DEEP Classifier package

workflow

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config.
6. Update the components
7. Update the pipeline
8. Test run pipeline stage
9. run tox for testing your package
10. Update the dvc.yaml
11. run "dvc repro" for running all the stages in pipeline

OUTER STRUCTURE

dvc.yaml is used for orchestration i.e. to connect several pipelines. It can act as a substitute for main.py used in ML project.

params.yaml keeps all the parameters related to the project like batch size, epochs etc.

config.yaml keeps the project structure

STEP 1: Set the env variable | Get it from dagshub -> remote tab -> mlflow tab MLFLOW_TRACKING_URI=https://dagshub.com/AAKAAASSHHH24/Deep_CNN_classifier.mlflow MLFLOW_TRACKING_USERNAME=AAKAAASSHHH24 MLFLOW_TRACKING_PASSWORD=eace9117203f613afad0874c7cf8db27d285d1b3 python script.py

STEP 2: install mlflow

STEP 3: Set remote URI

STEP 4: Use context manager of mlflow to start run and then log metrics, params and model

DOCKER RUN COMMAND: docker build -t prediction .