misc/website/docs/examples/static-capacity.md @@ -0,0 +1,85 @@ +--- +sidebar_position: 8 +title: Static Capacity Pools +--- + +# Static Capacity Pools + +## What are static capacity pools? + +Setting `spec.replicas` on a NodePool tells EKS Auto Mode to maintain exactly N nodes at all times, regardless of pod demand. These nodes are never consolidated away when empty. They exist whether workloads are scheduled on them or not. + +```yaml +spec: + replicas: 2 # Always maintain exactly 2 nodes +``` + +## Why they exist + +Some workloads need guaranteed capacity available at all times: + +- **Always-on inference endpoints** that must respond in milliseconds, not minutes +- **Database replicas** where cold-start means replay from WAL/snapshot +- **License-bound software** where the license is tied to a running host +- **Latency-sensitive services** where waiting for a node to provision (30-90s) is unacceptable + +Dynamic scaling introduces cold-start latency. For workloads where that latency violates SLOs, static pools eliminate it entirely. + +## How it differs from dynamic pools + +| Behavior | Dynamic Pool | Static Pool | +|----------|-------------|-------------| +| Scale-from-zero | Yes | No -- always N nodes | +| Consolidation | Removes underutilized nodes | Never removes nodes | +| Scaling trigger | Pending pods | Manual (`kubectl scale nodepool`) | +| Cost model | Pay only for what you use | Pay for N nodes 24/7 | +| Cold-start risk | Yes (30-90s node provision) | None | + +You can resize a static pool at any time: + +```bash +kubectl scale nodepool static-gpu-nodepool --replicas=4 +``` + +## The trade-off + +You pay for N nodes 24/7 whether they are fully loaded or completely idle. Use static pools only where: + +> **Cost of idle nodes < Cost of cold-start latency** + +If your workload can tolerate 60-90 seconds of scale-up time, a dynamic pool is cheaper. + +## When to use + +- Always-on model serving (vLLM, TGI, Triton) +- Stateful databases (PostgreSQL, Redis cluster nodes) +- License servers (FlexLM, RLM) +- Baseline capacity layer -- handle steady-state traffic with static nodes, burst traffic with a separate dynamic pool + +## Deploy + +```bash +# Render the template (if using Terraform templatefile) +terraform apply + +# Or apply directly after filling variables +kubectl apply -f static-nodepool.yaml +``` + +## What to observe + +```bash +# Verify exactly 2 nodes exist for this pool (even with zero pods) +kubectl get nodes -l karpenter.sh/nodepool=static-gpu-nodepool + +# Confirm replicas are set +kubectl get nodepool static-gpu-nodepool -o jsonpath='{.spec.replicas}' + +# Watch that consolidation does NOT touch these nodes +kubectl logs -n kube-system -l app.kubernetes.io/name=karpenter -f | grep static-gpu + +# Scale up +kubectl scale nodepool static-gpu-nodepool --replicas=4 +# Watch a new node provision immediately (no pending pod required) +kubectl get nodes -w -l karpenter.sh/nodepool=static-gpu-nodepool +``` diff --git a/misc/website/docs/getting-started.md b/misc/website/docs/getting-started.md new file mode 100644 index 0000000..41fb612 --- /dev/null +++ b/misc/website/docs/getting-started.md @@ -0,0 +1,70 @@ +--- +sidebar_position: 2 +title: Getting Started +--- + +# Getting Started + +## Prerequisites + +- [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html) configured with appropriate credentials +- [kubectl](https://kubernetes.io/docs/tasks/tools/) +- [Terraform](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli) >= 1.5 + +## Deploy the base cluster + +```bash +git clone https://github.com/aws-samples/sample-aws-eks-auto-mode.git +cd sample-aws-eks-auto-mode/terraform +terraform init +terraform apply -auto-approve +``` + +Configure kubectl: + +```bash +$(terraform output -raw configure_kubectl) +``` + +## Apply an example + +Each example is self-contained. Pick one and apply: + +```bash +kubectl apply -f examples/graviton/ +``` + +## Configuration + +All inputs are defined in `terraform/variables.tf`. Override with `-var` flags or a `terraform.tfvars` file. + +| Variable | Description | Default | +|----------|-------------|---------| +| `name` | VPC and EKS cluster name | `automode-cluster` | +| `region` | AWS region | `us-west-2` | +| `eks_cluster_version` | Kubernetes version | `1.34` | +| `vpc_cidr` | VPC CIDR block | `10.0.0.0/16` | +| `tags` | Tags applied to all resources (5-layer) | `{"auto-delete" = "never"}` | +| `base_domain` | Route53 zone for HTTPS (empty = internal-only) | `""` | +| `subdomain` | Prefix under base_domain | `""` | +| `ephemeral_storage_kms_key_id` | KMS key for node storage encryption | `""` | +| `enable_observability` | CloudWatch Container Insights | `false` | + +### Public exposure (opt-in) + +By default, all workloads are internal-only. To expose on a real domain: + +```bash +terraform apply \ + -var='base_domain=example.com' \ + -var='subdomain=automode' \ + -var='enable_observability=true' +``` + +## Cleanup + +```bash +./scripts/cleanup.sh +``` + +See [Cleanup Architecture](./architecture/cleanup) for the full teardown playbook. diff --git a/misc/website/docs/intro.md b/misc/website/docs/intro.md new file mode 100644 index 0000000..ba59d23 --- /dev/null +++ b/misc/website/docs/intro.md @@ -0,0 +1,39 @@ +--- +sidebar_position: 1 +title: Introduction +--- + +# EKS Auto Mode Samples + +[Amazon EKS Auto Mode](https://aws.amazon.com/eks/auto-mode/) simplifies Kubernetes cluster management by automating compute, storage, and networking decisions. Under the hood it runs Karpenter, the AWS Load Balancer Controller, and the EBS CSI driver as managed components — you get the benefits without installing or upgrading any of them. + +This repository is an educational companion. Each example demonstrates a specific EKS Auto Mode pattern (Graviton, GPU, Spot, ODCR targeting, disruption budgets, etc.) with a self-contained README explaining the "why" alongside the "how." Deploy the base cluster once, then apply individual examples to explore. + +## Key capabilities covered + +- Graviton (ARM64) and x86 workloads side by side +- GPU and Inferentia2 (Neuron) ML inference +- Spot and On-Demand mixed pools with overprovision headroom +- On-Demand Capacity Reservation targeting +- Static capacity pools and disruption budgets +- HPA and KEDA-driven autoscaling +- KMS encryption for ephemeral node storage +- CloudWatch Container Insights observability +- 5-layer resource tagging for cost allocation + +## What's in this repo + +| Directory | Purpose | +|-----------|---------| +| `terraform/` | Base cluster infrastructure (VPC, EKS, IAM, NodePools) | +| `examples/` | Self-contained deployable patterns | +| `nodepool-templates/` | Templatized NodePool/NodeClass definitions | +| `scripts/` | Cleanup and maintenance utilities | +| `claude-md/` | Architecture reference docs (tagging, cleanup) | + +## Where to go next + +- [Getting Started](./getting-started) — deploy the base cluster +- [Examples](./examples) — browse deployable patterns +- [Architecture](./architecture) — tagging, cleanup, security +- [Contributing](./contributing) — improve this repo diff --git a/misc/website/docusaurus.config.ts b/misc/website/docusaurus.config.ts new file mode 100644 index 0000000..609ce8c --- /dev/null +++ b/misc/website/docusaurus.config.ts @@ -0,0 +1,108 @@ +import {themes as prismThemes} from 'prism-react-renderer'; +import type {Config} from '@docusaurus/types'; +import type * as Preset from '@docusaurus/preset-classic'; + +const config: Config = { + title: 'EKS Auto Mode Samples', + tagline: 'Learn EKS Auto Mode through deployable Terraform examples', + favicon: 'img/favicon.svg', + + url: 'https://aws-samples.github.io', + baseUrl: '/sample-aws-eks-auto-mode/', + + organizationName: 'aws-samples', + projectName: 'sample-aws-eks-auto-mode', + + onBrokenLinks: 'warn', + + markdown: { + format: 'detect', + }, + + i18n: { + defaultLocale: 'en', + locales: ['en'], + }, + + presets: [ + [ + 'classic', + { + docs: { + sidebarPath: './sidebars.ts', + editUrl: 'https://github.com/aws-samples/sample-aws-eks-auto-mode/edit/main/misc/website/', + }, + blog: false, + theme: { + customCss: './src/css/custom.css', + }, + } satisfies Preset.Options, + ], + ], + + themeConfig: { + navbar: { + title: 'EKS Auto Mode Samples', + items: [ + { + type: 'doc', + docId: 'intro', + position: 'left', + label: 'Docs', + }, + { + to: '/docs/examples', + position: 'left', + label: 'Examples', + }, + { + to: '/docs/architecture', + position: 'left', + label: 'Architecture', + }, + { + href: 'https://github.com/aws-samples/sample-aws-eks-auto-mode', + label: 'GitHub', + position: 'right', + }, + ], + }, + footer: { + style: 'dark', + links: [ + { + title: 'Docs', + items: [ + {label: 'Introduction', to: '/docs/intro'}, + {label: 'Getting Started', to: '/docs/getting-started'}, + {label: 'Examples', to: '/docs/examples'}, + {label: 'Architecture', to: '/docs/architecture'}, + ], + }, + { + title: 'Resources', + items: [ + {label: 'EKS Auto Mode docs', href: 'https://docs.aws.amazon.com/eks/latest/userguide/automode.html'}, + {label: 'Karpenter docs', href: 'https://karpenter.sh/docs/'}, + {label: 'AWS Samples', href: 'https://github.com/aws-samples'}, + ], + }, + { + title: 'More', + items: [ + {label: 'GitHub', href: 'https://github.com/aws-samples/sample-aws-eks-auto-mode'}, + {label: 'Contributing', to: '/docs/contributing'}, + ], + }, + ], + copyright: `Built by AWS Solutions Architects · MIT-0 License · Copyright © ${new Date().getFullYear()} Amazon.com, Inc. or its affiliates.`, + }, + prism: { + theme: prismThemes.github, + darkTheme: prismThemes.dracula, + additionalLanguages: ['bash', 'hcl', 'yaml', 'json'], + }, + } satisfies Preset.ThemeConfig, +}; + +export default config; diff --git a/misc/website/package-lock.json b/misc/website/package-lock.json new file mode 100644 index 0000000..e67b621 --- /dev/null +++ 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write-translations", + "write-heading-ids": "docusaurus write-heading-ids", + "typecheck": "tsc" + }, + "dependencies": { + "@docusaurus/core": "3.10.1", + "@docusaurus/preset-classic": "3.10.1", + "@mdx-js/react": "^3.0.0", + "clsx": "^2.0.0", + "prism-react-renderer": "^2.3.0", + "react": "^18.0.0", + "react-dom": "^18.0.0" + }, + "devDependencies": { + "@docusaurus/module-type-aliases": "3.10.1", + "@docusaurus/tsconfig": "3.10.1", + "@docusaurus/types": "3.10.1", + "typescript": "~5.5.2" + }, + "browserslist": { + "production": [">0.5%", "not dead", "not op_mini all"], + "development": ["last 3 chrome version", "last 3 firefox version", "last 5 safari version"] + }, + "engines": { + "node": ">=18.0" + } +} diff --git a/misc/website/sidebars.ts b/misc/website/sidebars.ts new file mode 100644 index 0000000..7af6655 --- /dev/null +++ b/misc/website/sidebars.ts @@ -0,0 +1,7 @@ +import type {SidebarsConfig} from '@docusaurus/plugin-content-docs'; + +const sidebars: SidebarsConfig = { + docsSidebar: [{type: 'autogenerated', dirName: '.'}], +}; + +export default sidebars; diff --git a/misc/website/src/components/CopyMarkdownButton.tsx b/misc/website/src/components/CopyMarkdownButton.tsx new file mode 100644 index 0000000..ca4f92d --- /dev/null +++ b/misc/website/src/components/CopyMarkdownButton.tsx @@ -0,0 +1,61 @@ +import React, {useState, useCallback} from 'react'; + +function toRawUrl(editUrl: string): string | null { + const match = editUrl.match( + /^https:\/\/github\.com\/([^/]+)\/([^/]+)\/(blob|edit)\/([^/]+)\/(.+)$/ + ); + if (!match) return null; + const [, owner, repo, , branch, path] = match; + return `https://raw.githubusercontent.com/${owner}/${repo}/${branch}/${path}`; +} + +interface Props { + editUrl: string; +} + +export default function CopyMarkdownButton({editUrl}: Props): React.JSX.Element | null { + const [copied, setCopied] = useState(false); + const rawUrl = toRawUrl(editUrl); + + if (!rawUrl) return null; + + const handleCopy = useCallback(async () => { + try { + const res = await fetch(rawUrl); + if (!res.ok) throw new Error(`HTTP ${res.status}`); + const text = await res.text(); + await navigator.clipboard.writeText(text); + setCopied(true); + setTimeout(() => setCopied(false), 2000); + } catch { + window.open(rawUrl, '_blank'); + } + }, [rawUrl]); + + return ( + + ); +} diff --git a/misc/website/src/css/custom.css b/misc/website/src/css/custom.css new file mode 100644 index 0000000..14e4dd3 --- /dev/null +++ b/misc/website/src/css/custom.css @@ -0,0 +1,214 @@ +@keyframes float { + 0%, 100% { transform: translateY(0); } + 50% { transform: translateY(-8px); } +} + +:root { + --ifm-color-primary: #ff9900; + --ifm-color-primary-dark: #e68a00; + --ifm-color-primary-darker: #d98200; + --ifm-color-primary-darkest: #b36b00; + --ifm-color-primary-light: #ffa31a; + --ifm-color-primary-lighter: #ffa826; + --ifm-color-primary-lightest: #ffb84d; + --ifm-font-family-base: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, sans-serif; + --ifm-heading-font-weight: 700; + --ifm-line-height-base: 1.75; + --ifm-code-font-size: 95%; + --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1); +} + +[data-theme='dark'] { + --ifm-color-primary: #ff9900; + --ifm-color-primary-dark: #e68a00; + --ifm-color-primary-darker: #d98200; + --ifm-color-primary-darkest: #b36b00; + --ifm-color-primary-light: #ffa31a; + --ifm-color-primary-lighter: #ffa826; + --ifm-color-primary-lightest: #ffb84d; + --ifm-background-color: #0a0e17; + --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3); +} + +.copy-page-button { + display: inline-flex; + align-items: center; + gap: 6px; + padding: 6px 14px; + font-size: 0.8rem; + font-weight: 600; + color: var(--ifm-color-primary); + background: rgba(255, 153, 0, 0.08); + border: 1px solid rgba(255, 153, 0, 0.25); + border-radius: 9999px; + cursor: pointer; + transition: all 0.2s ease; + position: absolute; + top: 12px; + right: 12px; +} + +.copy-page-button:hover { + background: rgba(255, 153, 0, 0.15); + border-color: rgba(255, 153, 0, 0.4); +} + +.copy-page-icon { + width: 14px; + height: 14px; +} + +.theme-doc-markdown { + position: relative; +} + +/* Landing page */ +.landing-hero { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + min-height: 55vh; + padding: 4rem 2rem; + text-align: center; + background: + radial-gradient(ellipse at 50% 0%, rgba(255, 153, 0, 0.08) 0%, transparent 60%), + radial-gradient(ellipse at 50% 50%, #0f1724 0%, #0a0e17 100%); + overflow: hidden; + position: relative; +} + +.landing-hero::before { + content: ''; + position: absolute; + inset: 0; + background-image: radial-gradient(rgba(255, 255, 255, 0.04) 1px, transparent 1px); + background-size: 32px 32px; + pointer-events: none; +} + +[data-theme='light'] .landing-hero { + background: linear-gradient(180deg, #ffffff 0%, #f8fafc 100%); +} + +[data-theme='light'] .landing-hero::before { + background-image: radial-gradient(rgba(0, 0, 0, 0.04) 1px, transparent 1px); +} + +.landing-hero__container { + position: relative; + z-index: 1; + max-width: 700px; +} + +.landing-hero__badge { + display: inline-block; + padding: 6px 16px; + font-size: 0.8rem; + font-weight: 600; + letter-spacing: 0.03em; + color: #ff9900; + background: rgba(255, 153, 0, 0.1); + border: 1px solid rgba(255, 153, 0, 0.25); + border-radius: 9999px; + margin-bottom: 1.5rem; +} + +.landing-hero__title { + font-size: 3rem; + font-weight: 800; + margin-bottom: 1rem; + color: #f1f5f9; +} + +[data-theme='light'] .landing-hero__title { + color: #1e293b; +} + +.landing-hero__tagline { + font-size: 1.2rem; + color: #94a3b8; + margin-bottom: 2rem; + line-height: 1.6; +} + +[data-theme='light'] .landing-hero__tagline { + color: #64748b; +} + +.landing-hero__actions { + display: flex; + gap: 1rem; + justify-content: center; + flex-wrap: wrap; +} + +.landing-hero__btn { + padding: 12px 28px; + border-radius: 8px; + font-weight: 600; + font-size: 1rem; + text-decoration: none; + transition: all 0.2s ease; +} + +.landing-hero__btn--primary { + background: #ff9900; + color: #000; +} + +.landing-hero__btn--primary:hover { + background: #ffb84d; + color: #000; + text-decoration: none; +} + +.landing-hero__btn--secondary { + background: rgba(255, 153, 0, 0.1); + color: #ff9900; + border: 1px solid rgba(255, 153, 0, 0.3); +} + +.landing-hero__btn--secondary:hover { + background: rgba(255, 153, 0, 0.2); + color: #ff9900; + text-decoration: none; +} + +.landing-features { + padding: 4rem 2rem; + background: var(--ifm-background-color); +} + +.landing-features__grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); + gap: 1.5rem; + max-width: 960px; + margin: 0 auto; +} + +.landing-features__card { + padding: 1.5rem; + border-radius: 12px; + border: 1px solid rgba(255, 153, 0, 0.15); + background: rgba(255, 153, 0, 0.03); + transition: all 0.2s ease; +} + +.landing-features__card:hover { + border-color: rgba(255, 153, 0, 0.35); + background: rgba(255, 153, 0, 0.06); +} + +.landing-features__card h3 { + color: #ff9900; + margin-bottom: 0.5rem; + font-size: 1.1rem; +} + +.landing-features__card p { + margin: 0; + font-size: 0.95rem; + line-height: 1.6; +} diff --git a/misc/website/src/pages/index.tsx b/misc/website/src/pages/index.tsx new file mode 100644 index 0000000..84e295f --- /dev/null +++ b/misc/website/src/pages/index.tsx @@ -0,0 +1,66 @@ +import React from 'react'; +import Layout from '@theme/Layout'; +import Link from '@docusaurus/Link'; + +function Hero() { + return ( +
+
+ AWS Samples +

EKS Auto Mode Samples

+

+ Learn EKS Auto Mode through deployable Terraform examples. +
+ Deploy once, explore everything. +

+
+ + Get Started + + + View Examples + +
+
+
+ ); +} + +const features = [ + { + title: 'Compute Patterns', + description: 'Graviton, GPU, Spot, and Neuron — each with a self-contained example explaining the "why" alongside the "how."', + }, + { + title: 'Cost Optimization', + description: 'ODCR targeting, disruption budgets, static capacity pools, and OD/Spot mixed scheduling with overprovision headroom.', + }, + { + title: 'Operational Patterns', + description: '5-layer resource tagging, automated cleanup playbook, CloudWatch Container Insights, and security considerations.', + }, +]; + +function Features() { + return ( +
+
+ {features.map((f) => ( +
+

{f.title}

+

{f.description}

+
+ ))} +
+
+ ); +} + +export default function Home(): React.JSX.Element { + return ( + + + + + ); +} diff --git a/misc/website/static/img/favicon.svg b/misc/website/static/img/favicon.svg new file mode 100644 index 0000000..b825a11 --- /dev/null +++ b/misc/website/static/img/favicon.svg @@ -0,0 +1,4 @@ + + + EKS + diff --git a/misc/website/tsconfig.json b/misc/website/tsconfig.json new file mode 100644 index 0000000..d250afa --- /dev/null +++ b/misc/website/tsconfig.json @@ -0,0 +1,6 @@ +{ + "extends": "@docusaurus/tsconfig", + "compilerOptions": { + "baseUrl": "." + } +} diff --git a/nodepool-templates/gpu-nodepool.yaml.tpl b/nodepool-templates/gpu-nodepool.yaml.tpl index 11eb9e5..de48090 100644 --- a/nodepool-templates/gpu-nodepool.yaml.tpl +++ b/nodepool-templates/gpu-nodepool.yaml.tpl @@ -13,6 +13,10 @@ spec: aws:eks:cluster-name: ${cluster_name} tags: ${indent(4, yamlencode(tags))} +%{ if kms_key_id != "" ~} + ephemeralStorage: + kmsKeyID: ${kms_key_id} +%{ endif ~} --- apiVersion: karpenter.sh/v1 kind: NodePool @@ -28,7 +32,7 @@ spec: requirements: - key: "eks.amazonaws.com/instance-family" operator: In - values: [ "g5", "g6", "g6e" ] + values: ["g5", "g6", "g6e", "p5", "p5e"] - key: "karpenter.sh/capacity-type" operator: In values: ["spot", "on-demand"] @@ -39,5 +43,7 @@ spec: limits: cpu: 1000 disruption: - consolidationPolicy: WhenEmpty - consolidateAfter: 30s + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 60s + budgets: + - nodes: "10%" diff --git a/nodepool-templates/graviton-nodepool.yaml.tpl b/nodepool-templates/graviton-nodepool.yaml.tpl index eff485f..5a4c74c 100644 --- a/nodepool-templates/graviton-nodepool.yaml.tpl +++ b/nodepool-templates/graviton-nodepool.yaml.tpl @@ -13,6 +13,10 @@ spec: aws:eks:cluster-name: ${cluster_name} tags: ${indent(4, yamlencode(tags))} +%{ if kms_key_id != "" ~} + ephemeralStorage: + kmsKeyID: ${kms_key_id} +%{ endif ~} --- apiVersion: karpenter.sh/v1 kind: NodePool @@ -39,5 +43,7 @@ spec: limits: cpu: 1000 disruption: - consolidationPolicy: WhenEmpty - consolidateAfter: 30s + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 60s + budgets: + - nodes: "10%" diff --git a/nodepool-templates/neuron-nodepool.yaml.tpl b/nodepool-templates/neuron-nodepool.yaml.tpl index 8e796b2..e0b5cfa 100644 --- a/nodepool-templates/neuron-nodepool.yaml.tpl +++ b/nodepool-templates/neuron-nodepool.yaml.tpl @@ -13,6 +13,10 @@ spec: aws:eks:cluster-name: ${cluster_name} tags: ${indent(4, yamlencode(tags))} +%{ if kms_key_id != "" ~} + ephemeralStorage: + kmsKeyID: ${kms_key_id} +%{ endif ~} --- apiVersion: karpenter.sh/v1 kind: NodePool @@ -28,7 +32,7 @@ spec: requirements: - key: "eks.amazonaws.com/instance-family" operator: In - values: ["inf2"] + values: ["inf2", "trn2"] - key: "karpenter.sh/capacity-type" operator: In values: ["spot", "on-demand"] @@ -39,5 +43,7 @@ spec: limits: cpu: 1000 disruption: - consolidationPolicy: WhenEmpty - consolidateAfter: 30s + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 60s + budgets: + - nodes: "10%" diff --git a/nodepool-templates/spot-nodepool.yaml.tpl b/nodepool-templates/spot-nodepool.yaml.tpl index 0bae0f6..ff65e25 100644 --- a/nodepool-templates/spot-nodepool.yaml.tpl +++ b/nodepool-templates/spot-nodepool.yaml.tpl @@ -13,6 +13,10 @@ spec: aws:eks:cluster-name: ${cluster_name} tags: ${indent(4, yamlencode(tags))} +%{ if kms_key_id != "" ~} + ephemeralStorage: + kmsKeyID: ${kms_key_id} +%{ endif ~} --- apiVersion: karpenter.sh/v1 kind: NodePool @@ -42,5 +46,7 @@ spec: limits: cpu: 1000 disruption: - consolidationPolicy: WhenEmpty - consolidateAfter: 30s + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 60s + budgets: + - nodes: "10%" diff --git a/terraform/eks.tf b/terraform/eks.tf index 1c67a25..7920bb0 100644 --- a/terraform/eks.tf +++ b/terraform/eks.tf @@ -21,6 +21,10 @@ module "eks" { enabled = true node_pools = ["general-purpose"] } + + # Module handles the IAM policy for custom tags on Auto Mode resources (Layer 3 enabler) + enable_auto_mode_custom_tags = true + cluster_tags = local.tags tags = local.tags } \ No newline at end of file diff --git a/terraform/observability.tf b/terraform/observability.tf new file mode 100644 index 0000000..b66af06 --- /dev/null +++ b/terraform/observability.tf @@ -0,0 +1,15 @@ +resource "aws_eks_addon" "cloudwatch_observability" { + count = var.enable_observability ? 1 : 0 + cluster_name = module.eks.cluster_name + addon_name = "amazon-cloudwatch-observability" + + resolve_conflicts_on_update = "OVERWRITE" + + depends_on = [module.eks] +} + +resource "aws_iam_role_policy_attachment" "cloudwatch_agent" { + count = var.enable_observability ? 1 : 0 + role = module.eks.node_iam_role_name + policy_arn = "arn:aws:iam::aws:policy/CloudWatchAgentServerPolicy" +} diff --git a/terraform/outputs.tf b/terraform/outputs.tf index 36af89b..b49efcf 100644 --- a/terraform/outputs.tf +++ b/terraform/outputs.tf @@ -22,3 +22,8 @@ output "name" { description = "Cluster name variable" value = var.name } + +output "cloudwatch_dashboard_url" { + description = "CloudWatch Container Insights URL (only when observability is enabled)" + value = var.enable_observability ? "https://${var.region}.console.aws.amazon.com/cloudwatch/home?region=${var.region}#container-insights:infrastructure" : null +} diff --git a/terraform/setup.tf b/terraform/setup.tf index 7d65e0c..ec4bc7f 100644 --- a/terraform/setup.tf +++ b/terraform/setup.tf @@ -9,6 +9,7 @@ resource "local_file" "setup_graviton" { node_iam_role_name = module.eks.node_iam_role_name cluster_name = module.eks.cluster_name tags = local.tags + kms_key_id = var.ephemeral_storage_kms_key_id }) filename = "${path.module}/../nodepools/graviton-nodepool.yaml" } @@ -18,6 +19,7 @@ resource "local_file" "setup_spot" { node_iam_role_name = module.eks.node_iam_role_name cluster_name = module.eks.cluster_name tags = local.tags + kms_key_id = var.ephemeral_storage_kms_key_id }) filename = "${path.module}/../nodepools/spot-nodepool.yaml" } @@ -27,6 +29,7 @@ resource "local_file" "setup_gpu" { node_iam_role_name = module.eks.node_iam_role_name cluster_name = module.eks.cluster_name tags = local.tags + kms_key_id = var.ephemeral_storage_kms_key_id }) filename = "${path.module}/../nodepools/gpu-nodepool.yaml" } @@ -36,10 +39,31 @@ resource "local_file" "setup_neuron" { node_iam_role_name = module.eks.node_iam_role_name cluster_name = module.eks.cluster_name tags = local.tags + kms_key_id = var.ephemeral_storage_kms_key_id }) filename = "${path.module}/../nodepools/neuron-nodepool.yaml" } +resource "local_file" "setup_odcr_nodepool" { + content = templatefile("${path.module}/../examples/capacity-reservation/odcr-nodepool.yaml.tpl", { + node_iam_role_name = module.eks.node_iam_role_name + cluster_name = module.eks.cluster_name + tags = local.tags + kms_key_id = var.ephemeral_storage_kms_key_id + }) + filename = "${path.module}/../examples/capacity-reservation/odcr-nodepool.yaml" +} + +resource "local_file" "setup_static_nodepool" { + content = templatefile("${path.module}/../examples/static-capacity/static-nodepool.yaml.tpl", { + node_iam_role_name = module.eks.node_iam_role_name + cluster_name = module.eks.cluster_name + tags = local.tags + kms_key_id = var.ephemeral_storage_kms_key_id + }) + filename = "${path.module}/../examples/static-capacity/static-nodepool.yaml" +} + # Example workload templates: render Ingress YAMLs with the public hostname # branch toggled by var.base_domain. ALBs carry cluster tags via the per-example # IngressClassParams.spec.tags rendered in ingressclass.tf. diff --git a/terraform/tagging.tf b/terraform/tagging.tf index 3928032..4ed82cd 100644 --- a/terraform/tagging.tf +++ b/terraform/tagging.tf @@ -1,143 +1,20 @@ # -------------------------------------------------------------------------- # Five-layer tag propagation for EKS Auto Mode. # -# Layers 1 and 2 (provider default_tags, EKS cluster_tags) live in main.tf -# and eks.tf respectively. Layer 3 (NodeClass spec.tags) lives in setup.tf -# and the nodepool-templates/. This file carries the remaining pieces: +# Layer 1: provider default_tags (main.tf) — all TF-created resources +# Layer 2: EKS cluster_tags (eks.tf) — EKS primary security group +# Layer 3: NodeClass spec.tags (nodepool-templates/) — EC2/EBS/ENI from Auto Mode +# Layer 4: StorageClass tagSpecification (below) — PVC-created EBS volumes +# Layer 5: IngressClassParams (ingressclass.tf) — ALB/TG/Listener # -# - IAM policy permitting the cluster role to set var.tags on the AWS -# resources that EKS Auto Mode creates (load-bearing for L3 / L4 / L5). -# - StorageClass (Layer 4) so PVCs default to a class that tags the EBS -# volume at create time. -# - IngressClass + IngressClassParams (Layer 5) so Ingresses default to a -# class that tags the resulting ALB / TG / Listener. +# The IAM policy enabling custom tags on Auto Mode resources (required for +# Layers 3-5) is managed by the EKS module via enable_auto_mode_custom_tags +# (default true since module v20.31). No manual IAM policy needed. # -# See claude-md/TAGGING.md for the full pattern explanation, including the -# explicit-override gaps these defaults do not cover (e.g. PVCs with -# storageClassName: gp3, Ingresses with an ingressClassName override). +# See claude-md/TAGGING.md for the full pattern explanation. # -------------------------------------------------------------------------- -# -------------------------------------------------------------------------- -# IAM: cluster role permission to set custom tags -# -# EKS Auto Mode's managed AmazonEKS*Policies gate ec2:RunInstances / -# CreateFleet / CreateVolume / CreateNetworkInterface / CreateLoadBalancer -# with a ForAllValues tag-key allowlist (eks:*, kubernetes.io/*, karpenter.*). -# Custom tags from var.tags fall outside that allowlist so the managed Allow -# does not match and the call is denied. -# -# This inline policy unions in the AWS-prescribed "Custom AWS tags for EKS -# Auto resources" statements so the cluster role can create Auto-Mode -# resources carrying var.tags. -# -# Source: https://docs.aws.amazon.com/eks/latest/userguide/auto-cluster-iam-role.html -# -------------------------------------------------------------------------- -resource "aws_iam_role_policy" "eks_cluster_allow_custom_tags" { - name = "${module.eks.cluster_name}-allow-custom-tags" - role = module.eks.cluster_iam_role_name - - policy = jsonencode({ - Version = "2012-10-17" - Statement = [ - { - Sid = "Compute" - Effect = "Allow" - Action = [ - "ec2:CreateFleet", - "ec2:RunInstances", - "ec2:CreateLaunchTemplate", - ] - Resource = "*" - Condition = { - StringEquals = { - "aws:RequestTag/eks:eks-cluster-name" = "$${aws:PrincipalTag/eks:eks-cluster-name}" - } - StringLike = { - "aws:RequestTag/eks:kubernetes-node-class-name" = "*" - "aws:RequestTag/eks:kubernetes-node-pool-name" = "*" - } - } - }, - { - Sid = "Storage" - Effect = "Allow" - Action = [ - "ec2:CreateVolume", - "ec2:CreateSnapshot", - ] - Resource = [ - "arn:aws:ec2:*:*:volume/*", - "arn:aws:ec2:*:*:snapshot/*", - ] - Condition = { - StringEquals = { - "aws:RequestTag/eks:eks-cluster-name" = "$${aws:PrincipalTag/eks:eks-cluster-name}" - } - } - }, - { - Sid = "Networking" - Effect = "Allow" - Action = "ec2:CreateNetworkInterface" - Resource = "*" - Condition = { - StringEquals = { - "aws:RequestTag/eks:eks-cluster-name" = "$${aws:PrincipalTag/eks:eks-cluster-name}" - } - StringLike = { - "aws:RequestTag/eks:kubernetes-cni-node-name" = "*" - } - } - }, - { - Sid = "LoadBalancer" - Effect = "Allow" - Action = [ - "elasticloadbalancing:CreateLoadBalancer", - "elasticloadbalancing:CreateTargetGroup", - "elasticloadbalancing:CreateListener", - "elasticloadbalancing:CreateRule", - "ec2:CreateSecurityGroup", - ] - Resource = "*" - Condition = { - StringEquals = { - "aws:RequestTag/eks:eks-cluster-name" = "$${aws:PrincipalTag/eks:eks-cluster-name}" - } - } - }, - { - Sid = "ShieldProtection" - Effect = "Allow" - Action = [ - "shield:CreateProtection", - ] - Resource = "*" - Condition = { - StringEquals = { - "aws:RequestTag/eks:eks-cluster-name" = "$${aws:PrincipalTag/eks:eks-cluster-name}" - } - } - }, - { - Sid = "ShieldTagResource" - Effect = "Allow" - Action = [ - "shield:TagResource", - ] - Resource = "arn:aws:shield::*:protection/*" - Condition = { - StringEquals = { - "aws:RequestTag/eks:eks-cluster-name" = "$${aws:PrincipalTag/eks:eks-cluster-name}" - } - } - }, - ] - }) -} - - # -------------------------------------------------------------------------- # Layer 4: StorageClass for tagged EBS volumes # diff --git a/terraform/variables.tf b/terraform/variables.tf index 2f1023c..acd03ac 100644 --- a/terraform/variables.tf +++ b/terraform/variables.tf @@ -12,7 +12,7 @@ variable "region" { variable "eks_cluster_version" { description = "EKS Cluster version" - default = "1.33" + default = "1.34" type = string } @@ -41,4 +41,16 @@ variable "subdomain" { description = "Optional subdomain prefix under var.base_domain (e.g. \"automode\" -> automode.example.com). Ignored when var.base_domain is empty." type = string default = "" +} + +variable "ephemeral_storage_kms_key_id" { + description = "Optional KMS key ID for encrypting ephemeral storage on Auto Mode nodes. Leave empty to use default encryption." + type = string + default = "" +} + +variable "enable_observability" { + description = "Enable CloudWatch Container Insights via EKS addon. Provides cluster metrics, pod logs, and Application Signals tracing. Incurs CloudWatch costs." + type = bool + default = false } \ No newline at end of file diff --git a/terraform/versions.tf b/terraform/versions.tf index 5b45c42..993eb79 100644 --- a/terraform/versions.tf +++ b/terraform/versions.tf @@ -1,10 +1,10 @@ terraform { - required_version = ">= 1.3" + required_version = ">= 1.5" required_providers { aws = { source = "hashicorp/aws" - version = ">= 5.34" + version = ">= 5.79" } kubernetes = { source = "hashicorp/kubernetes" @@ -12,11 +12,11 @@ terraform { } helm = { source = "hashicorp/helm" - version = "~> 2.17.0" + version = "~> 2.17" } kubectl = { source = "alekc/kubectl" - version = "~> 2.1.3" + version = "~> 2.1" } null = { source = "hashicorp/null"