README.md

metadata

title: Image To Text App
emoji: 📹
colorFrom: blue
colorTo: red
sdk: streamlit
app_file: app.py
pinned: false

Kidney-disease-classification-mlops

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. app.py

How to run?

STEPS:

Clone the repository

https://github.com/HAKIM-ML/
Kidney-disease-classification-mlops

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

cmd

• mlflow ui

dagshub

dagshub MLFLOW_TRACKING_URI = https://dagshub.com/azizulhakim8291/Kidney-disease-classification-mlops.mlflow

python script.py

import dagshub dagshub.init(repo_owner='azizulhakim8291', repo_name='Kidney-disease-classification-mlops', mlflow=True)

import mlflow with mlflow.start_run(): mlflow.log_param('parameter name', 'value') mlflow.log_metric('metric name', 1)

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)