GradientCanvas is a parallel-processing, auto-scaling render platform for real-time data visualization utilizing a distributed toolkit framework.
GradientCanvas is aimed at developers who need a straightforward, dependable solution.
Highlights
- GradientCanvas is a parallel-processing, auto-scaling
- render platform for real-time data
- visualization utilizing a distributed toolkit
- GradientCanvas is a parallel-processing, auto-scaling
- render platform for real-time data
- visualization utilizing a distributed toolkit
- python
- Modular architecture
- CI-ready (GitHub Actions)
- Clone the repository:
git clone https://github.com/fuad403273/GradientCanvas.git - Install required dependencies:
pip install -r requirements.txt
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
Pull requests and issue reports are both welcome. Please read the existing code style before submitting.
Released under the MIT License — see the LICENSE file.