MartinKalema/Kidney-Disease-Classification-MLflow-DVC

End to End Kidney Disease Classification

End-to-End-Kidney-Disease-Classification-Using-MLflow-DVC

MLflow for experiment tracking and DVC for ML Pipeline tracking, Data versioning and experiment tracking too. I'm using mlflow for experiment tracking.

Tasks

• Project template creation
• Project setup and requirements installation
• Logging, Utils and exception module
• Project Workflows
• All components notebook experiment
• All components modular code implementation
• training pipeline
• MLflow (MLOps tool for experiments tracking and model registration)
• DVC (MLOps tool for pipeline tracking and implementation)
• Prediction pipeline and user app creation
• Docker
• Final CI/CD Deployment on AWS

Workflows

• Update config.yaml
• Update secrets.yaml [Optional]
• Update params.yaml
• Update the entity
• Update the configuration manager in src config
• Update the components
• Update the pipeline
• Update the main.py
• Upddate the dvc.yaml
• Update app.py

How to install

Clone the repository

git clone https://github.com/MartinKalema/Kidney-Disease-Classification-MLflow-DVC.git

Create a conda environment after opening the repository and activate it

conda create -n kidney python=3.8 -y

conda activate kidney

Install the requirements

pip install -r requirements.txt

This Project is connected to Dagshub so all my experiments are sent to dagshub and can be viewed on dagshub itself or on the mlflow platform integrated there. MLflow is a production grade experiments tracker for managing end-to-end machine learning lifecycle. It helps with experiments tracking, packaging code into reproducible runs and sharing and deploying models.

View experiments locally.

Do not set the tracking uri using,

mlflow.set_tracking_uri()

All experiments will be stored inside an auto generated folder called mlruns. Use the command below to view them in the mlflow web interface

mlflow ui

For remote views & collaboration.

Connect your github account to DagsHub @ https://dagshub.com

Whenever you close your editor and open it again, the mlflow uri configs are erased and you must run the commands below in your bash terminal again, otherwise the experiments will be saved on your local machine in a folder called mlruns

export MLFLOW_TRACKING_URI=https://dagshub.com/kalema3502/Kidney-Disease-Classification-MLflow-DVC.mlflow

export MLFLOW_TRACKING_USERNAME=kalema3502

export MLFLOW_TRACKING_PASSWORD=fb3845efcc3b2e46a4157b1d2c977a21e02dd16e

DVC setup

DVC (Data Version Control) is a light weight experiments tracker and it can perform orchestration (creating pipelines)

dvc init

Add the project pipelines to the dvc.yaml file, then run the command below.

dvc repro

To the pipeline structure, use the command below

dvc dag

AWS CI/CD Deployment with Github Actions

• Login to the AWS console

• Create an IAM user for deployment

• The user should have EC2 & ECR access. The deployment steps are mentioned below,

  1. Build the docker image of the source code
  2. Push your docker image to the ECR
  3. Launch your EC2
  4. Pull your image from the ECR to the EC2
  5. Launch your docker image in the EC2

• Policy

  1. Amazon EC2 Container Registry Access
  2. Amazon EC2 Full Access

• Create EC2 repo to save docker image

  save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken
  

• Create Ubuntu EC2 VM

• Install Docker on the EC2 Machine

      #optional
  
      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
  

• Configure EC2 as self-hosted runner

  setting > actions > runner > new self hosted runner > choose os >
  

• Setup github secrets

      AWS_ACCESS_KEY_ID=
  
      AWS_SECRET_ACCESS_KEY=
  
      AWS_REGION = us-east-1
  
      AWS_ECR_LOGIN_URI = demo >>  566373416292.dkr.ecr.ap-south-1.amazonaws.com
  
      ECR_REPOSITORY_NAME = kidney