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Version: v1.0.3

Installation Troubleshooting

Linux issues​

  • If Taichi crashes and reports /usr/lib/ version `CXXABI_1.3.11' not found:

    You might be using Ubuntu 16.04. Please try the solution in this thread:

    sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y
    sudo apt-get update
    sudo apt-get install libstdc++6

Windows issues​

Python issues​

  • If pip could not find a satisfying package, i.e.,

    ERROR: Could not find a version that satisfies the requirement taichi (from versions: none)
    ERROR: No matching distribution found for taichi
    • Make sure you're using Python version 3.6/3.7/3.8/3.9/3.10:

      python3 -c "print(__import__('sys').version[:3])"
      # 3.6, 3.7, 3.8, 3.9, or 3.10
    • Make sure your Python executable is 64-bit:

      python3 -c "print(__import__('platform').architecture()[0])"
      # 64bit

CUDA issues​

  • If Taichi crashes with the following errors:

    [Taichi] mode=release
    [Taichi] version 0.6.0, supported archs: [cpu, cuda, opengl], commit 14094f25, python 3.8.2
    [W 05/14/20 10:46:49.549] [cuda_driver.h:call_with_warning@60] CUDA Error CUDA_ERROR_INVALID_DEVICE: invalid device ordinal while calling mem_advise (cuMemAdvise)
    [E 05/14/20 10:46:49.911] Received signal 7 (Bus error)

    This might be because that your NVIDIA GPU is pre-Pascal, and it has limited support for Unified Memory.

    • Possible solution: add export TI_USE_UNIFIED_MEMORY=0 to your ~/.bashrc. This disables unified memory usage in the CUDA backend.
  • If Taichi exits with message "Out of CUDA pre-allocated memory", e.g.,

    import taichi as ti


    x = ti.field(dtype=ti.i16)

    ti.root.pointer(ti.i, 1024).dense(ti.i, 1024 * 1024).place(x)
    # A sparse array. Each dense block is 2MB in size.

    # Populate 1024 * 2MB = 2GB memory
    def populate():
    for k in range(1024):
    x[k * 1024 * 1024] = 1


    ... may give you ...

    [Taichi] Starting on arch=cuda
    Taichi JIT:0: allocate_from_buffer: block: [0,0,0], thread: [0,0,0] Assertion `Out of CUDA pre-allocated memory.
    Consider using ti.init(device_memory_fraction=0.9) or ti.init(device_memory_GB=4) to allocate more GPU memory` failed.

    This usually happens when you are using sparse data structures that need dynamic GPU memory allocation. On platforms without CUDA unified memory support (e.g., Windows), Taichi only pre-allocates 1 GB of GPU memory for dynamically allocated data structures. To fix this, simply pre-allocate more GPU memory:

    1. Set ti.init(..., device_memory_fraction=0.9) to allocate 90% of GPU memory. Replace "90%" with any other fraction depending on your hardware.
    2. Set ti.init(..., device_memory_GB=4) to allocate 4 GB GPU memory. Feel free to use any number bigger than 1.
    3. Setting environment variables TI_DEVICE_MEMORY_FRACTION=0.9 and TI_DEVICE_MEMORY_GB=4 would also work.

    Note that on Linux, Taichi automatically grows the memory pool using CUDA unified memory mechanisms.

  • If you find other CUDA problems:

    • Possible solution: add export TI_ENABLE_CUDA=0 to your ~/.bashrc. This disables the CUDA backend completely and Taichi will fall back on other GPU backends such as OpenGL.

OpenGL issues​

  • If Taichi crashes with a stack backtrace containing a line of glfwCreateWindow (see #958):

    [Taichi] mode=release
    [E 05/12/20 18.25:00.129] Received signal 11 (Segmentation Fault)
    * Taichi Compiler Stack Traceback *

    ... (many lines, omitted)

    /lib/python3.8/site-packages/taichi/core/../lib/ _glfwPlatformCreateWindow
    /lib/python3.8/site-packages/taichi/core/../lib/ glfwCreateWindow
    /lib/python3.8/site-packages/taichi/core/../lib/ taichi::lang::opengl::initialize_opengl(bool)

    ... (many lines, omitted)

    it is likely because you are running Taichi on a (virtual) machine with an old OpenGL API. Taichi requires OpenGL 4.3+ to work.

    • Possible solution: add export TI_ENABLE_OPENGL=0 to your ~/.bashrc even if you initialize Taichi with other backends than OpenGL. This disables the OpenGL backend detection to avoid incompatibilities.

Installation interrupted​

During the installation, the downloading process is interrupted because of HTTPSConnection error. You can try installing Taichi from a mirror source.

pip install taichi -i

Other issues​

  • If none of those above address your problem, please report this by opening an issue on GitHub. This would help us improve user experiences and compatibility, many thanks!