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Infrastructure & DevOps Ecosystem

Comprehensive infrastructure automation and DevOps tooling archetypes designed for modern software delivery, CI/CD pipelines, and cloud-native deployments.

Available Components​

CI/CD & Automation​

GitHub Action

Complete GitHub Action repository scaffolding with managed workflows, testing, and automated release management.

BasicGitHub ActionsCI/CDAutomation

Container Orchestration​

Helm Chart Wrapper

Standardized Helm chart for consistent Kubernetes application deployment with multi-environment support.

IntermediateHelmKubernetesDeployment

KubeRay

Ray cluster deployment and management on Kubernetes with auto-scaling and distributed computing capabilities.

AdvancedKubeRayDistributed ComputingML Operations

Documentation​

mdBook Documentation

Modern documentation site generator using mdBook with Markdown, search, and interactive features.

BasicmdBookDocumentationMarkdown

DevOps Architecture Overview​

Technology Stack​

Core Technologies​

  • GitHub Actions: CI/CD automation and workflows
  • Kubernetes: Container orchestration platform
  • Helm: Kubernetes package manager
  • Docker: Containerization platform
  • Terraform: Infrastructure as code

Specialized Tools​

  • KubeRay: Ray cluster management on Kubernetes
  • mdBook: Documentation site generation
  • ArgoCD/Flux: GitOps deployment
  • Prometheus/Grafana: Monitoring and observability

Cloud Providers​

  • AWS: Elastic Kubernetes Service (EKS)
  • Google Cloud: Google Kubernetes Engine (GKE)
  • Azure: Azure Kubernetes Service (AKS)
  • Multi-cloud: Platform-agnostic deployments

Use Cases by Component​

GitHub Actions​

  • Custom Workflows: Reusable workflow components
  • Release Automation: Semantic versioning and publishing
  • Quality Gates: Automated testing and security scanning
  • Multi-environment Deployment: Staged deployment pipelines

Helm Chart Wrapper​

  • Application Deployment: Standardized app packaging
  • Configuration Management: Environment-specific configs
  • Blue-Green Deployments: Zero-downtime updates
  • Multi-tenancy: Isolated application instances

KubeRay​

  • Distributed ML: Large-scale machine learning workloads
  • Data Processing: Parallel data processing pipelines
  • Auto-scaling: Dynamic resource allocation
  • GPU Workloads: Accelerated computing clusters

mdBook Documentation​

  • Technical Documentation: API and user guides
  • Knowledge Base: Internal documentation
  • Project Documentation: Open source project docs
  • Educational Content: Tutorials and learning materials

Getting Started​

Prerequisites​

  • Kubernetes cluster access
  • Docker installed locally
  • Helm 3.x installed
  • GitHub account with Actions enabled

Quick Setup by Use Case​

CI/CD Pipeline​

# Create GitHub Action
archetect render git@github.com:p6m-archetypes/github-action.archetype.git

# Set up repository
git init
git add .
git commit -m "Initial GitHub Action setup"
git remote add origin https://github.com/your-org/your-action.git
git push -u origin main

Kubernetes Deployment​

# Create Helm chart
archetect render git@github.com:p6m-archetypes/helm-chart-wrapper.archetype.git

# Deploy to Kubernetes
helm install myapp . --namespace myapp --create-namespace

Documentation Site​

# Create documentation project
archetect render git@github.com:p6m-archetypes/mdbook.archetype.git

# Start development server
mdbook serve --open

Ray Cluster​

# Deploy KubeRay
archetect render git@github.com:p6m-archetypes/kuberay.archetype.git

# Deploy cluster
./scripts/deploy.sh ray-system production

Best Practices​

CI/CD Pipeline Design​

  • Fail Fast: Quick feedback on code quality issues
  • Security First: Integrate security scanning early
  • Parallel Execution: Run independent tasks concurrently
  • Artifact Management: Proper versioning and storage

Infrastructure as Code​

  • Version Control: All infrastructure configurations in Git
  • Modular Design: Reusable and composable components
  • Environment Parity: Consistent across all environments
  • Documentation: Clear setup and usage instructions

Container Orchestration​

  • Resource Limits: Proper CPU and memory allocation
  • Health Checks: Readiness and liveness probes
  • Security Policies: Pod security standards and RBAC
  • Monitoring: Comprehensive observability setup

Documentation Strategy​

  • Living Documentation: Keep docs updated with code
  • Multiple Formats: Support different learning styles
  • Search Functionality: Easy content discovery
  • Contribution Guidelines: Enable community contributions

Security Considerations​

Access Control​

  • RBAC: Role-based access control in Kubernetes
  • Service Accounts: Minimal privilege principles
  • Secrets Management: Secure handling of sensitive data
  • Network Policies: Traffic segmentation and isolation

Container Security​

  • Image Scanning: Vulnerability assessment
  • Base Images: Use minimal, updated base images
  • Non-root Users: Run containers as non-privileged users
  • Security Contexts: Apply security constraints

CI/CD Security​

  • Secret Scanning: Detect leaked credentials
  • Dependency Scanning: Check for vulnerable dependencies
  • Code Signing: Verify artifact integrity
  • Audit Logging: Track all pipeline activities

Monitoring & Observability​

Key Metrics​

  • Pipeline Success Rate: CI/CD pipeline reliability
  • Deployment Frequency: Release velocity tracking
  • Lead Time: Code to production time
  • Mean Time to Recovery: Incident response time

Alerting Strategy​

  • Proactive Monitoring: Detect issues before impact
  • SLA Tracking: Monitor service level agreements
  • Capacity Planning: Resource utilization trends
  • Security Events: Anomaly detection and response

Dashboards​

  • Executive Dashboards: High-level KPIs
  • Operational Dashboards: Real-time system health
  • Development Dashboards: Team productivity metrics
  • Security Dashboards: Threat and compliance status

Scaling & Performance​

Horizontal Scaling​

  • Auto-scaling: Dynamic resource allocation
  • Load Balancing: Traffic distribution
  • Multi-region: Geographic distribution
  • Caching: Performance optimization

Vertical Scaling​

  • Resource Optimization: Right-sizing workloads
  • Performance Tuning: Application optimization
  • Database Scaling: Data layer optimization
  • Network Optimization: Bandwidth and latency

Disaster Recovery​

Backup Strategies​

  • Data Backup: Regular data protection
  • Configuration Backup: Infrastructure state preservation
  • Cross-region Replication: Geographic redundancy
  • Point-in-time Recovery: Granular restore capabilities

Business Continuity​

  • Failover Procedures: Automated disaster response
  • Recovery Testing: Regular DR exercises
  • Documentation: Clear recovery procedures
  • Communication Plans: Stakeholder notification

Cost Optimization​

Resource Management​

  • Right-sizing: Optimal resource allocation
  • Spot Instances: Cost-effective compute
  • Reserved Capacity: Long-term commitments
  • Resource Cleanup: Automated garbage collection

Monitoring & Analytics​

  • Cost Tracking: Resource usage visibility
  • Budget Alerts: Spending threshold notifications
  • Optimization Recommendations: Automated suggestions
  • Chargeback: Team-based cost allocation

Support & Community​

Documentation​

  • Architecture Guides - Infrastructure patterns documented in each archetype
  • Deployment Tutorials - Step-by-step guides included with generated projects
  • Troubleshooting - Common issues and solutions in archetype documentation
  • Best Practices - DevOps patterns embedded in archetype templates

Community Resources​

  • GitHub Discussions for questions
  • Slack channels for real-time support
  • Regular office hours and Q&A sessions
  • Contribution guidelines for improvements