65 lines
3.0 KiB
Python
65 lines
3.0 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
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DATABASE_URL: str = os.getenv("TRAINING_DATABASE_URL",
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"postgresql+asyncpg://training_user:training_pass123@training-db:5432/training_db")
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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_STORAGE_PATH: str = os.getenv("MODEL_STORAGE_PATH", "/app/models")
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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_TRAINING_TIME_MINUTES: int = int(os.getenv("MAX_TRAINING_TIME_MINUTES", "30"))
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MAX_CONCURRENT_TRAINING_JOBS: int = int(os.getenv("MAX_CONCURRENT_TRAINING_JOBS", "3"))
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MIN_TRAINING_DATA_DAYS: int = int(os.getenv("MIN_TRAINING_DATA_DAYS", "30"))
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TRAINING_BATCH_SIZE: int = int(os.getenv("TRAINING_BATCH_SIZE", "1000"))
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# Prophet Specific Configuration
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PROPHET_SEASONALITY_MODE: str = os.getenv("PROPHET_SEASONALITY_MODE", "additive")
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PROPHET_CHANGEPOINT_PRIOR_SCALE: float = float(os.getenv("PROPHET_CHANGEPOINT_PRIOR_SCALE", "0.05"))
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PROPHET_SEASONALITY_PRIOR_SCALE: float = float(os.getenv("PROPHET_SEASONALITY_PRIOR_SCALE", "10.0"))
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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_SPANISH_HOLIDAYS: bool = True
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ENABLE_MADRID_HOLIDAYS: bool = True
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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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settings = TrainingSettings() |