CAFFE实验学习笔记（3）——SSD(Single Shot MultiBox Detector)

1. cuda-7.5 is installed,but itshows 5.5 while checking the nvcc –v

http://blog.csdn.net/xuezhisdc/article/details/48651003 1、将/usr/local/cuda-7.0/bin添加到环境变量PATH路径中，这样一来，就可以在任何路径下调用cuda相关的可执行文件了。 2、将/usr/local/cuda7.0/lib64添加环境变量LD_LIBRARY_PATH 中，作为共享库使用。这样一来，后面编译Cuda Samples 和OpenCV时，就不会提示找不到库的错误了。 操作1：将以下内容添加到文件/etc/profile的最后面，保存后，执行命令source /etc/profile，使配置生效。 PATH=/usr/local/cuda-7.5/bin:$PATH export PATH LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64 export LD_ LIBRARY_PATH

ruyunli@ruyunli-All-Series:~$ echo $PATH /usr/local/cuda-7.5/bin:/home/ruyunli/anaconda2/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games

ruyunli@ruyunli-All-Series:~$ echo $LD_LIBRARY_PATH /usr/local/cuda-7.5/lib64

# Optionally, download your own cudnn; requires registration. if [ -f "cudnn-7.0-linux-x64-v4.0-prod.tgz" ] ; then tar -xvf cudnn-7.0-linux-x64-v4.0-prod.tgz sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo cp cuda/include/cudnn.h /usr/local/cuda/include fi # Need to put cuda on the linker path. This may not be the best way, but it works. sudo sh -c "sudo echo '/usr/local/cuda/lib64' > /etc/ld.so.conf.d/cuda_hack.conf" sudo ldconfig /usr/local/cuda/lib64

http://buptldy.github.io/2016/04/09/2016-04-09-Deepin%20CUDA%E5%AE%89%E8%A3%85%E5%8F%8AKeras%E4%BD%BF%E7%94%A8GPU%E6%A8%A1%E5%BC%8F%E8%BF%90%E8%A1%8C/

Device 0: "GeForce GTX 980 Ti" CUDA Driver Version / Runtime Version 8.0 / 7.5 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 6075 MBytes (6369837056 bytes) (22) Multiprocessors, (128) CUDA Cores/MP: 2816 CUDA Cores GPU Max Clock rate: 1190 MHz (1.19 GHz) Memory Clock rate: 3505 Mhz Memory Bus Width: 384-bit L2 Cache Size: 3145728 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 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: 2048 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 2 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 PCI Domain ID / Bus ID / location ID: 0 / 1 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > Device 1: "GeForce GTX 970" CUDA Driver Version / Runtime Version 8.0 / 7.5 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 4034 MBytes (4229758976 bytes) (13) Multiprocessors, (128) CUDA Cores/MP: 1664 CUDA Cores GPU Max Clock rate: 1317 MHz (1.32 GHz) Memory Clock rate: 3505 Mhz Memory Bus Width: 256-bit L2 Cache Size: 1835008 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 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: 2048 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 2 copy engine(s) Run time limit on kernels: Yes 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 PCI Domain ID / Bus ID / location ID: 0 / 5 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > > Peer access from GeForce GTX 980 Ti (GPU0) -> GeForce GTX 970 (GPU1) : No > Peer access from GeForce GTX 970 (GPU1) -> GeForce GTX 980 Ti (GPU0) : No deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 7.5, NumDevs = 2, Device0 = GeForce GTX 980 Ti, Device1 = GeForce GTX 970

The NVIDIA CUDA® Deep Neural Network library(cuDNN) is a GPU-accelerated library of primitives for deep neural networks.