https://github.com/engineering-research-and-development/synthetic-data-generator
https://github.com/engineering-research-and-development/synthetic-data-generator
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/engineering-research-and-development/synthetic-data-generator
- Owner: Engineering-Research-and-Development
- Created: 2024-11-14T15:10:50.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2026-02-04T09:05:07.000Z (4 months ago)
- Last Synced: 2026-02-04T20:15:40.357Z (4 months ago)
- Language: Python
- Size: 1.4 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ENGINEERING SYNTHETIC DATA GENERATOR 🌟
🤖 **What is it?**
The Synthetic Data Generator is a tool designed to generate artificial data that mimics the characteristics of real-world data. 📊
This tool is useful for testing, training, and validating machine learning models, as well as for generating data for demonstration or proof-of-concept purposes. 📈
## KEY FEATURES 🎉
* 🚀 **Scalable and extensible architecture**: Generate synthetic data that grows with your needs!
* 👥 **Easy interaction**: UX and UI designed for humans, not just machines! 🤖
* 🚀 **One-click deploy**: Get started in no time with our simple deployment process! 🚀
## GETTING STARTED 🚀
To run the Synthetic Data Generator on your machine, make sure that on your system is installed Docker 🐳 and Docker Compose.
🐳 **Deploy with Docker Compose**
```bash
docker-compose up -d
```
Once the containers are up and running, you can access the UI at http://localhost:5173 🌐
### CUSTOMIZING THE ENVIRONMENT 🎨
📝 Modify the Docker Compose file: Change environment variables to customize the environment to your needs! 🤔
## GETTING THE DOCUMENTATION 📚
The Synthetic Data Generator has three main components, aggregated as independent microservices:
- Frontend
- Middleware
- Generator.
Each container exposes OpenAPI documentation on the ports specified in the Docker Compose file. 📊
## HARDWARE REQUIREMENTS 💻
The entire ecosystem requires an 8-core CPU (Intel I7 11390H) and 16Gb of RAM, a Nvidia Mx450 (2Gb) and 1Tb of HDD to run smoothly, but this highly depends on the dataset complexity and the chosen model.
Bigger datasets may need more computational power as well as storage capability.
## CONTRIBUTING 🤝
👋 We welcome contributions! Fork the repository and submit a pull request. 💻