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Documentation | 简体中文文档

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].

Gallery

Installation

python3 -m pip install taichi

Supported OS: Windows, Linux, Mac OS X; Python: 3.6, 3.7, 3.8; Backends: x64 CPUs, CUDA, Apple Metal, OpenGL Compute Shaders.

Please build from source for other configurations (e.g., your CPU is ARM).

Note:

• Starting April 13 2020 (v0.5.12), we release the Python package taichi instead of taichi-nightly. Now this PyPI package includes CPU, CUDA 10, Metal and OpenGL support.
• On Ubuntu 19.04+, please sudo apt install libtinfo5.
• On Windows, please install Microsoft Visual C++ Redistributable if you haven't.
• [All releases] [Change log]

	Linux (CUDA)	OS X (10.14+)	Windows	Documentation
Build
PyPI

Applications

• A High-Performance Multi-Material Continuum Physics Engine based on Taichi: Taichi Elements (work in progress)

Developers

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

• Ye Kuang (k-ye) (Apple Metal backend)
• 彭于斌 (archibate) (OpenGL Compute Shader backend)
• Mingkuan Xu (xumingkuan) (IR optimization & standardization)

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:

• 彭于斌 (archibate)
• Danni Li (isdanni)
• Chengchen Wang (rexwangcc)
• 万健洲 (ArkhamWJZ)

[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:

• (SIGGRAPH Asia 2019) Taichi: High-Performance Computation on Sparse Data Structures [Video] [BibTex] [Code]
• (ICLR 2020) DiffTaichi: Differentiable Programming for Physical Simulation [Video] [BibTex] [Code]