https://github.com/atrkhomeini/oncoscan

OncoScan merupakan Machine Learning yang ingin kami develop dengan fungsi untuk dapat mengklasifikasikan kanker berdasarkan gambar CT Scan. Tujuannya adalah kami ingin agar pengguna dapat mengetahui apakah gambar CT Scan yang dimilikinya benar merupakan kanker atau bukan.
https://github.com/atrkhomeini/oncoscan

Last synced: about 1 month ago
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OncoScan merupakan Machine Learning yang ingin kami develop dengan fungsi untuk dapat mengklasifikasikan kanker berdasarkan gambar CT Scan. Tujuannya adalah kami ingin agar pengguna dapat mengetahui apakah gambar CT Scan yang dimilikinya benar merupakan kanker atau bukan.

• Host: GitHub
• URL: https://github.com/atrkhomeini/oncoscan
• Owner: atrkhomeini
• Created: 2024-06-12T15:34:02.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2024-07-10T03:45:50.000Z (7 months ago)
• Last Synced: 2024-11-09T11:06:05.724Z (3 months ago)
• Language: Python
• Homepage:
• Size: 51.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# OncoScan - Chest Cancer Classification

## 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

## MLflow

- [Documentation](https://mlflow.org/docs/latest/index.html)

##### cmd

- mlflow ui

### dagshub

[dagshub](https://dagshub.com/)

```bash

import dagshub

dagshub.init(repo_owner='atrkhomeini', repo_name='chest-cancer-classification', 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 experiements

 - Logging & taging 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 docker image in aws

	#Description: About the deployment

	1. Build docker image of the source code

	2. Push docker image to ECR

	3. Launch EC2 

	4. Pull image from ECR in EC2

	5. Lauch docker image in EC2

	#Policy:

	1. AmazonEC2ContainerRegistryFullAccess

	2. AmazonEC2FullAccess

	

## 3. Create ECR repo to store/save docker image

    - Save the URI: 891377126928.dkr.ecr.ap-southeast-2.amazonaws.com/cancer

	

## 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 = ap-southeast-2

    AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com

    ECR_REPOSITORY_NAME = simple-app

## Screenshots

![App Screenshot](https://github.com/atrkhomeini/OncoScan/blob/master/image.png)