ML_project_with_ML_Flow/main.py

from src.mlProject import logger
from src.mlProject.pipeline.stage_01_data_ingestion import DataIngestionTrainingPipeline
from src.mlProject.pipeline.stage_02_data_validation import DataValidationTrainingPipeline
from src.mlProject.pipeline.stage_03_data_transformation import DataTransformationTrainingPipeline
from src.mlProject.pipeline.stage_04_model_trainer import ModelTrainerTrainingPipeline
from src.mlProject.pipeline.stage_05_model_evaluation import ModelEvaluationTrainingPipeline


import dagshub
dagshub.init(repo_owner='kashishthakur26', repo_name='ML_project_with_ML_Flow', mlflow=True)
import mlflow

with mlflow.start_run():
  # Your training code here...
  mlflow.log_metric('accuracy', 42)
  mlflow.log_param('Param name', 'Value')

STAGE_NAME = "Data Ingestion stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = DataIngestionTrainingPipeline()
   data_ingestion.main()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e




STAGE_NAME = "Data Validation stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = DataValidationTrainingPipeline()
   data_ingestion.main()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e



STAGE_NAME = "Data Transformation stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = DataTransformationTrainingPipeline()
   data_ingestion.main()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e




STAGE_NAME = "Model Trainer stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = ModelTrainerTrainingPipeline()
   data_ingestion.main()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e



STAGE_NAME = "Model evaluation stage"
try:
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<") 
   data_ingestion = ModelEvaluationTrainingPipeline()
   data_ingestion.main()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
        logger.exception(e)
        raise e