https://github.com/danishzulfiqar/micro-service-predictive-maintenance
Predictive maintenance backend server for model inferance, upload and update.
https://github.com/danishzulfiqar/micro-service-predictive-maintenance
aws-ec2 container deployment docker fastapi inference keras-tensorflow machine-learning microservice numpy nural-network pandas predictive-maintenance python shap tensorflow
Last synced: 4 months ago
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Predictive maintenance backend server for model inferance, upload and update.
- Host: GitHub
- URL: https://github.com/danishzulfiqar/micro-service-predictive-maintenance
- Owner: danishzulfiqar
- Created: 2025-03-01T18:23:14.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-02T00:44:42.000Z (4 months ago)
- Last Synced: 2025-03-02T01:28:37.229Z (4 months ago)
- Topics: aws-ec2, container, deployment, docker, fastapi, inference, keras-tensorflow, machine-learning, microservice, numpy, nural-network, pandas, predictive-maintenance, python, shap, tensorflow
- Language: Python
- Homepage:
- Size: 644 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predictive Maintenance
### Technical Details
Predictive maintenance backend server for model inferance, upload and update.
### Techstack
- Tenserflow
- Fast-API
- Docker
- Shap
- Keras
- Numpy
- Pandas
## Running the code
### Python version: Python 3.10.12 to 3.10.14
### Method 1: Docker Container
1. Clone the repository
```bash
git clone https://github.com/danishzulfiqar/Micro-Service-Predictive-Maintenance.git
```
2. Change directory
```bash
cd Micro-Service-Predictive-Maintenance
```
3. Make image
```bash
docker build -t api .
```
4. Run image in a container
```bash
docker run --name pred-men -p 8000:8000 api
```
5. Open App at localhost:
http://localhost:8000
### Method 2: venv
1. Clone the repository
```bash
git clone https://github.com/danishzulfiqar/Micro-Service-Predictive-Maintenance.git
```
2. Change directory
```bash
cd Micro-Service-Predictive-Maintenance
```
3. Make env
```bash
python -m venv .venv
```
4. Select env
```bash
source .venv/bin/activate
```
5. Install dependencies
```bash
pip install -r requirements.txt --no-cache-dir
```
6. Run Fast-API
```bash
fastapi run app/main.py --port 8000 --reload
```
6. Open App at localhost:
http://localhost:8000
### Deployment
## AWS EC2
### Recommended: ubunto t2.small or greater machines
Note:
1. The application is optimised and runs with ubunto (t2.micro) as well but for higher traffic and optimised response use recommended machines.
2. Change tensorflow to tensorflow-cpu in requiremnts.text for deployment.
## Running Server
### 1. Without Docker
1. Make .venv
```bash
python -m venv .venv
```
2. Move to venv
```bash
source .venv/bin/activate
```
3. Install requirements
```bash
pip install -r requirements.txt --no-cache-dir
```
4. Run app
```bash
nohup fastapi run app/main.py --port 8000
```
### 2. With Docker
1. Build image
```bash
docker build -t api .
```
2. Run image
```bash
docker run --name pred-men -p 8000:8000 api
```
## Microservice Usage
### Documentation and Routes
1. Open the browser and paste the link below
```bash
http://localhost:8000/docs
```
### Note:
For development of models compatible with this microservice, please refer: [Predictive-Maintenance-Model-Training](https://github.com/danishzulfiqar/Predictive-Maintenance-Model-Training.git)