jetson@jetson-desktop:~$ sudo passwd [sudo] password for jetson: Enter new UNIX password: Retype new UNIX password: passwd: password updated successfully jetson@jetson-desktop:~$ su Password: root@jetson-desktop:/home/jetson#
设置允许超级管理员远程访问 # vi /etc/ssh/sshd_config 找到并用#注释掉这行：PermitRootLogin prohibit-password
/etc/network/interfaces # interfaces(5) file used by ifup(8) and ifdown(8) # Include files from /etc/network/interfaces.d: source-directory /etc/network/interfaces.d
root@jetson-desktop:/# cd /usr/src/cudnn_samples_v7/mnistCUDNN/ root@jetson-desktop:/usr/src/cudnn_samples_v7/mnistCUDNN# make /usr/local/cuda/bin/nvcc -ccbin g++ -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_53,code=compute_53 -o fp16_dev.o -c fp16_dev.cu g++ -I/usr/local/cuda/include -IFreeImage/include -o fp16_emu.o -c fp16_emu.cpp g++ -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_53,code=compute_53 -o mnistCUDNN fp16_dev.o fp16_emu.o mnistCUDNN.o -I/usr/local/cuda/include -IFreeImage/include -LFreeImage/lib/linux/aarch64 -LFreeImage/lib/linux -lcudart -lcublas -lcudnn -lfreeimage -lstdc++ -lm FreeImage/lib/linux/aarch64/libfreeimage.a(strenc.o): In function `StrIOEncInit': strenc.c:(.text+0x1294): warning: the use of `tmpnam' is dangerous, better use `mkstemp'
执行sample root@jetson-desktop:/usr/src/cudnn_samples_v7/mnistCUDNN# chmod a+x mnistCUDNN root@jetson-desktop:/usr/src/cudnn_samples_v7/mnistCUDNN# ./mnistCUDNN cudnnGetVersion() : 7301 , CUDNN_VERSION from cudnn.h : 7301 (7.3.1) Host compiler version : GCC 7.3.0 There are 1 CUDA capable devices on your machine : device 0 : sms 1 Capabilities 5.3, SmClock 921.6 Mhz, MemSize (Mb) 3964, MemClock 12.8 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 1 Testing cudnnFindConvolutionForwardAlgorithm ... ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.341979 time requiring 3464 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.395625 time requiring 0 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 5.210573 time requiring 207360 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 5.213230 time requiring 2057744 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 14.978802 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 1 Testing cudnnFindConvolutionForwardAlgorithm ... ^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.135000 time requiring 0 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.170885 time requiring 3464 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.282708 time requiring 28800 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 1.206094 time requiring 207360 memory ^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 5.214895 time requiring 203008 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!
测试通过； 2.4核对python
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root@jetson-desktop:/# python3 Python 3.6.7 (default, Oct 22 2018, 11:32:17) [GCC 8.2.0] on linux Type "help", "copyright", "credits" or "license" for more information.
root@jetson-desktop:~# python3 Python 3.6.7 (default, Oct 22 2018, 11:32:17) [GCC 8.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import cv2 >>> print(cv2.__version__) 3.2.0 >>>
默认3.2版本，和系统本身自带的不统一; python2.7版本自带的opencv
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root@jetson-desktop:~# python Python 2.7.15rc1 (default, Nov 12 2018, 14:31:15) [GCC 7.3.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import cv2 >>> print(cv2.__version__) File "<stdin>", line 1 print(cv2.__version__) ^ IndentationError: unexpected indent >>> print(cv2.__version__) 3.3.1 >>>
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe
