CUDA:Failed to compile cuda_ndarray.cu: DLL load failed: 找不到指定的模块

在Windows 10环境下,使用CUDA 7.5和Keras GPU版本时遇到CUDA编译失败问题,错误为'DLL load failed: 找不到指定的模块'。尝试更新CUDA版本、查阅相关资料未果后,通过设置CUDA相关环境变量解决了问题,具体包括CUDA_SDK_PATH, CUDA_LIB_PATH, CUDA_BIN_PATH, CUDA_SDK_LIB_PATH, CUDA_SDK_BIN_PATH,并在系统环境变量Path中添加这些路径的64位版本。" 113671647,10548038,Python批量操作数据库:数据添加、查询与更新,"['Python', '数据库操作', 'pymysql', '数据处理', '数据批量操作']

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我环境配置:

WIN10 64位,显卡GT 740M,cpu i54200U,8G内存
numpy1.12.0
theano0.8.2
pip8.1.2
Anconda2.7
CUDA7.5
cudnn-7.5-windows7-x64-v5.0-ga
keras1.2.1


按照http://keras-cn.readthedocs.io/en/latest/getting_started/keras_windows/的教程安装后
在测试时import theano或者import keras(gpu版本),出现错误
ERROR (theano.sandbox.cuda):
Failed to compile cuda_ndarray.cu: DLL load failed: 找不到指定的模块

网上找了大量的资料也没找到答案,于是各种怀疑。
1、笔记本显卡不支持?
https://developer.nvidia.com/cuda-gpus 处确定显卡没问题。
2、在http://stackoverflow.com/questions/40911741/theano-import-error-when-used-with-gpu
处出现跟我类似的问题,貌似是通过重装CUDA8.0解决的。

坚信我的不是版本的问题,而且肯定跟CUDA有关,于是在其它教程
(http://blog.youkuaiyun.com/m624197265/article/details/45700619)中看到
CUDA安装后还手动设置了如下环境变量:
CUDA_SDK_PATH = C:\ProgramData\NVIDIA Corporation\CUDASamples\v5.0
CUDA_LIB_PATH = %CUDA_PATH%\lib\Win32
CUDA_BIN_PATH = %CUDA_PATH%\bin
CUDA_SDK_LIB_PATH = %CUDA_SDK_PATH%\common\lib\Win32
CUDA_SDK_BIN_PATH = %CUDA_SDK_PATH%\bin\Win32
然后在系统环境变量Path 后添加内容:
;%CUDA_LIB_PATH%;%CUDA_BIN_PATH%;%CUDA_SDK_LIB_PATH%;%CUDA_SDK_BIN_PATH%

将其改为自己对应的64位版本,再测试,成功了!
难道CUDA8.0中不需要设置如上环境变量????

The above exception was the direct cause of the following exception: Traceback (most recent call last): File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\TrainCNO.py", line 160, in <module> output_pred_batch = model(input_batch) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\CNOModule.py", line 476, in forward x = self.lift(x) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\CNOModule.py", line 151, in forward x = self.inter_CNOBlock(x) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\CNOModule.py", line 104, in forward return self.activation(x) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\training\filtered_networks.py", line 392, in forward x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\torch_utils\ops\filtered_lrelu.py", line 114, in filtered_lrelu if impl == 'cuda' and x.device.type == 'cuda' and _init(): File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\torch_utils\ops\filtered_lrelu.py", line 26, in _init _plugin = custom_ops.get_plugin( File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\torch_utils\custom_ops.py", line 136, in get_plugin torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 1309, in load return _jit_compile( File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 1719, in _jit_compile _write_ninja_file_and_build_library( File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 1832, in _write_ninja_file_and_build_library _run_ninja_build( File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 2123, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'filtered_lrelu_plugin': [1/1] "D:\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x64/link.exe" filtered_lrelu.o filtered_lrelu_wr.cuda.o filtered_lrelu_rd.cuda.o filtered_lrelu_ns.cuda.o /nologo /DLL c10.lib c10_cuda.lib torch_cpu.lib torch_cuda.lib -INCLUDE:?warp_size@cuda@at@@YAHXZ torch.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\lib torch_python.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\libs "/LIBPATH:C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64" cudart.lib /out:filtered_lrelu_plugin.pyd FAILED: filtered_lrelu_plugin.pyd "D:\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x64/link.exe" filtered_lrelu.o filtered_lrelu_wr.cuda.o filtered_lrelu_rd.cuda.o filtered_lrelu_ns.cuda.o /nologo /DLL c10.lib c10_cuda.lib torch_cpu.lib torch_cuda.lib -INCLUDE:?warp_size@cuda@at@@YAHXZ torch.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\lib torch_python.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\libs "/LIBPATH:C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64" cudart.lib /out:filtered_lrelu_plugin.pyd 正在创建库 filtered_lrelu_plugin.lib 和对象 filtered_lrelu_plugin.exp filtered_lrelu.o : error LNK2019: 无法解析的外部符号 __std_find_trivial_1,函数 "char const * __cdecl std::_Find_vectorized<char const ,char>(char const * const,char const * const,char)" (??$_Find_vectorized@$$CBDD@std@@YAPEBDQEBD0D@Z) 中引用了该符号 filtered_lrelu.o : error LNK2019: 无法解析的外部符号 __std_search_1,函数 "char const * __cdecl std::_Search_vectorized<char const ,char const >(char const * const,char const * const,char const * const,unsigned __int64)" (??$_Search_vectorized@$$CBD$$CBD@std@@YAPEBDQEBD00_K@Z) 中引用了该符号 filtered_lrelu.o : error LNK2019: 无法解析的外部符号 __std_find_end_1,函数 "char const * __cdecl std::_Find_end_vectorized<char const ,char const >(char const * const,char const * const,char const * const,unsigned __int64)" (??$_Find_end_vectorized@$$CBD$$CBD@std@@YAPEBDQEBD00_K@Z) 中引用了该符号 ninja: build stopped: subcommand failed.如何解决
07-06
Traceback (most recent call last): File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 2107, in _run_ninja_build subprocess.run( File "D:\anaconda\envs\pytorch2.3.1\lib\subprocess.py", line 528, 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 "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\TrainCNO.py", line 160, in <module> output_pred_batch = model(input_batch) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\CNOModule.py", line 476, in forward x = self.lift(x) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\CNOModule.py", line 151, in forward x = self.inter_CNOBlock(x) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\CNOModule.py", line 104, in forward return self.activation(x) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\training\filtered_networks.py", line 392, in forward x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\torch_utils\ops\filtered_lrelu.py", line 114, in filtered_lrelu if impl == 'cuda' and x.device.type == 'cuda' and _init(): File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\torch_utils\ops\filtered_lrelu.py", line 26, in _init _plugin = custom_ops.get_plugin( File "D:\pycharm\pythonProject\ConvolutionalNeuralOperator-main\torch_utils\custom_ops.py", line 136, in get_plugin torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 1309, in load return _jit_compile( File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 1719, in _jit_compile _write_ninja_file_and_build_library( File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 1832, in _write_ninja_file_and_build_library _run_ninja_build( File "D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\utils\cpp_extension.py", line 2123, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error building extension 'filtered_lrelu_plugin': [1/4] C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\nvcc --generate-dependencies-with-compile --dependency-output filtered_lrelu_ns.cuda.o.d -Xcudafe --diag_suppress=dll_interface_conflict_dllexport_assumed -Xcudafe --diag_suppress=dll_interface_conflict_none_assumed -Xcudafe --diag_suppress=field_without_dll_interface -Xcudafe --diag_suppress=base_class_has_different_dll_interface -Xcompiler /EHsc -Xcompiler /wd4068 -Xcompiler /wd4067 -Xcompiler /wd4624 -Xcompiler /wd4190 -Xcompiler /wd4018 -Xcompiler /wd4275 -Xcompiler /wd4267 -Xcompiler /wd4244 -Xcompiler /wd4251 -Xcompiler /wd4819 -Xcompiler /MD -DTORCH_EXTENSION_NAME=filtered_lrelu_plugin -DTORCH_API_INCLUDE_EXTENSION_H -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\torch\csrc\api\include -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\TH -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include" -ID:\anaconda\envs\pytorch2.3.1\Include -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_89,code=compute_89 -gencode=arch=compute_89,code=sm_89 -std=c++17 --use_fast_math --allow-unsupported-compiler -c C:\Users\Administrator\AppData\Local\torch_extensions\torch_extensions\Cache\py39_cu118\filtered_lrelu_plugin\2e9606d7cf844ec44b9f500eaacd35c0-nvidia-geforce-rtx-4060-ti\filtered_lrelu_ns.cu -o filtered_lrelu_ns.cuda.o [2/4] C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\nvcc --generate-dependencies-with-compile --dependency-output filtered_lrelu_rd.cuda.o.d -Xcudafe --diag_suppress=dll_interface_conflict_dllexport_assumed -Xcudafe --diag_suppress=dll_interface_conflict_none_assumed -Xcudafe --diag_suppress=field_without_dll_interface -Xcudafe --diag_suppress=base_class_has_different_dll_interface -Xcompiler /EHsc -Xcompiler /wd4068 -Xcompiler /wd4067 -Xcompiler /wd4624 -Xcompiler /wd4190 -Xcompiler /wd4018 -Xcompiler /wd4275 -Xcompiler /wd4267 -Xcompiler /wd4244 -Xcompiler /wd4251 -Xcompiler /wd4819 -Xcompiler /MD -DTORCH_EXTENSION_NAME=filtered_lrelu_plugin -DTORCH_API_INCLUDE_EXTENSION_H -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\torch\csrc\api\include -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\TH -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include" -ID:\anaconda\envs\pytorch2.3.1\Include -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_89,code=compute_89 -gencode=arch=compute_89,code=sm_89 -std=c++17 --use_fast_math --allow-unsupported-compiler -c C:\Users\Administrator\AppData\Local\torch_extensions\torch_extensions\Cache\py39_cu118\filtered_lrelu_plugin\2e9606d7cf844ec44b9f500eaacd35c0-nvidia-geforce-rtx-4060-ti\filtered_lrelu_rd.cu -o filtered_lrelu_rd.cuda.o [3/4] C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin\nvcc --generate-dependencies-with-compile --dependency-output filtered_lrelu_wr.cuda.o.d -Xcudafe --diag_suppress=dll_interface_conflict_dllexport_assumed -Xcudafe --diag_suppress=dll_interface_conflict_none_assumed -Xcudafe --diag_suppress=field_without_dll_interface -Xcudafe --diag_suppress=base_class_has_different_dll_interface -Xcompiler /EHsc -Xcompiler /wd4068 -Xcompiler /wd4067 -Xcompiler /wd4624 -Xcompiler /wd4190 -Xcompiler /wd4018 -Xcompiler /wd4275 -Xcompiler /wd4267 -Xcompiler /wd4244 -Xcompiler /wd4251 -Xcompiler /wd4819 -Xcompiler /MD -DTORCH_EXTENSION_NAME=filtered_lrelu_plugin -DTORCH_API_INCLUDE_EXTENSION_H -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\torch\csrc\api\include -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\TH -ID:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\include" -ID:\anaconda\envs\pytorch2.3.1\Include -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_89,code=compute_89 -gencode=arch=compute_89,code=sm_89 -std=c++17 --use_fast_math --allow-unsupported-compiler -c C:\Users\Administrator\AppData\Local\torch_extensions\torch_extensions\Cache\py39_cu118\filtered_lrelu_plugin\2e9606d7cf844ec44b9f500eaacd35c0-nvidia-geforce-rtx-4060-ti\filtered_lrelu_wr.cu -o filtered_lrelu_wr.cuda.o [4/4] "D:\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x64/link.exe" filtered_lrelu.o filtered_lrelu_wr.cuda.o filtered_lrelu_rd.cuda.o filtered_lrelu_ns.cuda.o /nologo /DLL c10.lib c10_cuda.lib torch_cpu.lib torch_cuda.lib -INCLUDE:?warp_size@cuda@at@@YAHXZ torch.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\lib torch_python.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\libs "/LIBPATH:C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64" cudart.lib /out:filtered_lrelu_plugin.pyd FAILED: filtered_lrelu_plugin.pyd "D:\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x64/link.exe" filtered_lrelu.o filtered_lrelu_wr.cuda.o filtered_lrelu_rd.cuda.o filtered_lrelu_ns.cuda.o /nologo /DLL c10.lib c10_cuda.lib torch_cpu.lib torch_cuda.lib -INCLUDE:?warp_size@cuda@at@@YAHXZ torch.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\lib\site-packages\torch\lib torch_python.lib /LIBPATH:D:\anaconda\envs\pytorch2.3.1\libs "/LIBPATH:C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib\x64" cudart.lib /out:filtered_lrelu_plugin.pyd 正在创建库 filtered_lrelu_plugin.lib 和对象 filtered_lrelu_plugin.exp filtered_lrelu.o : error LNK2019: 无法解析的外部符号 __std_find_trivial_1,函数 "char const * __cdecl std::_Find_vectorized<char const ,char>(char const * const,char const * const,char)" (??$_Find_vectorized@$$CBDD@std@@YAPEBDQEBD0D@Z) 中引用了该符号 ninja: build stopped: subcommand failed.这是怎么回事
最新发布
07-06
<think>嗯,用户遇到了执行`compile_op.sh`脚本时出现`Can't open compile_op.sh`的错误,也就是`sh: Can't open compile_op.sh`。我需要帮他们出可能的原因和解决方法。 首先,我应该考虑文件是否存在。用户可能输入了错误的文件名或者路径。比如,如果当前目录下没有这个文件,直接运行`sh compile_op.sh`就会报错。这时候需要检查文件是否存在,可以用`ls`命令查看当前目录下的文件,或者用绝对路径来执行脚本。 然后,权限问题也是一个常见原因。如果脚本没有可执行权限,即使文件存在,也无法运行。这时候应该用`chmod +x compile_op.sh`命令添加执行权限,再尝试运行。不过用户用的是`sh`命令,可能不需要可执行权限,因为`sh`后面跟文件名的话,只要有读取权限就可以,但不确定具体情况,所以还是建议检查权限设置。 接下来,文件路径是否正确。如果用户不在脚本所在的目录下,直接运行可能会不到文件。需要确认当前工作目录,或者使用`./compile_op.sh`来执行当前目录下的脚本,或者使用绝对路径。 还有可能是文件编码或格式问题。比如,脚本是在Windows下编辑的,换行符是CRLF,而Linux下需要LF。或者脚本开头没有指定正确的shebang,比如`#!/bin/bash`。这时候可以用`dos2unix`工具转换文件格式,或者用`vim`查看是否有特殊字符。 另外,用户提供的引用里提到了一些编译问题,比如需要安装build-essential、Java JDK等依赖。虽然当前错误是文件打不开,但可能后续步骤需要这些依赖,所以提醒用户安装可能也是必要的。 可能用户还漏掉了某些步骤,比如解压文件或者进入正确的目录。需要确保他们按照文档的步骤正确操作,比如引用[1]中的解压步骤和安装依赖。 总结下来,可能的原因包括文件不存在、权限不足、路径错误、文件格式问题,以及依赖缺失。解决方法需要逐一排查这些点,并给出对应的命令或步骤。</think>### 错误原因分析及解决方法 #### 1. 文件路径错误 - **现象**: 使用相对路径时未进入脚本所在目录,或文件名拼写错误 - **验证方法**: 执行 `ls -l compile_op.sh` 检查是否存在 - **解决方法**: ```bash cd /path/to/script_directory # 切换到脚本所在目录 sh ./compile_op.sh # 显式指定当前目录下的脚本 ``` #### 2. 文件权限不足 - **现象**: 未添加可执行权限(即使使用`sh`命令,仍需读取权限) - **验证方法**: 执行 `ls -l compile_op.sh` 查看权限 - **解决方法**: ```bash chmod +x compile_op.sh # 添加可执行权限 sh compile_op.sh # 或 ./compile_op.sh ``` #### 3. 文件格式问题 - **现象**: Windows格式换行符导致解析错误(常见于跨平台传输文件) - **验证方法**: 使用 `file compile_op.sh` 查看文件类型 - **解决方法**: ```bash sudo apt-get install dos2unix # 安装格式转换工具[^1] dos2unix compile_op.sh # 转换文件格式 ``` #### 4. 依赖缺失 根据引用[1]的编译环境要求,需确保已安装: ```bash sudo apt-get install build-essential openjdk-11-jdk python zip unzip ``` ### 完整排查流程 1. 确认文件存在性 `ls compile_op.sh` 2. 检查文件权限 `ls -l compile_op.sh` 3. 验证文件格式 `file compile_op.sh` 4. 安装必要依赖(见上述第4条) 5. 使用绝对路径执行 `/full/path/to/compile_op.sh`
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