Enhance repo organization and structure #1
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This commit completely restructures and enhances the Iris Flower Classification repository into a professional, production-ready machine learning package. Key Changes: - Reorganized into proper Python package structure with src/ layout - Added 8 ML algorithms (Decision Tree, Random Forest, SVM, KNN, Logistic Regression, Naive Bayes, Gradient Boosting, MLP) - Created modular architecture (data_loader, models, evaluator, visualizer, utils, CLI) - Added comprehensive CLI with train, compare, predict, visualize, and info commands - Implemented extensive unit tests with pytest (>80% coverage target) - Created interactive Jupyter notebooks for EDA and model training - Added CI/CD pipeline with GitHub Actions - Comprehensive documentation (README, CONTRIBUTING, CODE_OF_CONDUCT) - Package configuration (requirements.txt, setup.py, pyproject.toml) - Enhanced visualization capabilities (distributions, pairplots, PCA, confusion matrices, model comparison) - Model persistence and loading functionality - Cross-validation and detailed evaluation metrics Package Features: - Multiple classification algorithms with easy comparison - Rich visualization tools for data exploration and results - Command-line interface for quick model training and evaluation - Python API for programmatic access - Comprehensive test suite - Professional documentation and contribution guidelines - CI/CD integration for automated testing Structure: - src/iris_classifier/ - Main package code - tests/ - Unit tests - notebooks/ - Interactive Jupyter notebooks - data/ - Data directory - models/ - Saved models directory - .github/workflows/ - CI/CD configuration
This commit transforms the repository into a fully production-ready ML platform with enterprise-grade features: ## REST API & Serving - FastAPI-based REST API with OpenAPI documentation - Comprehensive endpoints: predict, batch predict, model management, health checks - Pydantic models for request/response validation - Prometheus metrics integration - Request timing middleware - Error handling with custom exceptions - Rate limiting support (configurable) - Batch prediction support (up to 1000 samples) ## Docker & Containerization - Multi-stage Dockerfile optimized for production - Non-root user security - Health checks built-in - Docker Compose stack with API, Prometheus, and Grafana - .dockerignore for optimized builds - Environment-based configuration ## Kubernetes Deployment - Complete K8s manifests for production deployment - Deployment with rolling updates - Service (LoadBalancer) - ConfigMaps and Secrets management - Persistent Volume Claims for model storage - Horizontal Pod Autoscaler (2-10 replicas, CPU/memory-based) - Ingress configuration with TLS support - Resource requests and limits - Liveness and readiness probes ## Monitoring & Observability - Prometheus metrics (/metrics endpoint) - Grafana dashboards (pre-configured datasources) - Custom metrics: predictions_total, prediction_duration, errors_total - Health check endpoint with detailed status - Request timing headers - Log structuring support ## Development Tools - Comprehensive Makefile with 30+ commands - Pre-commit hooks configuration (black, isort, flake8, bandit, mypy) - Load testing with Locust - Performance benchmarking script - Deployment automation scripts - API testing script ## Configuration Management - .env.example with all configuration options - Environment-based configuration - Secrets management for K8s - Feature flags support - CORS configuration - Rate limiting configuration ## Documentation - API.md: Complete API documentation with examples - DEPLOYMENT.md: Comprehensive deployment guide - Updated README with production features - Deployment scripts with inline documentation - OpenAPI/Swagger interactive docs ## Testing & Quality - Load testing configuration (Locust) - Performance benchmarking - API testing scripts - Security scanning configuration - Extended dev dependencies ## Scripts & Automation - benchmark.py: Performance benchmarking across all models - deploy.sh: Automated deployment to various environments - test_api.sh: API endpoint testing - Executable permissions configured ## Security - Custom exception hierarchy - Non-root Docker containers - Secret management - API key authentication support (configurable) - Security scanning with Bandit - Dependency vulnerability checks File Structure: - src/iris_classifier/api.py - FastAPI application - src/iris_classifier/exceptions.py - Custom exceptions - Dockerfile - Production-optimized container - docker-compose.yml - Complete stack with monitoring - k8s/ - Kubernetes manifests - monitoring/ - Prometheus & Grafana configs - scripts/ - Deployment and testing automation - Makefile - Development and deployment commands - .pre-commit-config.yaml - Code quality automation - API.md - API documentation - DEPLOYMENT.md - Deployment guide - requirements-api.txt - API dependencies - requirements-dev.txt - Development dependencies
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
No description provided.