簡介：
深度學習算法使用nvidia GPU可以加速推理速度。但開發(fā)具有一定的困難谱秽。記錄一下自己的安裝流程
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native 開發(fā)流程
- 安裝nvidia driver,請參考一下工程。
https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07安裝完成后心例，重啟電腦， 使用 nvidia-smi 查看顯卡是否安裝成功
- 安裝 cuda
https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#package-manager-installation- cudnn 安裝
https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-linux建議tar包安裝
Navigate to your <cudnnpath> directory containing the cuDNN Tar file. Unzip the cuDNN package. $ tar -xzvf cudnn-10.2-linux-x64-v7.6.5.32.tgz Copy the following files into the CUDA Toolkit directory, and change the file permissions. $ sudo cp cuda/include/cudnn.h /usr/local/cuda/include $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*- tensorrt安裝（optional鞋囊，如果工程有tensorrt依賴的話）
https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing建議tar包安裝
Download the TensorRT tar file that matches the Linux distribution you are using. Choose where you want to install TensorRT. This tar file will install everything into a subdirectory called TensorRT-7.x.x.x. Unpack the tar file. version=”7.x.x.x” os=”<os>” arch=$(uname -m) cuda=”cuda-x.x” cudnn=”cudnn7.x” tar xzvf TensorRT-${version}.${os}.${arch}-gnu.${cuda}.${cudnn}.tar.gz Where: 7.x.x.x is your TensorRT version <os> is: Ubuntu-16.04 Ubuntu-18.04 CentOS-7.6 cuda-x.x is CUDA version 9.0, 10.0, or 10.2. cudnn7.x is cuDNN version 7.6. This directory will have sub-directories like lib, include, data, etc… ls TensorRT-${version} bin data doc graphsurgeon include lib python samples targets TensorRT-Release-Notes.pdf uff Add the absolute path to the TensorRTlib directory to the environment variable LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib> -
docker 開發(fā)流程
１．安裝nvidia driver,請參考上面流程
２．安裝nvidia docker,請參考
https://github.com/NVIDIA/nvidia-docker3 . 下載 nvidia/docker相關 鏡像并編譯
3 . PKG-CONFIG 方式管理cuda 和tensorrt
1 . 請先理解pkg-config
https://zh.wikipedia.org/wiki/Pkg-config
2 . cuda.pc模板如下
cudaroot=/usr/local/cuda
libdir=${cudaroot}/lib64
includedir=${cudaroot}/include
Name: cuda
Description: CUDA Driver Library
Version: 10.0
Libs: -L${libdir} -lcublas -lcudart -lcurand -lcudnn
Cflags: -I${includedir}
3 . tensorrt.pc 模板如下
root=/home/gh/TensorRT-7.0.0.11
libdir=${root}/lib
includedir=${root}/include
includecommondir=${root}/include
Name: tensorrt
Description: tensorrt for c++
Version: 7.0.0.11
Libs: -L${libdir} -lnvcaffe_parser -lnvinfer -lnvparsers -lnvinfer_plugin -lnvonnxparser -lmyelin
Cflags: -I${includedir} -I${includecommondir}
4 . 模板放到PKG_CONFIG_PATH使用的地址止后。比方說ubuntu 地址為 /usr/local/lib/pkgconfig , centos 路徑為 /usr/local/lib64/pkgconfig
5 . CMakeLists.txt 使用
find_package(PkgConfig)
pkg_search_module(CUDA REQUIRED cuda)
include_directories(${CUDA_INCLUDE_DIRS})
link_directories(${CUDA_LIBRARY_DIRS})
pkg_search_module(TENSORRT REQUIRED tensorrt)
include_directories(${TENSORRT_INCLUDE_DIRS})
link_directories(${TENSORRT_LIBRARY_DIRS} )
