Troubleshooting and Resolution of CUDA Version Conflict Problems in PyTorch Environment

introduction

CUDA version compatibility issues are a cliché when developing deep learning using PyTorch. This article will analyze the scenarios of conflict between the CUDA runtime library of the PyTorch virtual environment and the system's global CUDA environment through a real investigation process, and analyze the problems and locate the causes step by step, and finally provide a solution.

Problem recurring: ImportError: undefined symbol

Start with a seemingly simple import torch statement

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ python
Python 3.12.9 (main, Feb 12 2025, 14:50:50) [Clang 19.1.6 ] on linux
Type "help", "copyright", "credits" or "license" for more information.

>>> import torch
>>> Traceback (most recent call last):
>>> File "<stdin>", line 1, in <module>
>>> File "/home/wangh/codes/ModelForger/.venv/lib/python3.12/site-packages/torch/__init__.py", line 367, in <module>
>>> from torch._C import *  # noqa: F403
>>> ^^^^^^^^^^^^^^^^^^^^^^
>>> ImportError: /home/wangh/codes/ModelForger/.venv/lib/python3.12/site-packages/torch/lib/../../nvidia/cusparse/lib/.12: undefined symbol: __nvJitLinkComplete_12_4, version .12

The error message is clear, in.12The symbol cannot be found in__nvJitLinkComplete_12_4, which usually means there is a problem of version mismatch.

Preliminary Troubleshooting: Environment & CUDA Version

First, let's check the environment and CUDA version

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ echo $LD_LIBRARY_PATH
/usr/local/cuda/lib64:

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Wed_Nov_22_10:17:15_PST_2023
Cuda compilation tools, release 12.3, V12.3.107
Build cuda_12.3.r12.3/compiler.33567101_0

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ uv pip list | grep nvidia
Using Python 3.12.9 environment at: /home/wangh/codes/ModelForger/.venv
nvidia-cublas-cu12        12.4.5.8
nvidia-cuda-cupti-cu12    12.4.127
nvidia-cuda-nvrtc-cu12    12.4.127
nvidia-cuda-runtime-cu12  12.4.127
nvidia-cudnn-cu12         9.1.0.70
nvidia-cufft-cu12         11.2.1.3
nvidia-curand-cu12        10.3.5.147
nvidia-cusolver-cu12      11.6.1.9
nvidia-cusparse-cu12      12.3.1.170
nvidia-nccl-cu12          2.21.5
nvidia-nvjitlink-cu12     12.4.127
nvidia-nvtx-cu12          12.4.127

Two key information were found

nvcc --versionThe CUDA version installed by the system is 12.3.
nvidia-*The version number of nvjitlink installed in the virtual environment is 12.4.127.

According to the error message, the dynamic library in the PyTorch virtual environment.12What is needed is.12of__nvJitLinkComplete_12_4Version,pipThe installed dependency package version itself has no problem, so it is speculated that it may be incorrectly linked to the CUDA 12.3 in the system..12。

Analysis: Dynamic link library loading path

To verify the conjecture, we usepatchelfandlddCommand View.12Dynamic link status:

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ patchelf --print-rpath .12
$ORIGIN:$ORIGIN/../../nvjitlink/lib

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ ldd .12
        .1 (0x00007ffc507e2000)
        .12 => /usr/local/cuda/lib64/.12 (0x00007f867a399000)
        .0 => /lib/x86_64-linux-gnu/.0 (0x00007f867a353000)
        .1 => /lib/x86_64-linux-gnu/.1 (0x00007f867a349000)
        .2 => /lib/x86_64-linux-gnu/.2 (0x00007f867a343000)
        .6 => /lib/x86_64-linux-gnu/.6 (0x00007f867a1f4000)
        libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007f867a1d7000)
        .6 => /lib/x86_64-linux-gnu/.6 (0x00007f8679fe5000)
        /lib64/.2 (0x00007f868e62d000)

Sure enough.12Depend on.12Loaded to the system CUDA directory (/usr/local/cuda/lib64) directory under , instead of predefined PyTorch virtual environment.

The root of the problem: LD_LIBRARY_PATH priority

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ echo $LD_LIBRARY_PATH
/usr/local/cuda/lib64:

At this point, the root cause of the problem has been clarified: the LD_LIBRARY_PATH environment variable causes the system CUDA library path to be loaded before the CUDA library path of the PyTorch virtual environment. This results in a version mismatch and PyTorch cannot find the desired symbol.

Solution: unset LD_LIBRARY_PATH

The most direct way to solve this problem is to remove the LD_LIBRARY_PATH setting of the system CUDA path:

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ unset LD_LIBRARY_PATH

Check again.12Dynamic links:

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ ldd .12
        .1 (0x00007fff959a7000)
        .12 => /home/wangh/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib/./../../nvjitlink/lib/.12 (0x00007f303000e000)
        .0 => /lib/x86_64-linux-gnu/.0 (0x00007f302ffc8000)
        .1 => /lib/x86_64-linux-gnu/.1 (0x00007f302ffbe000)
        .2 => /lib/x86_64-linux-gnu/.2 (0x00007f302ffb8000)
        .6 => /lib/x86_64-linux-gnu/.6 (0x00007f302fe69000)
        libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007f302fe4c000)
        .6 => /lib/x86_64-linux-gnu/.6 (0x00007f302fc5a000)
        /lib64/.2 (0x00007f30443f9000)

Now,.12Correctly loaded into the directory of the PyTorch virtual environment.

Verification: Problem Solving

(modelforger) wangh@ubuntu:~/codes/ModelForger/.venv/lib/python3.12/site-packages/nvidia/cusparse/lib$ python
Python 3.12.9 (main, Feb 12 2025, 14:50:50) [Clang 19.1.6 ] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch

import torch success! Problem solved.

Best Practices and Summary

Avoid global settings LD_LIBRARY_PATH: Setting LD_LIBRARY_PATH in global environment variables such as .bashrc or .bash_profile will interfere with the independence of the virtual environment.
Understand dynamic linking mechanisms: Understanding the role of LD_LIBRARY_PATH and the loading order of dynamic link libraries can help quickly locate and resolve similar problems.

The above is the detailed content of the problem of CUDA version conflict in the PyTorch environment. For more information about PyTorch CUDA version conflict, please pay attention to my other related articles!