78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
# ================================================================
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# TRAINING SERVICE CONFIGURATION
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# services/training/app/core/config.py
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# ================================================================
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"""
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Training service configuration
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ML model training and management
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"""
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from shared.config.base import BaseServiceSettings
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import os
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class TrainingSettings(BaseServiceSettings):
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"""Training service specific settings"""
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# Service Identity
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APP_NAME: str = "Training Service"
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SERVICE_NAME: str = "training-service"
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DESCRIPTION: str = "Machine learning model training service"
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# Database configuration (secure approach - build from components)
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@property
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def DATABASE_URL(self) -> str:
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"""Build database URL from secure components"""
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# Try complete URL first (for backward compatibility)
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complete_url = os.getenv("TRAINING_DATABASE_URL")
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if complete_url:
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return complete_url
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# Build from components (secure approach)
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user = os.getenv("TRAINING_DB_USER", "training_user")
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password = os.getenv("TRAINING_DB_PASSWORD", "training_pass123")
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host = os.getenv("TRAINING_DB_HOST", "localhost")
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port = os.getenv("TRAINING_DB_PORT", "5432")
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name = os.getenv("TRAINING_DB_NAME", "training_db")
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return f"postgresql+asyncpg://{user}:{password}@{host}:{port}/{name}"
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# Redis Database (dedicated for training cache)
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REDIS_DB: int = 1
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# ML Model Storage
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MODEL_BACKUP_ENABLED: bool = os.getenv("MODEL_BACKUP_ENABLED", "true").lower() == "true"
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MODEL_VERSIONING_ENABLED: bool = os.getenv("MODEL_VERSIONING_ENABLED", "true").lower() == "true"
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# Training Configuration
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MAX_CONCURRENT_TRAINING_JOBS: int = int(os.getenv("MAX_CONCURRENT_TRAINING_JOBS", "3"))
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# Prophet Specific Configuration
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PROPHET_HOLIDAYS_PRIOR_SCALE: float = float(os.getenv("PROPHET_HOLIDAYS_PRIOR_SCALE", "10.0"))
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# Spanish Holiday Integration
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ENABLE_CUSTOM_HOLIDAYS: bool = os.getenv("ENABLE_CUSTOM_HOLIDAYS", "true").lower() == "true"
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# Data Processing
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DATA_PREPROCESSING_ENABLED: bool = True
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OUTLIER_DETECTION_ENABLED: bool = os.getenv("OUTLIER_DETECTION_ENABLED", "true").lower() == "true"
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SEASONAL_DECOMPOSITION_ENABLED: bool = os.getenv("SEASONAL_DECOMPOSITION_ENABLED", "true").lower() == "true"
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# Model Validation
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CROSS_VALIDATION_ENABLED: bool = os.getenv("CROSS_VALIDATION_ENABLED", "true").lower() == "true"
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VALIDATION_SPLIT_RATIO: float = float(os.getenv("VALIDATION_SPLIT_RATIO", "0.2"))
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MIN_MODEL_ACCURACY: float = float(os.getenv("MIN_MODEL_ACCURACY", "0.7"))
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# Distributed Training (for future scaling)
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DISTRIBUTED_TRAINING_ENABLED: bool = os.getenv("DISTRIBUTED_TRAINING_ENABLED", "false").lower() == "true"
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TRAINING_WORKER_COUNT: int = int(os.getenv("TRAINING_WORKER_COUNT", "1"))
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PROPHET_DAILY_SEASONALITY: bool = True
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PROPHET_WEEKLY_SEASONALITY: bool = True
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PROPHET_YEARLY_SEASONALITY: bool = True
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# Throttling settings for parallel training to prevent heartbeat blocking
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MAX_CONCURRENT_TRAININGS: int = int(os.getenv("MAX_CONCURRENT_TRAININGS", "3"))
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settings = TrainingSettings()
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