End-to-End-Cancer-Classification-using-MLflow-DVC/README.md

End-to-End-Cancer-Classification-using-MLflow-DVC

step 1: create a virtual environment

conda create -p venv python=3.8 -y

step 2: install requirements

pip install -r requirements.txt

Step 3:

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/tproma/End-to-End-Cancer-Classification-using-MLflow-DVC.mlflow MLFLOW_TRACKING_USERNAME=tproma MLFLOW_TRACKING_PASSWORD=7b4574af852ac4a1b1b6350935ca23e65d699d65 python script.py

export MLFLOW_TRACKING_URI=https://dagshub.com/tproma/End-to-End-Cancer-Classification-using-MLflow-DVC.mlflow
export MLFLOW_TRACKING_USERNAME=tproma
export MLFLOW_TRACKING_PASSWORD=7b4574af852ac4a1b1b6350935ca23e65d699d65

Workflows

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. Update the main.py
9. Update the dvc.yaml

Kaggle Dataset

https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images

Data link

link

cmd

• mlflow ui

DVC cmd

1. dvc init
2. dvc repro
3. dvc dag