一、参考资料

环境搭建01-Ubuntu16.04如何查看显卡信息及安装NVDIA显卡驱动

Linux安装NVIDIA显卡驱动的正确姿势

二、环境配置

系统：Ubuntu16.04
显卡：NVIDIA GeForce GTX 1650,4GB

三、准备工作

1. 根据电脑显卡型号，下载对应显卡驱动 下载链接

# 博主对应下载的驱动版本
NVIDIA-Linux-x86_64-470.57.02.run

2. 将显卡驱动放到 /home/yichao/Downloads 目录

四、关键步骤

1. 查看显卡型号、驱动

# 查看显卡型号
ubuntu-drivers devices

如果没有输出，继续下面的操作

2. 禁用nouveau

# （1）验证是否禁用 nouveau
lsmod | grep nouveau# （2）若有输出，说明没有禁用，进行以下操作禁用
sudo gedit /etc/modprobe.d/blacklist.conf# （3）在文件末尾中添加两条：
blacklist nouveau
options nouveau modeset=0# （4）更新配置
sudo update-initramfs -u  # （5）重启系统# （6）查看是否禁用 nouveau，若无输出，则已禁用
lsmod | grep nouveau

3. nvidia驱动更新

# （1）添加ppa源
sudo add-apt-repository ppa:graphics-drivers/ppa#（2）更新apt-get
sudo apt update# （3）查看更新后可以安装的显卡驱动版本
ubuntu-drivers devices  # （4）安装推荐的显卡驱动
sudo apt-get install nvidia-430

4. 安装nvidia显卡驱动

# 1. 将显卡驱动文件拷贝到 /home/yichao/Downloads 目录 # 1.1 按 CTRL + ALT + F1 键登录，从 GUI 转至终端tty1（TUI文本用户界面）# 1.2 输入自己的login(我的是yichao)和password# 2. 临时关闭图形界面
sudo service lightdm stop # 3. 删除已安装的显卡驱动
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove  (加上才能卸载干净原驱动)# 4. 修改显卡驱动文件的权限
sudo chmod  a+x NVIDIA-Linux-x86_64-396.18.run# 5. 安装显卡驱动
sudo ./NVIDIA-Linux-x86_64-470.57.02.run -no-x-check -no-nouveau-check -no-opengl-files   参数说明：
1) –no-x-check：表示安装驱动时不检查X服务，非必需，我们已经禁用图形界面。2) –no-nouveau-check：表示安装驱动时不检查nouveau，非必需，我们已经禁用驱动。3) –no-opengl-files：表示只安装驱动文件，不安装OpenGL文件。这个参数不可省略，否则会导致登陆界面死循环，英语一般称为”login loop”或者”stuck in login”。# 5.1 安装过程中的选项
（1）The distribution-provided pre-install script failed! Are you sure you want to continue?     选择 yes 继续。（2）Would you like to register the kernel module souces with DKMS? This will allow DKMS to automatically build a new module, if you install a different kernel later?            选择NO继续（3）Would you like to run the nvidia-xconfigutility to automatically update your x configuration so that the NVIDIA x driver will be used when you restart x? Any pre-existing x confile will be backed up.  选择 Yes  继续（4）Install 32-Bit compatibility libraries?选择 No 继续开始会显示大量点点（......），然后进入图形化安装界面：  如果提示是否接受（accept），选接受；  如果提示检测到xxx不完整，是否退出安装，选不退出（continue）； 如果提示有旧驱动，询问是否删除旧驱动，选Yes；   如果提示缺少某某模块（modules），询问是否上网下载，选no；   如果提示编译模块，询问是否进行编译，选ok；   如果提示将要修改Xorg.conf，询问是否允许，选Yes；这些选项如果选择错误可能会导致安装失败，没关系，只要前面不出错，多尝试几次就好。# 6. 开启图形界面，没自动跳的话 crtl+alt+f7退回到GUI图形用户界面
sudo service lightdm start  # 7. 若弹出设置对话框，亦表示驱动安装成功
nvidia-settings

5. 查看已安装显卡驱动

nvidia-smi

五、可能出现的问题

缺少软链接

/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8 is not a symbolic link/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 is not a symbolic link/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 is not a symbolic link/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 is not a symbolic link/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 is not a symbolic link/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 is not a symbolic link/sbin/ldconfig.real: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 is not a symbolic link

解决办法：
创建软链接sudo ln -sf /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.1 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8sudo ln -sf /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.1 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8sudo ln -sf /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.1 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8sudo ln -sf /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.1 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8sudo ln -sf /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.1 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8sudo ln -sf /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.1 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8

测试cuDNN错误

# 进入mnistCUDNN目录
cd /usr/src/cudnn_samples_v7/mnistCUDNN# 编译mnistCUDNN
sudo make
或者
sudo make -j8

mnistCUDNN.cpp:538:53: error: ‘CUDNN_CONVOLUTION_FWD_PREFER_FASTEST’ was not declared in this scopeCUDNN_CONVOLUTION_FWD_PREFER_FASTEST,^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
mnistCUDNN.cpp:541:53: error: there are no arguments to ‘cudnnGetConvolutionForwardAlgorithm’ that depend on a template parameter, so a declaration of ‘cudnnGetConvolutionForwardAlgorithm’ must be available [-fpermissive]) );^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
mnistCUDNN.cpp:541:53: note: (if you use ‘-fpermissive’, G++ will accept your code, but allowing the use of an undeclared name is deprecated)) );^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
mnistCUDNN.cpp:541:53: error: there are no arguments to ‘cudnnGetConvolutionForwardAlgorithm’ that depend on a template parameter, so a declaration of ‘cudnnGetConvolutionForwardAlgorithm’ must be available [-fpermissive]) );^
error_util.h:67:65: note: in definition of macro ‘checkCUDNN’_error << "CUDNN failure\nError: " << cudnnGetErrorString(status); \^
mnistCUDNN.cpp: In instantiation of ‘void network_t<value_type>::convoluteForward(const Layer_t<value_type>&, int&, int&, int&, int&, value_type*, value_type**) [with value_type = float]’:
mnistCUDNN.cpp:739:25:   required from ‘int network_t<value_type>::classify_example(const char*, const Layer_t<value_type>&, const Layer_t<value_type>&, const Layer_t<value_type>&, const Layer_t<value_type>&) [with value_type = float]’
mnistCUDNN.cpp:831:75:   required from here
mnistCUDNN.cpp:533:60: error: ‘cudnnGetConvolutionForwardAlgorithm’ was not declared in this scopecheckCUDNN( cudnnGetConvolutionForwardAlgorithm(cudnnHandle,^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
mnistCUDNN.cpp:533:60: error: ‘cudnnGetConvolutionForwardAlgorithm’ was not declared in this scope, and no declarations were found by argument-dependent lookup at the point of instantiation [-fpermissive]checkCUDNN( cudnnGetConvolutionForwardAlgorithm(cudnnHandle,^
error_util.h:67:65: note: in definition of macro ‘checkCUDNN’_error << "CUDNN failure\nError: " << cudnnGetErrorString(status); \^
mnistCUDNN.cpp:533:60: note: ‘cudnnGetConvolutionForwardAlgorithm’ declared here, later in the translation unitcheckCUDNN( cudnnGetConvolutionForwardAlgorithm(cudnnHandle,^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
mnistCUDNN.cpp: In instantiation of ‘void network_t<value_type>::convoluteForward(const Layer_t<value_type>&, int&, int&, int&, int&, value_type*, value_type**) [with value_type = __half]’:
mnistCUDNN.cpp:739:25:   required from ‘int network_t<value_type>::classify_example(const char*, const Layer_t<value_type>&, const Layer_t<value_type>&, const Layer_t<value_type>&, const Layer_t<value_type>&) [with value_type = __half]’
mnistCUDNN.cpp:898:83:   required from here
mnistCUDNN.cpp:533:60: error: ‘cudnnGetConvolutionForwardAlgorithm’ was not declared in this scopecheckCUDNN( cudnnGetConvolutionForwardAlgorithm(cudnnHandle,^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
mnistCUDNN.cpp:533:60: error: ‘cudnnGetConvolutionForwardAlgorithm’ was not declared in this scope, and no declarations were found by argument-dependent lookup at the point of instantiation [-fpermissive]checkCUDNN( cudnnGetConvolutionForwardAlgorithm(cudnnHandle,^
error_util.h:67:65: note: in definition of macro ‘checkCUDNN’_error << "CUDNN failure\nError: " << cudnnGetErrorString(status); \^
mnistCUDNN.cpp:533:60: note: ‘cudnnGetConvolutionForwardAlgorithm’ declared here, later in the translation unitcheckCUDNN( cudnnGetConvolutionForwardAlgorithm(cudnnHandle,^
error_util.h:66:9: note: in definition of macro ‘checkCUDNN’if (status != CUDNN_STATUS_SUCCESS) {                              \^
Makefile:235: recipe for target 'mnistCUDNN.o' failed
make: *** [mnistCUDNN.o] Error 1

Linking agains cublasLt = true
CUDA VERSION: 11000
TARGET ARCH: x86_64
HOST_ARCH: x86_64
TARGET OS: linux
SMS: 30 35 50 53 60 61 62 70 72 75
/usr/local/cuda/bin/nvcc -ccbin g++ -I/usr/local/cuda/include -I/usr/local/cuda/include -IFreeImage/include  -m64    -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o fp16_dev.o -c fp16_dev.cu
nvcc fatal   : Unsupported gpu architecture 'compute_30'
Makefile:238: recipe for target 'fp16_dev.o' failed
make: *** [fp16_dev.o] Error 1

解决办法：
修改Makefile文件
sudo gedit /home/yichao/MyDocuments/cudnn_samples_v7/mnistCUDNN/Makefile$(foreach sm,$(SMS),$(eval GENCODE_FLAGS += -gencode arch=compute_$(sm),code=sm_$(sm)))
修改为
GENCODE_FLAGS += -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75

参考资料
[](https://stackoverflow.com/questions/64774548/unsupported-gpu-architecture-compute-30-on-a-cuda-5-capable-gpu)[NVIDIA CUDA Toolkit 10.2.89](https://docs.nvidia.com/cuda/archive/10.2/pdf/CUDA_Toolkit_Release_Notes.pdf)
错误原因：
CUDA 11.x no longer supports compute capability 3.0
CUDA11.x不再支持compute_30解决办法：
cd /usr/local/cuda-11.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt
Makefile.config#>> nvcc -m64 -gencode=compute_20,code=sm_20 ... -dc math-funcs.cu -o math-funcs64.o
add_custom_command(OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/math-funcs64.o"COMMAND ${NVCC} -m64 -gencode arch=compute_20,code=sm_20 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -dc "${CMAKE_CURRENT_SOURCE_DIR}/math-funcs.cu" -o "${CMAKE_CURRENT_BINARY_DIR}/math-funcs64.o"DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/math-funcs.cu"COMMENT "Building math-funcs64.o")改为
#>> nvcc -m64 -gencode=compute_20,code=sm_20 ... -dc math-funcs.cu -o math-funcs64.o
add_custom_command(OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/math-funcs64.o"COMMAND ${NVCC} -m64 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -dc "${CMAKE_CURRENT_SOURCE_DIR}/math-funcs.cu" -o "${CMAKE_CURRENT_BINARY_DIR}/math-funcs64.o"DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/math-funcs.cu"COMMENT "Building math-funcs64.o")
删除
-gencode=arch=compute_20,code=sm_20
-gencode=arch=compute_30,code=sm_30

测试cuDNN错误

Makefile:163: ../samples_common.mk: 没有那个文件或目录
CUDA VERSION:
TARGET ARCH: x86_64
HOST_ARCH: x86_64
TARGET OS: linux
SMS:
>>> WARNING - no SM architectures have been specified - waiving sample <<<
make: *** No rule to make target '../samples_common.mk'。 停止。

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