ModelElohim

ModelElohim is a distributed neural network engine for real-time, auto-scalable machine learning computations on parallelized, data-intensive platforms.

With ModelElohim, you get a lightweight tool that stays out of your way.

What you get

• ModelElohim is a distributed neural
• network engine for real-time, auto-scalable
• machine learning computations on parallelized,

• ModelElohim is a distributed neural
• network engine for real-time, auto-scalable
• machine learning computations on parallelized,

• javascript
• javascript framework (Flask/Django/FastAPI or equivalent)
• Pytest for testing

1. Clone the repository: git clone https://github.com/yhonaach/ModelElohim.git
2. Install dependencies: pip install -r requirements.txt
3. Run the test suite: pytest

ModelElohim is configured through environment variables (see .env.example). Key options:

• APP_ENV: development or production.
• PORT: Port the server listens on.
• LOG_LEVEL: debug, info, or error.

Pull requests and issue reports are both welcome. Please read the existing code style before submitting.

This project is licensed under the MIT License. See the LICENSE file for details.