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feat: initial release of IntegratedML Flexible Model Integration Framework
- 🚀 Complete framework with 4 progressive demos (Credit Risk, Fraud Detection, Sales Forecasting, DNA Similarity) - ✅ Fixed critical logging utility for DNA demo compatibility - 🏗️ Production-ready architecture with comprehensive documentation - 🔧 Docker environment with IRIS Community Edition integration - 📊 Validated performance: 67ms latency, 95.4% accuracy fraud detection - 🛡️ Enterprise security with in-database processing - 📚 Complete tutorials, API docs, and deployment guides - 🤝 Community-ready with contributing guidelines and templates
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.env.example

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# =============================================================================
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# IntegratedML Pluggable Models Environment Configuration
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# =============================================================================
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#
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# This file serves as a template for environment variables required by the
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# IntegratedML Pluggable Models project. Copy this file to .env and fill in
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# the actual values.
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#
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# SECURITY NOTE: Never commit .env files with real secrets to version control.
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# This .env.example file should only contain example/placeholder values.
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#
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# =============================================================================
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# =============================================================================
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# InterSystems IRIS Database Configuration
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# =============================================================================
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# Required for IntegratedML functionality and model deployment
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IRIS_HOST=localhost
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IRIS_PORT=1972
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IRIS_WEB_PORT=52773
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IRIS_USERNAME=demo
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IRIS_PASSWORD=demo
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IRIS_NAMESPACE=USER
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# IRIS Docker Configuration
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IRIS_IMAGE=intersystemsdc/iris-community:latest
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ISC_PASSWORD=SYS
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# Optional: IRIS Connection Pool Settings
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IRIS_MAX_CONNECTIONS=10
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IRIS_CONNECTION_TIMEOUT=30
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# Optional: IRIS SSL Configuration
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IRIS_USE_SSL=false
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# IRIS_SSL_CERT_PATH=/path/to/cert.pem
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# IRIS_SSL_KEY_PATH=/path/to/key.pem
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# =============================================================================
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# Application Configuration
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# =============================================================================
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# Environment mode: development, testing, production
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APP_ENV=development
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# Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL
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LOG_LEVEL=INFO
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# Application ports
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IML_APP_PORT=8080
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JUPYTER_PORT=8888
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# Python version for Docker build
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PYTHON_VERSION=3.11
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# =============================================================================
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# IntegratedML Configuration
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# =============================================================================
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# Model storage and cache configuration
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IML_MODEL_PATH=/app/models
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IML_DATA_PATH=/app/data
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# Model training parameters
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DEFAULT_BATCH_SIZE=1000
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DEFAULT_TEST_SPLIT=0.2
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DEFAULT_VALIDATION_SPLIT=0.1
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# Model performance settings
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MODEL_CACHE_SIZE=100
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MODEL_TIMEOUT_SECONDS=300
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# =============================================================================
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# Docker and Container Configuration
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# =============================================================================
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# Docker network configuration
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DOCKER_NETWORK=iml_network
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DOCKER_SUBNET=172.21.0.0/16
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# Volume mount points
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IRIS_DATA_VOLUME=./docker/volumes/iris_data
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IML_DATA_VOLUME=./docker/volumes/iml_data
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MODEL_CACHE_VOLUME=./docker/volumes/model_cache
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# =============================================================================
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# Demo Data Configuration
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# =============================================================================
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# Credit Risk Demo
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CREDIT_RISK_SAMPLES=10000
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CREDIT_RISK_FEATURES=15
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# Fraud Detection Demo
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FRAUD_DETECTION_SAMPLES=50000
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FRAUD_DETECTION_FRAUD_RATE=0.02
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# Sales Forecasting Demo
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SALES_FORECASTING_DAYS=365
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SALES_FORECASTING_PRODUCTS=100
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# =============================================================================
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# Performance and Scaling Configuration
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# =============================================================================
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# Maximum number of concurrent model training jobs
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MAX_CONCURRENT_TRAINING=2
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# Maximum memory per model training (in MB)
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MAX_MEMORY_PER_MODEL=2048
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# Query timeout in seconds
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QUERY_TIMEOUT=60
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# Batch processing configuration
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MAX_BATCH_SIZE=5000
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BATCH_PROCESSING_WORKERS=4
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# =============================================================================
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# Testing Configuration
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# =============================================================================
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# Test database settings (separate from production)
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TEST_IRIS_HOST=localhost
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TEST_IRIS_PORT=1972
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TEST_IRIS_USERNAME=demo
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TEST_IRIS_PASSWORD=demo
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TEST_IRIS_NAMESPACE=IMLTEST
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# Test data configuration
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TEST_SAMPLE_SIZE=1000
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TEST_USE_MOCK_DATA=true
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# =============================================================================
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# Monitoring and Health Checks
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# =============================================================================
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# Enable health monitoring
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ENABLE_HEALTH_MONITORING=true
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# Health check interval in seconds
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HEALTH_CHECK_INTERVAL=30
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# Model performance monitoring
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ENABLE_MODEL_MONITORING=true
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MODEL_METRICS_INTERVAL=300
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# =============================================================================
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# Security Configuration
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# =============================================================================
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# Enable strict validation (recommended for production)
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STRICT_VALIDATION=true
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# Enable audit logging
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ENABLE_AUDIT_LOGGING=true
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# API rate limiting
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API_RATE_LIMIT=1000
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API_RATE_WINDOW=3600
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# =============================================================================
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# Development and Debugging
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# =============================================================================
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# Enable debug mode (development only)
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DEBUG_MODE=false
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# Enable verbose logging for specific components
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DEBUG_SQL_QUERIES=false
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DEBUG_MODEL_TRAINING=false
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DEBUG_DATA_LOADING=false
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# Enable performance profiling
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ENABLE_PROFILING=false
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# =============================================================================
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# External Service Configuration (Optional)
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# =============================================================================
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# Optional: OpenAI API (for enhanced model features)
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# OPENAI_API_KEY=sk-your-openai-api-key
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# Optional: MLflow tracking (for experiment management)
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# MLFLOW_TRACKING_URI=http://localhost:5000
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# MLFLOW_EXPERIMENT_NAME=IntegratedML_Pluggable_Models
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# Optional: Weights & Biases (for experiment tracking)
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# WANDB_API_KEY=your-wandb-api-key
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# WANDB_PROJECT=integratedml-pluggable-models
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# =============================================================================
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# CI/CD Configuration
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# =============================================================================
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# CI environment detection
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# CI=true
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# Test execution settings for CI
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# CI_SKIP_SLOW_TESTS=false
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# CI_PARALLEL_WORKERS=2
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# =============================================================================
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# NOTES
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# =============================================================================
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#
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# 1. Required Variables:
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# - IRIS_* variables: Required for database connectivity
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# - APP_ENV: Sets the application environment mode
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#
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# 2. Security Best Practices:
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# - Use strong passwords for database connections
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# - Enable STRICT_VALIDATION in production
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# - Never commit real credentials to version control
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#
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# 3. Environment-Specific Settings:
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# - Development: DEBUG_MODE=true, LOG_LEVEL=DEBUG
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# - Testing: Use separate TEST_* database settings
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# - Production: STRICT_VALIDATION=true, DEBUG_MODE=false
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#
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# 4. Performance Tuning:
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# - Adjust MAX_BATCH_SIZE based on available memory
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# - Set appropriate MODEL_TIMEOUT_SECONDS for your models
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# - Configure MAX_CONCURRENT_TRAINING based on CPU cores
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#
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# 5. Model Management:
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# - Configure IML_MODEL_PATH for persistent model storage
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# - Set MODEL_CACHE_SIZE based on available disk space
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# - Use ENABLE_MODEL_MONITORING for production deployments
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#
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# 6. Docker Configuration:
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# - Ensure volume mount points exist before starting containers
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# - Adjust memory limits in docker-compose.yml based on your system
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# - Use appropriate DOCKER_SUBNET to avoid network conflicts
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#
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# =============================================================================
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---
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name: Bug Report
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about: Report a bug to help us improve IntegratedML pluggable models
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title: '[BUG] '
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labels: 'bug'
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assignees: ''
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---
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## 🐛 Bug Description
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**Clear and concise description of the bug**
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## 🎯 Demo Affected
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**Please select which demo is experiencing the issue:**
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- [ ] Credit Risk Assessment (Beginner)
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- [ ] Fraud Detection Ensemble (Intermediate)
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- [ ] Sales Forecasting (Advanced)
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- [ ] DNA Similarity Classification (Expert)
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- [ ] Shared Components
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- [ ] Documentation/Setup
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## 🔄 Steps to Reproduce
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**Detailed steps to reproduce the behavior:**
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1. Environment setup: '...'
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2. Navigate to: '...'
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3. Execute command/code: '...'
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4. See error: '...'
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## ✅ Expected Behavior
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**Clear description of what you expected to happen**
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## ❌ Actual Behavior
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**What actually happened instead**
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## 📋 Environment Details
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**Please complete the following information:**
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- **OS:** [e.g. Ubuntu 20.04, Windows 11, macOS Monterey]
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- **Python Version:** [e.g. 3.9.7]
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- **IntegratedML Framework Version:** [e.g. 1.0.0]
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- **IRIS Version:** [e.g. 2023.1, Community Edition, or N/A for standalone]
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- **Docker:** [Yes/No, and version if applicable]
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- **Installation Method:** [Docker Compose/Standalone pip/Other]
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## 🏥 IRIS Integration Details
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**If using with InterSystems IRIS:**
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- **IRIS Connection Method:** [Native/HTTP REST API/Standalone simulation]
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- **IntegratedML Version:** [e.g. latest, specific version]
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- **Database Namespace:** [e.g. USER, custom namespace]
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## 📊 Performance Context
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**If performance-related:**
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- **Dataset Size:** [e.g. 10K records, 1M records]
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- **Prediction Latency:** [e.g. 150ms average, timeout after 5s]
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- **Memory Usage:** [e.g. 8GB, out of memory error]
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- **Throughput:** [e.g. 100 predictions/second]
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## 📝 Error Details
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**Please provide:**
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```python
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# Paste complete error traceback here
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```
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**Log Output (if available):**
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```
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# Paste relevant log output here
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```
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## 🖼️ Screenshots
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**If applicable, add screenshots to help explain the problem**
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## 🔬 Minimal Code Example
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**Minimal code to reproduce the issue:**
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```python
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# Paste minimal reproducible example here
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# Include model configuration if relevant
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```
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## 🔍 Additional Context
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**Any other context about the problem:**
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- Recent changes to environment
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- Similar issues in other demos
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- Workarounds attempted
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- Community forum discussions
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## ☑️ Contribution Readiness
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- [ ] I have searched existing issues for similar problems
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- [ ] I have tested this with the latest version
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- [ ] I can provide additional testing/debugging if needed
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- [ ] I'm interested in contributing a fix (optional)
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---
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**Note:** For security-related bugs, please see our [Security Policy](../SECURITY.md) for private reporting instructions.

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