README.md

Kidney_Disease_Classification

End to End Kidney Disease Classification model using MLFlow, DVC, DagsHub, AWS, Github Actions, Docker

Kidney-Disease-Classification-MLflow-DVC

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/Jkanishkha0305/Kidney_Disease_Classification

STEP 01 - Create a conda environment after opening the repository

conda create -n kidney python=3.8 -y

conda activate kidney

STEP 02 - install the requirements

pip install -r requirements.txt

STEP 03 - collect dataset

Dataset : Kaggel [CT KIDNEY DATASET: Normal-Cyst-Tumor and Stone]
Dataset Link : https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
Size : 1.66 GB
Files : 12.4k Files

Step 04 - prepare base model

Keras API link : https://keras.io/api/applications/
Model Chosen : VGG16
Model link : https://keras.io/api/applications/vgg/#vgg16-function
Image size : 244x244x3

# Finally run the following command
python main.py

# Finally run the following command
python app.py

Now,

open up you local host and port

MLflow

• Documentation

• MLflow tutorial

• MLflow Model Registry Docs

cmd

• mlflow ui

dagshub

dagshub

import dagshub
dagshub.init(repo_owner='Jkanishkha0305', repo_name='Kidney_Disease_Classification', mlflow=True)

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

MLFLOW_TRACKING_URI=https://dagshub.com/Jkanishkha0305/Kidney_Disease_Classification.mlflow
MLFLOW_TRACKING_USERNAME=entbappy \
MLFLOW_TRACKING_PASSWORD=6bd16d7f5ee713eba8329fb353c637dc8de93b55 \
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/Jkanishkha0305/Kidney_Disease_Classification.mlflow

export MLFLOW_TRACKING_USERNAME=Jkanishkha0305

export MLFLOW_TRACKING_PASSWORD=6bd16d7f5ee713eba8329fb353c637dc8de93b55

DVC cmd

DVC

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)

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 729852511714.dkr.ecr.us-east-1.amazonaws.com/kidney

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

settings>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

settings>secrets and variables>action>add new repository secret

(from the access key .csv that was downloaded)
AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = 729852511714.dkr.ecr.us-east-1.amazonaws.com

ECR_REPOSITORY_NAME = kidney