Kidney-Disease-Classification-MLflow-DVC

Workflows

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml (paramaters used throughout the project: for model training)
4. Update the entity
5. Update the configuration manager in src config
6. Update the components (data ingestion, validation, transformation, etc.)
7. Update the pipeline
8. Update the main.py
9. Update the dvc.yaml
10. Update the app.py (UI related functionality)

How to run?

STEPS:

Clone the repository

https://github.com/miguelhermar/Kidney-Disease-Classification-MLflow-DVC

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/miguelangel.hermar410/Kidney-Disease-Classification-MLflow-DVC.mlflow
MLFLOW_TRACKING_USERNAME=miguelangel.hermar410
MLFLOW_TRACKING_PASSWORD=838058e049ff14335ccc9b935c026e41b1001293
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/miguelangel.hermar410/Kidney-Disease-Classification-MLflow-DVC.mlflow

export MLFLOW_TRACKING_USERNAME=miguelangel.hermar410 

export MLFLOW_TRACKING_PASSWORD=838058e049ff14335ccc9b935c026e41b1001293

DVC cmd

1. dvc init (initializes dvc directory and .dvcignore file)
2. dvc repro (generates dvc.lock file which tracks everything)
3. dvc dag (displays a graph of the relationships between the steps in the pipeline)

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: 714501908979.dkr.ecr.us-east-2.amazonaws.com/kidney-disease

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:

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

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-2

AWS_ECR_LOGIN_URI = 714501908979.dkr.ecr.us-east-2.amazonaws.com

ECR_REPOSITORY_NAME = kidney-disease