tf_pipeline.py

from adr.pipeline.phase_001_tf_data_ingestion import DataTFIngestionPipeline
from adr.pipeline.phase_002_tf_model_training import ModelTrainingPipeline
from adr.pipeline.phase_003_model_evaluation import ModelEvaluationPipeline
from adr.pipeline.phase_004_model_inference import ModelInferencePipeline
from adr import logger

PHASE_ID = "Data Ingestion" 
try:
   logger.info(f">>>>>> phase {PHASE_ID} started <<<<<<")
   obj = DataTFIngestionPipeline()
   obj.main()
   logger.info(f">>>>>> phase {PHASE_ID} done successfully <<<<<<\n\nx==========x")
except Exception as e:
   logger.exception(e)
   raise e


PHASE_ID = "Model Training" 
try:
   logger.info(f">>>>>> phase {PHASE_ID} started <<<<<<")
   obj = ModelTrainingPipeline()
   obj.main()
   logger.info(f">>>>>> phase {PHASE_ID} done successfully <<<<<<\n\nx==========x")
except Exception as e:
   logger.exception(e)
   raise e

PHASE_ID = "Model Evaluation" 
try:
   logger.info(f">>>>>> phase {PHASE_ID} started <<<<<<")
   obj = ModelEvaluationPipeline()
   obj.main()
   logger.info(f">>>>>> phase {PHASE_ID} done successfully <<<<<<\n\nx==========x")
except Exception as e:
   logger.exception(e)
   raise e

PHASE_ID = "Model Inference" 
try:
   logger.info(f">>>>>> phase {PHASE_ID} started <<<<<<")
   obj = ModelInferencePipeline()
   obj.main()
   logger.info(f">>>>>> phase {PHASE_ID} done successfully <<<<<<\n\nx==========x")
except Exception as e:
   logger.exception(e)
   raise e