Taichi

Graphics programming for everyone

Productive

As a language embedded in Python, Taichi has a Python-style syntax which is extremely easy to learn. Research shows Taichi programs are 10x shorter compared to equivalent C++/CUDA code while achieving higher performance.

Portable

Without any code modification, a Taichi program can run on various platforms, including x64 & ARM CPUs, GPUs, web browsers and smartphones. Taichi supports Windows, Linux, and OS X.

Performant

Taichi's Just-In-Time compiler offloads compute-intensive tasks to multi-core CPUs and massively parallel GPUs. The Taichi language design allows effective performance optimizations by the Taichi compiler.

REMINDER beta

This docsite is still under construction 🚧 and the content is subject to change. For detailed instructions, especially the API docs of Taichi and the Chinese version of the Taichi documentation, please visit our old documentation site on Read the Docs (opens new window) and 中文文档 (opens new window)

# Hello, Taichi!

Taichi can be easily installed via pip:

python3 -m pip install taichi

(Please make sure you are using 64-bit Python 3.6/3.7/3.8.)

Download fractal.py (opens new window) and run it with

python3 fractal.py

You will see the animation below:

$fractal$

# fractal.py
import taichi as ti

ti.init(arch=ti.gpu)

n = 320
pixels = ti.field(dtype=float, shape=(n * 2, n))

@ti.func
def complex_sqr(z):
    return ti.Vector([z[0]**2 - z[1]**2, z[1] * z[0] * 2])

@ti.kernel
def paint(t: float):
    for i, j in pixels: # Automatically parallelized
        c = ti.Vector([-0.8, ti.cos(t) * 0.2])
        z = ti.Vector([i / n - 1, j / n - 0.5]) * 2
        iterations = 0
        while z.norm() < 20 and iterations < 50:
            z = complex_sqr(z) + c
            iterations += 1
        pixels[i, j] = 1 - iterations * 0.02

gui = ti.GUI("Julia Set", res=(n * 2, n))

for i in range(1000000):
    paint(i * 0.03)
    gui.set_image(pixels)
    gui.show()