Linux下如何设置CUDA的路径

最新推荐文章于 2025-08-04 08:00:00 发布

knighthood2001

最新推荐文章于 2025-08-04 08:00:00 发布

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文章标签： linux 运维服务器

本文链接：https://blog.youkuaiyun.com/knighthood2001/article/details/149121099

今天遇到一个关于CUDA的问题，我要跑的深度学习代码，他里面有cuda编程，需要编译。但是你运行就报错。

代码提示我大段报错。

(score-denoise) ubuntu@GPUA10002:~/wbd/score-denoise_Transformerdepth20$ python train.py 
Detected CUDA files, patching ldflags
Emitting ninja build file /home/ubuntu/wbd/score-denoise_Transformerdepth20/utils/cutils/build/build.ninja...
Building extension module cutils_...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/3] /usr/bin/nvcc  -DTORCH_EXTENSION_NAME=cutils_ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/TH -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/THC -isystem /data/miniconda3/envs/score-denoise/include/python3.8 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -Xptxas -v --generate-code=arch=compute_80,code=sm_80 -std=c++14 -c /home/ubuntu/wbd/score-denoise_Transformerdepth20/utils/cutils/srcs/half_aligned_knn_sub_maxpooling.cu -o half_aligned_knn_sub_maxpooling.cuda.o 
FAILED: half_aligned_knn_sub_maxpooling.cuda.o 
/usr/bin/nvcc  -DTORCH_EXTENSION_NAME=cutils_ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/TH -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/THC -isystem /data/miniconda3/envs/score-denoise/include/python3.8 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -Xptxas -v --generate-code=arch=compute_80,code=sm_80 -std=c++14 -c /home/ubuntu/wbd/score-denoise_Transformerdepth20/utils/cutils/srcs/half_aligned_knn_sub_maxpooling.cu -o half_aligned_knn_sub_maxpooling.cuda.o 
nvcc fatal   : Unsupported gpu architecture 'compute_80'
[2/3] /usr/bin/nvcc  -DTORCH_EXTENSION_NAME=cutils_ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/TH -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/THC -isystem /data/miniconda3/envs/score-denoise/include/python3.8 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -Xptxas -v --generate-code=arch=compute_80,code=sm_80 -std=c++14 -c /home/ubuntu/wbd/score-denoise_Transformerdepth20/utils/cutils/srcs/aligned_knn_sub_maxpooling.cu -o aligned_knn_sub_maxpooling.cuda.o 
FAILED: aligned_knn_sub_maxpooling.cuda.o 
/usr/bin/nvcc  -DTORCH_EXTENSION_NAME=cutils_ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/TH -isystem /data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/include/THC -isystem /data/miniconda3/envs/score-denoise/include/python3.8 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -Xptxas -v --generate-code=arch=compute_80,code=sm_80 -std=c++14 -c /home/ubuntu/wbd/score-denoise_Transformerdepth20/utils/cutils/srcs/aligned_knn_sub_maxpooling.cu -o aligned_knn_sub_maxpooling.cuda.o 
nvcc fatal   : Unsupported gpu architecture 'compute_80'
ninja: build stopped: subcommand failed.
Traceback (most recent call last):
  File "/data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1666, in _run_ninja_build
    subprocess.run(
  File "/data/miniconda3/envs/score-denoise/lib/python3.8/subprocess.py", line 516, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "train.py", line 13, in <module>
    from models.denoise import *
  File "/home/ubuntu/wbd/score-denoise_Transformerdepth20/models/denoise.py", line 7, in <module>
    from .feature import FeatureExtractionWithResLFE
  File "/home/ubuntu/wbd/score-denoise_Transformerdepth20/models/feature.py", line 6, in <module>
    from .ResLFE_block import ResLFE_Block
  File "/home/ubuntu/wbd/score-denoise_Transformerdepth20/models/ResLFE_block.py", line 8, in <module>
    from utils.cutils import knn_edge_maxpooling
  File "/home/ubuntu/wbd/score-denoise_Transformerdepth20/utils/cutils/__init__.py", line 14, in <module>
    cutils = load("cutils_", sources=sources, extra_cflags=["-O3", "-mavx2", "-funroll-loops"], extra_cuda_cflags=["-Xptxas","-v",  "--generate-code=arch=compute_80,code=sm_80"],
  File "/data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1080, in load
    return _jit_compile(
  File "/data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1293, in _jit_compile
    _write_ninja_file_and_build_library(
  File "/data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1405, in _write_ninja_file_and_build_library
    _run_ninja_build(
  File "/data/miniconda3/envs/score-denoise/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1682, in _run_ninja_build
    raise RuntimeError(message) from e
RuntimeError: Error building extension 'cutils_'

然后你问ai，ai给你的建议是查看nvcc版本

nvcc --version

然后你发现，没有这个，然后会提示你

sudo apt install nvidia-cuda-toolkit

然后你又去安装，安装好后查看了一下

(base) ubuntu@GPUA10002:~/wbd/score-denoise_Transformerdepth20$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85

发现这是很老的版本

新版本的这样的提示

(score-denoise) wu@wu:~/code/pointDenoise/score-denoise_Transformerdepth20$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Feb_27_16:19:38_PST_2024
Cuda compilation tools, release 12.4, V12.4.99

然后老版本的，我就编译不通过。

然后我就一直在查找相关问题，一直在想，没安装nvcc前，别人也能够跑深度学习，然后感觉很奇怪。

然后我后面把这个卸载了

sudo apt remove nvidia-cuda-toolkit

这个是可以卸载的，大家放心。

然后就报错CUDA路径的问题
在这里插入图片描述然后我就去~/.bashrc找

在这里插入图片描述发现里面是有这个路径的，然后我就试着在终端输入

export PATH=/usr/local/cuda-12.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:$LD_LIBRARY_PATH

还是不行。

然后我就没辙了。

最后的最后，我进入/usr/local查看，发现他下载的是cuda-12.0，不是12.1

在这里插入图片描述

然后我就在我要跑的代码终端，输入下面两个命令，然后执行跑代码，就能够编译通过了。

export PATH=/usr/local/cuda-12.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.0/lib64:$LD_LIBRARY_PATH

我也不知道为啥，~/.bashrc里面为啥不是cuda-12.1，所以我也没有把他里面改成12.1，就按照原来的吧，以后要跑，直接先输入这两行命令，然后执行代码，当然，如果你不要编译，你直接运行是没有关系的。

这个问题，浪费了我一下午的时间，如果你也有相关问题，一定要注意，去看看/usr/local/到底是多少版本的cuda。