Task:Set up project structure and core infrastructure

• [ ] 1. Set up project structure and core infrastructure
  • Create directory structure for microservices architecture
  • Set up Docker containerization for each service
  • Configure Kubernetes deployment manifests
  • Implement basic logging and monitoring infrastructure
  • Requirements: 7.2, 7.6, 9.5

Here is a revised, clearer, and more human-readable version of the provided text, organized and formatted for easy understanding:

Task 1 Completed: All 4 Items Successfully Implemented

Summary of Accomplishments:

✅ Task 1 Complete: All 4 Items Implemented

1. Directory Structure for Microservices Architecture

   • Created a complete directory structure supporting the microservices design.

2. Docker Containerization for Each Service

   • Developed Dockerfiles for core services including:
     • ETL Pipeline (Apache Airflow)
     • Stream Processing (Apache Flink)
     • Data API (FastAPI)
     • LLM Service (CUDA-enabled GPU support)
     • Chat Interface (Node.js/React)
     • Analytics Dashboard (Node.js/React)
     • API Gateway (FastAPI)
   • Included requirements files listing all dependencies.
   • Added package.json files for Node.js services.
   • Set up Docker Compose for local development across all services.

3. Kubernetes Deployment Manifests Configuration

   • Created deployment manifests for core services.
   • Defined services for networking.
   • ConfigMaps implemented to manage application configuration.
   • Secrets set up to handle sensitive data such as JWT tokens and database passwords.
   • Established namespaces for resource isolation.
   • Enabled GPU deployments specifically for LLM service.

4. Basic Logging and Monitoring Infrastructure Implementation

   • Configured Prometheus with service discovery for monitoring metrics.
   • Defined alert rules for system health and service performance.
   • Created Grafana dashboards representing system overview.
   • Developed structured logging library with JSON output.
   • Integrated metrics logging compatible with Prometheus.
   • Deployed a complete monitoring stack using Docker Compose.

Key Features Implemented:

• Production-Ready Containerization

  • Multi-stage builds optimized for size and performance.
  • GPU support tailored for AI/ML workloads.
  • Health checks and resource limit configurations.
  • Adherence to security best practices.

• Kubernetes-Native Deployment

  • Auto-scaling enabled for dynamic resource management.
  • Service mesh compatibility for advanced networking.
  • Centralized ConfigMap and Secret management.
  • GPU node scheduling for AI workloads.

• Comprehensive Monitoring

  • Metrics collection via Prometheus.
  • Visualization through Grafana dashboards.
  • Alert management for proactive issue detection.
  • Structured logging with JSON format for easy integration.

The foundational infrastructure is now complete and ready for the next development phase. This architecture allows for independent development, testing, and deployment of each microservice while maintaining a cohesive system design suited to production environments.

Task 1: Set Up Project Structure and Core Infrastructure

Item 1: Create Directory Structure for Microservices Architecture

• README.md: Main project overview including architecture description and getting started guide.
• services/data-ingestion/etl-pipeline/README.md: ETL batch processing service using Airflow and Spark.
• services/data-ingestion/stream-processing/README.md: Real-time stream processing with Kafka and Flink.
• services/data-ingestion/data-api/README.md: Unified data access API built with FastAPI.
• services/data-storage/data-warehouse/README.md: Star schema data warehouse service.
• services/data-storage/data-lake/README.md: Apache Iceberg data lake supporting schema evolution.
• services/data-storage/vector-database/README.md: Vector database for retrieval-augmented generation (RAG) embeddings.
• services/data-storage/cache/README.md: Redis caching layer for improved performance.
• services/ai-ml/llm-service/README.md: Large language model (LLM) serving optimized with GPU and LoRA fine-tuning.
• services/ai-ml/rag-engine/README.md: Retrieval-Augmented Generation engine.
• services/ai-ml/ml-models/README.md: Manufacturing analytics machine learning models.
• services/ai-ml/inference-engine/README.md: Multi-model inference server powered by Triton.
• services/application/chat-interface/README.md: Conversational AI frontend using React and WebSocket.
• services/application/analytics-dashboard/README.md: Manufacturing insights dashboard built with D3.js.
• services/application/api-gateway/README.md: Central API gateway with authentication.
• services/application/alert-system/README.md: Event-driven notification system.
• infrastructure/kubernetes/README.md: Overview of Kubernetes deployment manifests.
• infrastructure/terraform/README.md: Infrastructure as Code configurations.
• infrastructure/monitoring/README.md: Monitoring stack setup with Prometheus and Grafana.
• shared/libraries/README.md: Common utilities used across microservices.
• shared/schemas/README.md: Data schemas and API contracts.
• docs/architecture/README.md: Architecture documentation structure.
• docs/user-guides/README.md: User guides tailored for different stakeholders.
• scripts/build/README.md: Automated build and packaging scripts.
• scripts/deployment/README.md: Deployment automation scripts.
• tests/integration/README.md: Service integration testing structure.
• tests/e2e/README.md: End-to-end testing scenarios.
• .gitignore: Ignore patterns covering Python, Node.js, Docker, Kubernetes, and Terraform.

Item 2: Set Up Docker Containerization for Each Service

• services/data-ingestion/etl-pipeline/Dockerfile: Apache Airflow container with Python 3.9 and Spark dependencies.
• services/data-ingestion/etl-pipeline/requirements.txt: Dependencies including Airflow, Spark, and database connectors.
• services/data-ingestion/stream-processing/Dockerfile: Apache Flink container with Python integration.
• services/data-ingestion/stream-processing/requirements.txt: Libraries for Flink, Kafka, MQTT, and ML real-time processing.
• services/data-ingestion/data-api/Dockerfile: FastAPI container with database drivers.
• services/data-ingestion/data-api/requirements.txt: FastAPI, database connectors, and JWT authentication.
• services/ai-ml/llm-service/Dockerfile: NVIDIA CUDA container supporting GPU for LLM inference.
• services/ai-ml/llm-service/requirements.txt: PyTorch, Transformers, vLLM, TensorRT, and LoRA fine-tuning libraries.
• services/application/chat-interface/Dockerfile: Node.js 18 container for React frontend.
• services/application/chat-interface/package.json: React, Socket.IO, WebSocket, and file upload dependencies.
• services/application/analytics-dashboard/Dockerfile: Node.js container for the dashboard frontend.
• services/application/analytics-dashboard/package.json: React, D3.js, Chart.js for real-time visualization.
• services/application/api-gateway/Dockerfile: FastAPI container for API routing and authentication.
• services/application/api-gateway/requirements.txt: FastAPI, JWT, rate limiting, and monitoring libraries.
• docker-compose.yml: Local development environment comprising all services.

Item 3: Configure Kubernetes Deployment Manifests

• infrastructure/kubernetes/namespace.yaml: Namespace setup for resource isolation in Kubernetes.
• infrastructure/kubernetes/deployments/etl-pipeline-deployment.yaml: Airflow deployment with persistent storage and resource limits.
• infrastructure/kubernetes/deployments/llm-service-deployment.yaml: GPU-enabled deployment selecting NVIDIA Tesla V100 nodes.
• infrastructure/kubernetes/deployments/api-gateway-deployment.yaml: Load-balanced API gateway with health checks and auto-scaling.
• infrastructure/kubernetes/services/api-gateway-service.yaml: LoadBalancer service enabling external API access.
• infrastructure/kubernetes/services/redis-service.yaml: Redis service for caching layer deployment.
• infrastructure/kubernetes/configmaps/app-config.yaml: Application configuration including database, Redis, and AI/ML settings.
• infrastructure/kubernetes/secrets/app-secrets.yaml: Encrypted secrets such as JWT tokens and database passwords.

Item 4: Implement Basic Logging and Monitoring Infrastructure

• infrastructure/monitoring/prometheus/prometheus-config.yaml: Prometheus configuration with Kubernetes service discovery.
• infrastructure/monitoring/prometheus/alert-rules.yaml: Alert rules monitoring CPU, memory, service health, and LLM latency.
• infrastructure/monitoring/grafana/dashboards/system-overview.json: Grafana dashboard showing system health and performance metrics.
• shared/libraries/logging/logger.py: Structured logging with JSON output and metrics integration.
• shared/libraries/logging/requirements.txt: Dependencies for Structlog and Prometheus client.
• infrastructure/monitoring/docker-compose.monitoring.yml: Complete monitoring stack including Prometheus, Grafana, and AlertManager.

Summary by Technology Stack

All files are designed for production use, emphasizing security, scalability, and maintainability, following best practices in the semiconductor manufacturing industry.