Frequently Asked Questions

Can I enable auto compeletion for Taichi?

Yes, Taichi's Python user-facing APIs should work natively with any language server for Python.

Take VSCode as an example, you can install Python or Pylance extensions to get language support like signature help with type information, code completion etc.

If it doesn't work out of box after installing the extension, please make sure the right Python interpreter is selected by:

• invoke command palette (Shift + Command + P (Mac) / Ctrl + Shift + P (Windows/Linux))
• find Python: Select Interpreter
• make sure you select the path to the Python interpreter you're using with a taichi package installed

Why does my pip complain package not found when installing Taichi?

You may have a Python interpreter with an unsupported version. Currently, Taichi only supports Python 3.7/3.8/3.9/3.10 (64-bit) . For more information about installation-specific issues, please check Installation Troubleshooting.

Does Taichi provide built-in constants such as ti.pi?

There is no built-in constant such as pi. We recommended using math.pi directly.

Outer-most loops in Taichi kernels are by default parallel. How can I serialize one of them?

A solution is to add an additional ghost loop with only one iteration outside the loop you want to serialize.

for _ in range(1):  # This "ghost" loop will be "parallelized", but with only one thread. Therefore, the containing loop below is serialized.
    for i in range(100):  # The loop you want to serialize
        ...

What is the most convenient way to load images into Taichi fields?

One feasible solution is field.from_numpy(ti.tools.imread('filename.png')).

Can Taichi interact with other Python packages such as matplotlib?

Yes, Taichi supports many popular Python packages. Taichi provides helper functions such as from_numpy and to_numpy to transfer data between Taichi fields and NumPy arrays, so that you can also use your favorite Python packages (e.g., numpy, pytorch, matplotlib) together with Taichi as below:

import taichi as ti
pixels = ti.field(ti.f32, (1024, 512))
import numpy as np
arr = np.random.rand(1024, 512)
pixels.from_numpy(arr)   # load numpy data into taichi fields
import matplotlib.pyplot as plt
arr = pixels.to_numpy()  # store taichi data into numpy arrays
plt.imshow(arr)
plt.show()
import matplotlib.cm as cm
cmap = cm.get_cmap('magma')
gui = ti.GUI('Color map')
while gui.running:
    render_pixels()
    arr = pixels.to_numpy()
    gui.set_image(cmap(arr))
    gui.show()

Besides, you can also pass numpy arrays or torch tensors into a Taichi kernel as arguments. See Interacting with external arrays for more details.

How do I declare a field with a dynamic length?

The dynamic SNode supports variable-length fields. It acts similarly to std::vector in C++ or list in Python.

tip

An alternative solution is to allocate a large enough dense field, with a corresponding 0-D field field_len[None] tracking its length. In practice, programs allocating memory using dynamic SNodes may be less efficient than using dense SNodes, due to dynamic data structure maintenance overheads.

How do I program on less structured data structures (such as graphs and tetrahedral meshes) in Taichi?

These structures have to be decomposed into 1D Taichi fields. For example, when representing a graph, you can allocate two fields, one for the vertices and the other for the edges. You can then traverse the elements using for v in vertices or for v in range(n).

How to install Taichi on a server without Internet access?

Follow these steps to install Taichi on a server without Internet access.

1. From a computer with Internet access, pip download Taichi, ensuring that this computer has the same operating system as the target server:

pip download taichi

This command downloads the wheel package of Taichi and all its dependencies.

2. Copy the downloaded .whl packages to your local server and install each with the following command. Note that you must* complete all dependency installation before installing Taichi.

python -m pip install xxxx.whl

Can I integrate Taichi and Houdini?

The answer is an unequivocal Yes! Our contributors managed to embed taichi_elements, a multi-material continuum physics engine, into Houdini as an extension, combining Houdini's flexibility in preprocessing with Taichi's strength in high-performance computation.

You can follow the instructions provided here.

How do I accurately initialize a vector or matrix with f64 precision when my default floating-point precision (default_fp) is f32?

To better understand the question, look at the program below:

import taichi as ti

ti.init()

@ti.kernel
def foo():
    A = ti.Vector([0.2, 0.0], ti.f64)
    print('A =', A)

    B = ti.Vector([ti.f64(0.2), 0.0], ti.f64)
    print('B =', B)

foo()

You get the following output:

A = [0.200000002980, 0.000000000000]
B = [0.200000000000, 0.000000000000]

You may notice the value of A is slightly different from [0.2, 0]. This is because, by default, your float literals are converted to ti.f32, and 0.2 in ti.f32 precision becomes 0.200000002980. If you expect A and B to have ti.f64 precision, use ti.f64(0.2) to preserve more effective digits here so that 0.2 keeps its ti.f64 type.

Alternatively, if you can afford having all floating-point operations in f64 precision, you can directly initialize Taichi with ti.init(..., default_fp=ti.f64).

Why does it always return an error when I pass a list from the Python scope to a Taichi kernel?

A Taichi kernel cannot take a Python list directly. You need to use NumPy arrays as a bridge.

For example, the following code snippet does not work:

import taichi as ti
import numpy as np
ti.init()
x = ti.field(ti.i32, shape=3)
array = [10, 20, 30]

@ti.kernel
def test(arr: list):
    for i in range(3):
        x[i] = arr[i]
test(array)

You need to import NumPy:

import taichi as ti
import numpy as np
ti.init(arch=ti.cpu)
x = ti.field(ti.i32, shape=3)
array = np.array([10, 20, 30])
@ti.kernel
def test(arr: ti.types.ndarray()):
    for i in range(3):
        x[i] = arr[i]
test(array)