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Version: v1.1.0

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 3.7/3.8/3.9/3.10:

      python3 -c "print(__import__('sys').version[:3])"
      # 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!