NeuroOracle leverages deep learning algorithms for real-time data processing and intelligent auto-scaling within a robust system core.
The key benefits of using NeuroOracle include:
- NeuroOracle leverages deep learning algorithms
- for real-time data processing and
- intelligent auto-scaling within a robust
- NeuroOracle leverages deep learning algorithms
- for real-time data processing and
- intelligent auto-scaling within a robust
- python
- Standard library, minimal dependencies
- unittest/pytest
- Clone the repository:
git clone https://github.com/fuad403273/NeuroOracle.git - Install dependencies:
pip install -r requirements.txt - Run the test suite:
pytest
Runtime options can be set in the config file or overridden per call. The most commonly changed values are:
- timeout: how long operations may run before failing
- retries: how many times a failed operation is re-attempted
- cache: where temporary results are stored
Contributions are welcome. Open an issue for bugs or feature requests, or submit a pull request with your changes.
Released under the MIT License — see the LICENSE file.