Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/saritaphd/kidney-disease-classification-deep-learning-project-with-deployment
Kidney-disease-classification-deep-learning-project-with-deployment
https://github.com/saritaphd/kidney-disease-classification-deep-learning-project-with-deployment
aws deeplearning deployment python
Last synced: 3 days ago
JSON representation
Kidney-disease-classification-deep-learning-project-with-deployment
- Host: GitHub
- URL: https://github.com/saritaphd/kidney-disease-classification-deep-learning-project-with-deployment
- Owner: SaritaPhD
- License: mit
- Created: 2023-10-17T08:45:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-30T13:56:53.000Z (about 1 year ago)
- Last Synced: 2023-10-30T14:40:13.499Z (about 1 year ago)
- Topics: aws, deeplearning, deployment, python
- Language: Python
- Homepage:
- Size: 126 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Kidney-Disease-Classification-MLflow-DVC
The Kidney-Disease-Classification project aims to develop a binary classification model to distinguish between normal kidneys and kidneys with tumors using image data. Convolutional Neural Networks (CNN) are employed to analyze medical images for the classification task. MLflow is integrated for tracking experiments and model performance, while DVC (Data Version Control) manages dataset versioning to ensure reproducibility and scalability.
## 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
### STEP 01- Create a conda environment after opening the repository
```bash
conda create -n cnncls python=3.8 -y
```
```bash
conda activate cnncls
```
### STEP 02- install the requirements
```bash
pip install -r requirements.txt
```
```bash
# Finally run the following command
python app.py
```
Now,
```bash
open up you local host and port
```
## MLflow
- [Documentation](https://mlflow.org/docs/latest/index.html)
- [MLflow tutorial](https://youtu.be/qdcHHrsXA48?si=bD5vDS60akNphkem)
##### cmd
- mlflow ui
### dagshub
[dagshub](https://dagshub.com/)
MLFLOW_TRACKING_URI= \
MLFLOW_TRACKING_USERNAME= \
MLFLOW_TRACKING_PASSWORD=\
python script.py
Run this to export as env variables:
```bash
export MLFLOW_TRACKING_URI=
export MLFLOW_TRACKING_USERNAME=
export MLFLOW_TRACKING_PASSWORD=
```
### 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)
# 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: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken
## 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-1
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = simple-app