Ubuntu16.04+GTX2070+Driver418.43+CUDA10.1+cuDNN7.6
最近須要用到一臺服務器的GPU跑實驗，其間 COLMAP 編譯過程出錯，提示 cuda 版本不支持html
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因而又開始配置環境，首先根據本身機器配置NVIDIA官方網站下載 GeForce 驅動程序linux
>> 檢查機器環境及配置c++
內核版本及操做系統信息git
cv@cv:~/mvs_project/colmap/build$ uname -r
4.15.0-65-generic
cv@cv:~/mvs_project/colmap/build$ lsb_release -a No LSB modules are available. Distributor ID: Ubuntu Description: Ubuntu 16.04.6 LTS Release: 16.04 Codename: xenial cv@cv:~/mvs_project/colmap/build$ gcc --version gcc (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609 Copyright (C) 2015 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.已經安裝過顯卡驅動的機器能夠直接經過 nvidia-smi 命令顯示顯卡型號和驅動版本信息sql
cv@cv:~/mvs_project/colmap/build$ nvidia-smi Sat Nov 30 10:49:14 2019 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 418.43 Driver Version: 418.43 CUDA Version: 10.1 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 GeForce RTX 2070 Off | 00000000:01:00.0 Off | N/A | | 0% 65C P0 1W / 210W | 0MiB / 7952MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+對還沒有安裝過顯卡驅動的機器，能夠經過 lspci 指令查詢，grep -i 的意思是忽略後面匹配項的大小寫ubuntu
cv@cv:~/mvs_project/colmap/build$ lspci | grep -i vga | grep -i nvidia 01:00.0 VGA compatible controller: NVIDIA Corporation Device 1f07 (rev a1)這裏返回的是一串十六進制代碼 1f07，跟咱們日常所見略有不一樣，須要翻譯一下，到 PCI devices 查詢。打不開網頁或者打開很慢的能夠參考放在GitHub上的一份常見型號對應表vim
知道了本身的機器的配置就能夠到上面給出的網站(https://www.geforce.cn/drivers)下載對應的驅動程序。bash
開始安裝驅動以前的準備工做服務器
>> 卸載舊版本或安裝失敗的驅動架構
cv@cv:~/mvs_project/colmap/build$ cd cv@cv:~$ sudo ./NVIDIA-Linux-x86_64-418.43.run --uninstall>> 安裝可能須要的依賴
cv@cv:~$ sudo apt update cv@cv:~$ sudo apt install dkms build-essential linux-headers-generic cv@cv:~$ sudo apt install gcc-multilib xorg-dev cv@cv:~$ sudo apt install freeglut3-dev libx11-dev libxmu-dev libxi-dev cv@cv:~$ sudo apt install libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev>> 禁用 NOUVEAU 驅動
直接使用 VIM 打開，沒有該文件時自動新建
cv@cv:~$ sudo vim /etc/modprobe.d/blacklist-nouveau.conf在文件中添加以下內容，保存退出
blacklist nouveau blacklist lbm-nouveau options nouveau modeset=0 alias nouveau off alias lbm-nouveau off而後執行下面的指令，禁用 nouveau 內核模塊，更新配置，重啓
cv@cv:~$ echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf cv@cv:~$ sudo update-initramfs -u cv@cv:~$ sudo rebootCTRL+ALT+F1 進入命令行模式，輸入下面的命令，若是沒有任何顯示則代表禁用驅動成功了。而後關閉圖形界面，後面要記得從新打開。
cv@cv:~$ lsmod | grep nouveaucv@cv:~$ sudo service lightdm stop
而後開始安裝顯卡驅動
cv@cv:~$ chmod a+x NVIDIA-Linux-x86_64-418.43.run cv@cv:~$ sudo ./NVIDIA-Linux-x86_64-418.43.run --dkms --no-opengl-files-dkms 默認開啓。在 kernel 自行更新時將驅動程序安裝至模塊中，從而阻止驅動程序從新安裝。
–no-opengl-files 表示只安裝驅動文件，不安裝OpenGL文件。這個參數不可省略，不然會致使登錄界面死循環。由於NVIDIA的驅動默認會安裝OpenGL，而Ubuntu的內核自己也有OpenGL且與GUI顯示息息相關，
一旦NVIDIA的驅動覆蓋了OpenGL，在GUI須要動態連接OpenGL庫的時候就會出現問題。
–no-x-check 表示安裝驅動時不檢查X服務，非必需，已經禁用圖形界面。
–no-nouveau-check 表示安裝驅動時不檢查nouveau，非必需，已經禁用nouveau驅動。
–disable-nouveau 禁用nouveau。非必需，由於以前已經手動禁用了nouveau。
安裝過程當中彈出pre-install script failed的信息，繼續安裝便可，沒有影響。
dkms 選項選yes
32位兼容 選項選yes
x-org 選項保持默認選no
安裝完成後打開圖形桌面。
cv@cv:~$ sudo service lightdm start cv@cv:~$ nvidia-smi若是有顯示GPU相關信息表示驅動安裝成功。
卸載CUDA
首先卸載之前安裝的或安裝失敗的CUDA，以便咱們順利進行下面的步驟，直接執行CUDA自帶的卸載腳本。
cv@cv:~$ sudo /usr/local/cuda-9.0/bin/uninstall_cuda_9.0.pl卸載完成後，清除殘留文件夾。
cv@cv:~$ sudo rm -rf /usr/local/cuda-9.0/
安裝CUDA和CUDNN
>> 首先下載安裝文件，咱們要安裝的是CUDA10.1和CUDNN7.6
根據對應關係到 CUDA 下載頁面尋找本身須要的版本，好比我下載的是 CUDA Toolkit 10.1 update2，選擇好操做系統，系統架構和安裝類型以後下載便可。
到 CUDA Toolkit Archive 網站上下載
cuda_10.1.243_418.87.00_linux.run
而後下載CUDNN，須要註冊或登陸NVIDIA帳號，看清楚版本，到 cuDNN Download 網站上 for CUDA 10.1 下載裏面的三個deb安裝包
libcudnn7_7.6.5.32-1+cuda10.1_amd64.deb
libcudnn7-dev_7.6.5.32-1+cuda10.1_amd64.deb
libcudnn7-doc_7.6.5.32-1+cuda10.1_amd64.deb
>> 而後開始安裝 CUDA
cv@cv:~$ sudo service lightdm stop cv@cv:~$ chmod a+x cuda_10.1.243_418.87.00_linux.run cv@cv:~$ sudo ./cuda_10.1.243_418.87.00_linux.run是否贊成條款 accept
選擇安裝界面，除了418.87取消勾選以外其餘保持默認
剩下的都保持默認便可
而後打開配置文件，並在末尾添加連接路徑，保存退出
cv@cv:~$ vim ~/.bashrc export PATH=/usr/local/cuda-10.1/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH使生效
cv@cv:~$ source ~/.bashrc>> 接着安裝 cuDNN
cv@cv:~$ sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.1_amd64.deb cv@cv:~$ sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.1_amd64.deb cv@cv:~$ sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.1_amd64.deb>> 打開圖形界面
cv@cv:~$ sudo service lightdm start
驗證安裝是否成功
>> CUDA 測試，進入到 CUDA 例程路徑下，編譯並測試
cv@cv:~$ cd NVIDIA_CUDA-10.1_Samples/ cv@cv:~/NVIDIA_CUDA-10.1_Samples$ make cv@cv:~/NVIDIA_CUDA-10.1_Samples$ cd bin/x86_64/linux/release/cv@cv:~/NVIDIA_CUDA-10.1_Samples/bin/x86_64/linux/release$ ./deviceQuery ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "GeForce RTX 2070" CUDA Driver Version / Runtime Version 10.1 / 10.1 CUDA Capability Major/Minor version number: 7.5 Total amount of global memory: 7952 MBytes (8338604032 bytes) (36) Multiprocessors, ( 64) CUDA Cores/MP: 2304 CUDA Cores GPU Max Clock rate: 1710 MHz (1.71 GHz) Memory Clock rate: 7001 Mhz Memory Bus Width: 256-bit L2 Cache Size: 4194304 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 1024 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 3 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device supports Compute Preemption: Yes Supports Cooperative Kernel Launch: Yes Supports MultiDevice Co-op Kernel Launch: Yes Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.1, CUDA Runtime Version = 10.1, NumDevs = 1 Result = PASScv@cv:~/NVIDIA_CUDA-10.1_Samples/bin/x86_64/linux/release$ ./bandwidthTest [CUDA Bandwidth Test] - Starting... Running on... Device 0: GeForce RTX 2070 Quick Mode Host to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(GB/s) 32000000 12.8 Device to Host Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(GB/s) 32000000 13.1 Device to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(GB/s) 32000000 382.0 Result = PASS NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.>> cuDNN 測試
cv@cv:~$ cat /usr/include/cudnn.h | grep CUDNN_MAJOR -A 2 -m 1 #define CUDNN_MAJOR 7 #define CUDNN_MINOR 6 #define CUDNN_PATCHLEVEL 5cv@cv:~$ cp -r /usr/src/cudnn_samples_v7/ . cv@cv:~$ cd cudnn_samples_v7/mnistCUDNN/ cv@cv:~$ make Linking agains cublasLt = true CUDA VERSION: 10010 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 g++ -I/usr/local/cuda/include -I/usr/local/cuda/include -IFreeImage/include -o fp16_emu.o -c fp16_emu.cpp g++ -I/usr/local/cuda/include -I/usr/local/cuda/include -IFreeImage/include -o mnistCUDNN.o -c mnistCUDNN.cpp /usr/local/cuda/bin/nvcc -ccbin g++ -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 mnistCUDNN fp16_dev.o fp16_emu.o mnistCUDNN.o -I/usr/local/cuda/include -I/usr/local/cuda/include -IFreeImage/include -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib64 -lcublasLt -LFreeImage/lib/linux/x86_64 -LFreeImage/lib/linux -lcudart -lcublas -lcudnn -lfreeimage -lstdc++ -lmcv@cv:~/cudnn_samples_v7/mnistCUDNN$ ./mnistCUDNN cudnnGetVersion() : 7605 , CUDNN_VERSION from cudnn.h : 7605 (7.6.5) Host compiler version : GCC 5.4.0 There are 1 CUDA capable devices on your machine : device 0 : sms 36 Capabilities 7.5, SmClock 1710.0 Mhz, MemSize (Mb) 7952, MemClock 7001.0 Mhz, Ecc=0, boardGroupID=0 Using device 0 Testing single precision Loading image data/one_28x28.pgm Performing forward propagation ... Testing cudnnGetConvolutionForwardAlgorithm ... Fastest algorithm is Algo 0 Testing cudnnFindConvolutionForwardAlgorithm ... ^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.039040 time requiring 0 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.100576 time requiring 3464 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.122400 time requiring 2057744 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.130560 time requiring 203008 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.173888 time requiring 57600 memory Resulting weights from Softmax: 0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000 Loading image data/three_28x28.pgm Performing forward propagation ... Resulting weights from Softmax: 0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000 Loading image data/five_28x28.pgm Performing forward propagation ... Resulting weights from Softmax: 0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006 Result of classification: 1 3 5 Test passed! Testing half precision (math in single precision) Loading image data/one_28x28.pgm Performing forward propagation ... Testing cudnnGetConvolutionForwardAlgorithm ... Fastest algorithm is Algo 0 Testing cudnnFindConvolutionForwardAlgorithm ... ^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.022528 time requiring 0 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.061344 time requiring 3464 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.065536 time requiring 28800 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.070208 time requiring 203008 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.082592 time requiring 207360 memory Resulting weights from Softmax: 0.0000001 1.0000000 0.0000001 0.0000000 0.0000563 0.0000001 0.0000012 0.0000017 0.0000010 0.0000001 Loading image data/three_28x28.pgm Performing forward propagation ... Resulting weights from Softmax: 0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000714 0.0000000 0.0000000 0.0000000 0.0000000 Loading image data/five_28x28.pgm Performing forward propagation ... Resulting weights from Softmax: 0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006 Result of classification: 1 3 5 Test passed!
當這些配置好以後，COLMAP 的編譯就很順利地經過了。
colmap_buildcv@cv:~/mvs_project/colmap/build$ cmake .. -- The C compiler identification is GNU 5.4.0 -- The CXX compiler identification is GNU 5.4.0 -- Check for working C compiler: /usr/bin/cc -- Check for working C compiler: /usr/bin/cc -- works -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Detecting C compile features -- Detecting C compile features - done -- Check for working CXX compiler: /usr/bin/c++ -- Check for working CXX compiler: /usr/bin/c++ -- works -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Detecting CXX compile features -- Detecting CXX compile features - done -- Found installed version of Eigen: /usr/lib/cmake/eigen3 -- Found required Ceres dependency: Eigen version 3.2.92 in /usr/include/eigen3 -- Found required Ceres dependency: glog -- Performing Test GFLAGS_IN_GOOGLE_NAMESPACE -- Performing Test GFLAGS_IN_GOOGLE_NAMESPACE - Success -- Found required Ceres dependency: gflags -- Found Ceres version: 1.14.0 installed in: /usr/local with components: [EigenSparse, SparseLinearAlgebraLibrary, LAPACK, SuiteSparse, CXSparse, SchurSpecializations, OpenMP, Multithreading] -- Boost version: 1.58.0 -- Found the following Boost libraries: -- program_options -- filesystem -- graph -- regex -- system -- unit_test_framework -- Found Eigen3: /usr/include/eigen3 (Required is at least version "2.91.0") -- Found Eigen -- Includes : /usr/include/eigen3 -- Found FreeImage -- Includes : /usr/include -- Libraries : /usr/lib/x86_64-linux-gnu/libfreeimage.so -- Found Glog -- Includes : /usr/include -- Libraries : /usr/lib/x86_64-linux-gnu/libglog.so -- Found OpenGL: /usr/lib/x86_64-linux-gnu/libGL.so -- Found Glew -- Includes : /usr/include -- Libraries : /usr/lib/x86_64-linux-gnu/libGLEW.so -- Found Git: /usr/bin/git (found version "2.7.4") -- Found Threads: TRUE -- Found Qt -- Module : /usr/lib/x86_64-linux-gnu/cmake/Qt5Core -- Module : /usr/lib/x86_64-linux-gnu/cmake/Qt5OpenGL -- Module : /usr/lib/x86_64-linux-gnu/cmake/Qt5Widgets -- Found CGAL -- Includes : /usr/include -- Libraries : /usr/lib/x86_64-linux-gnu/libCGAL.so.11.0.1 -- Build type not specified, using Release -- Enabling SIMD support -- Enabling OpenMP support -- Disabling interprocedural optimization -- Autodetected CUDA architecture(s): 7.5 -- Enabling CUDA support (version: 10.1, archs: sm_75) -- Enabling OpenGL support -- Disabling profiling support -- Enabling CGAL support -- Configuring done -- Generating done -- Build files have been written to: /home/cv/mvs_project/colmap/build cv@cv:~/mvs_project/colmap/build$ make Scanning dependencies of target flann_automoc [ 0%] Automatic rcc for target flann [ 0%] Built target flann_automoc Scanning dependencies of target flann [ 0%] Building CXX object lib/FLANN/CMakeFiles/flann.dir/flann.cpp.o [ 0%] Building C object lib/FLANN/CMakeFiles/flann.dir/ext/lz4.c.o [ 1%] Building C object lib/FLANN/CMakeFiles/flann.dir/ext/lz4hc.c.o [ 1%] Linking CXX static library libflann.a [ 1%] Built target flann Scanning dependencies of target graclus_automoc [ 1%] Automatic rcc for target graclus [ 1%] Built target graclus_automoc Scanning dependencies of target graclus [ 1%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/util.c.o [ 1%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/mincover.c.o [ 3%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/kwayrefine.c.o [ 3%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/refine.c.o [ 3%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/ometis.c.o [ 3%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/mmatch.c.o [ 3%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/mutil.c.o [ 3%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/mpmetis.c.o [ 5%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/balance.c.o [ 5%] Building C object lib/Graclus/CMakeFiles/graclus.dir/metisLib/mfm2.c.o [ 5%] 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參考資料
[1] NVIDIA DEVELOPER
[2] CUDA TOOLKIT DOCUMENTATION
[4] 最全面解析 Ubuntu 16.04 安裝nvidia驅動 以及各類錯誤
[6] Ubuntu server16.04安裝配置驅動418.8七、cuda10.一、cudnn7.6.4.3八、anaconda、pytorch超詳細解決
[7] Ubuntu 16.04 安裝 CUDA10.1 （解決循環登錄的問題）
[8] 【目標檢測】Ubuntu16.04+RTX2070+CUDA10.0+pytorch1.1搭建CenterNet環境
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