35 lines
1.6 KiB
Python
35 lines
1.6 KiB
Python
from enum import Enum
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class ExperimentStatus(str, Enum):
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"""
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Status values for experiment run lifecycle.
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This enum defines all possible status values that an experiment run can have
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throughout its lifecycle, from initialization through training, model saving,
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and cleanup. These statuses are used to track progress and identify failures
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in the training pipeline.
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The status values follow the naming convention from the original Mage pipeline
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to maintain compatibility with existing database records and monitoring systems.
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Attributes:
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ORCHESTRATOR_VALIDATION_ERROR: Error in the parameters validation.
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MAGE_WAITING_PROC: Initial status indicating experiment is registered and waiting for processing.
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TRAINING_SUCCESS: Training completed successfully with model and metrics calculated.
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TRAINING_ERROR: Training failed due to data issues, model errors, or other exceptions.
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MLFLOW_SENT: Model successfully saved to MLFlow.
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MLFLOW_SEND_ERROR: Model saving to MLFlow failed due to connection or serialization errors.
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FILE_DELETED: Cleanup completed successfully with all artifacts removed.
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FILE_DELETE_ERROR: Cleanup failed due to file system or MinIO errors.
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"""
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ORCHESTRATOR_VALIDATION_ERROR = 'ORCHESTRATOR_VALIDATION_ERROR'
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MAGE_WAITING_PROC = 'MAGE_WAITING_PROC'
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TRAINING_SUCCESS = 'TRAINING_SUCCESS'
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TRAINING_ERROR = 'TRAINING_ERROR'
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MLFLOW_SENT = 'MLFLOW_SENT'
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MLFLOW_SEND_ERROR = 'MLFLOW_SEND_ERROR'
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FILE_DELETED = 'FILE_DELETED'
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FILE_DELETE_ERROR = 'FILE_DELETE_ERROR'
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