Kidney-Disease-Classification-MLflow-DVC

Demo

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
10. Update the app.py

How to run?

STEPS:

Clone the repository

https://github.com/GauravPahwa2021/Kidney_Disease_Prediction

STEP 01- Create a conda environment after opening the repository

conda create -n cnncls python=3.8 -y

conda activate cnncls

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

MLflow

• Documentation

• MLflow tutorial

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/GauravPahwa2021/Kidney_Disease_Prediction.mlflow
MLFLOW_TRACKING_USERNAME=GauravPahwa2021
MLFLOW_TRACKING_PASSWORD=34255e2baf836bf6327d7bb761b9ef93d83d0201
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/GauravPahwa2021/Kidney_Disease_Prediction.mlflow

export MLFLOW_TRACKING_USERNAME=GauravPahwa2021

export MLFLOW_TRACKING_PASSWORD=34255e2baf836bf6327d7bb761b9ef93d83d0201

DVC cmd

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

About MLflow & DVC

MLflow

• Its Production Grade
• Trace all of your expriements
• Logging & taging your model

DVC

• Its very lite weight for POC only
• lite weight expriements tracker
• It can perform Orchestration (Creating Pipelines)