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You like pytorch? You like micrograd? You love tinygrad! ❤️

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gtinygrad
Minimal tinygrad path tracing playground.

tinygrad repo: https://github.com/tinygrad/tinygrad
tinygrad README: https://github.com/tinygrad/tinygrad#readme

Quick start
python examples/raytrace_demo.py

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You like pytorch? You like micrograd? You love tinygrad! ❤️

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Python
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C
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gtinygrad is a minimal path tracing playground built upon the tinygrad framework. The repository provides a streamlined environment for experimenting with ray tracing algorithms, offering a focused and lightweight solution compared to larger, more complex frameworks. It serves as a demonstration project, showcasing the capabilities of tinygrad while facilitating exploration and learning for users interested in computational geometry and rendering techniques. The project’s core purpose is to offer a simple, accessible entry point for understanding and implementing path tracing, allowing users to quickly prototype and test their ideas. The project’s structure includes a readily executable `examples/raytrace_demo.py` script, enabling immediate experimentation without requiring significant setup. The repository’s documentation, accessible through the `README.md` file, provides guidance on utilizing the project and outlines the fundamental concepts of path tracing. Licensing is governed by the MIT license, granting users the freedom to utilize, modify, and distribute the code under these terms. The project employs a multi-language approach, utilizing Python for the primary execution and potentially incorporating other languages (such as C) for optimization during development. The codebase is structured to promote modularity and maintainability. Key elements within the repository include supporting documentation, example scripts, and the core code implementing the path tracing algorithm. Quantitative data regarding the codebase’s composition reveals that Python constitutes the majority of the code (62.2%), followed by C (27.1%). Minor contributions are made by Cuda (4.3%), Assembly (2.2%), Metal (1.9%), C++ (1.2%), and other languages, suggesting a potential strategy for incorporating specialized hardware acceleration. The project is designed for ease of use and experimentation, emphasizing a quick learning curve. It represents a concise and practical application of the tinygrad framework, offering a valuable resource for developers and researchers interested in path tracing concepts and implementation.