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此仓库是为了提升国内下载速度的镜像仓库,每日同步一次。 原始仓库: https://github.com/taichi-dev/taichi
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README.md

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Overview

Taichi (太极) is a programming language designed for high-performance computer graphics. It is deeply embedded in Python, and its just-in-time compiler offloads compute-intensive tasks to multi-core CPUs and massively parallel GPUs.

Advanced features of Taichi include spatially sparse computing and differentiable programming [examples].

Please check out our SIGGRAPH 2020 course on Taichi basics: YouTube, Bilibili, slides (pdf).

中文视频教程: [哔哩哔哩], [幻灯片]

Examples (More...)

Installation Downloads

python3 -m pip install taichi

Supported OS: Windows, Linux, Mac OS X; Python: 3.6/3.7/3.8 (64-bit only); Backends: x64 CPUs, CUDA, Apple Metal, OpenGL Compute Shaders.

Please build from source for other configurations (e.g., your CPU is ARM, or you want to try out our experimental C backend).

Note:

Linux (CUDA) OS X (10.14+) Windows Documentation
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Links


  • Taichi THREE: A 3D rendering library based on Taichi.
  • Taichi GLSL: A Taichi extension library that provides a set of GLSL-style helper functions.
  • Taichi Elements: A high-performance multi-material continuum physics engine based on Taichi (work in progress).
  • Taichi Blend: Taichi Blender intergration for physics-based animations (work in progress)
  • Taichi.js: Run compiled Taichi programs in Javascript and WASM (work in progress).

Developers

The Taichi project was created by Yuanming Hu (yuanming-hu). Significant contributions are made by:

Kenneth Lozes (KLozes) and Yu Fang (squarefk) have also made notable contributions.

[List of all contributors to Taichi]


The Simplified Chinese documentation (简体中文文档) was created by Ark (StephenArk30). Significant contributions are made by:

[List of all contributors to the Simplified Chinese documentation of Taichi]


We welcome feedback and comments. If you would like to contribute to Taichi, please check out our Contributor Guidelines.

If you use Taichi in your research, please cite our papers:

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Taichi 是一个计算机图形库,使用 C++ 编写而成,并且 Python 封装友好,提供了一个 Python 3 绑定 spread retract
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