梧州论坛最新消息,门户网站优化怎么做,天津进口网站建设电话,做网站 服务器多少钱一年文章目录 一、Ubuntu18.04环境配置1.1 安装工具链和opencv1.2 安装Nvidia相关库1.2.1 安装Nvidia显卡驱动1.2.2 安装 cuda11.31.2.3 安装 cudnn8.21.2.4 下载 tensorrt8.4.2.4 二、编写CMakeLists.txt三、TensorRT系列教程 一、Ubuntu18.04环境配置
教程同样适用与ubuntu22.04… 文章目录 一、Ubuntu18.04环境配置1.1 安装工具链和opencv1.2 安装Nvidia相关库1.2.1 安装Nvidia显卡驱动1.2.2 安装 cuda11.31.2.3 安装 cudnn8.21.2.4 下载 tensorrt8.4.2.4 二、编写CMakeLists.txt三、TensorRT系列教程 一、Ubuntu18.04环境配置
教程同样适用与ubuntu22.04、ubuntu20.04。如果您对tensorrt不是很熟悉请务必保持下面库版本一致。请注意Linux系统安装以下库务必去进入系统bios下关闭安全启动(设置 secure boot 为 disable)。tensorrt依赖cuda、cudnn本文也会给出安装办法顺便opencv的安装方法也给了。最后也会分享如何在书写cmakelists文件以便在项目中使用tensorrt。
1.1 安装工具链和opencv
sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install git
sudo apt-get install gdb
sudo apt-get install cmakesudo apt-get install libopencv-dev
# pkg-config --modversion opencv1.2 安装Nvidia相关库
注Nvidia相关网站需要注册账号。
1.2.1 安装Nvidia显卡驱动
ubuntu-drivers devices
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-driver-470-server # for ubuntu18.04
nvidia-smi1.2.2 安装 cuda11.3
进入链接: https://developer.nvidia.com/cuda-toolkit-archive选择CUDA Toolkit 11.3.0(April 2021)选择[Linux] - [x86_64] - [Ubuntu] - [18.04] - [runfile(local)] 在网页你能看到下面安装命令我这里已经拷贝下来
wget https://developer.download.nvidia.com/compute/cuda/11.3.0/local_installers/cuda_11.3.0_465.19.01_linux.run
sudo sh cuda_11.3.0_465.19.01_linux.runcuda的安装过程中需要你在bash窗口手动作一些选择这里选择如下
select[continue] - [accept] - 接着按下回车键取消Driver和465.19.01这个选项如下图(it is important!) - [Install] bash窗口提示如下表示安装完成
#
# Summary
##Driver: Not Selected
#Toolkit: Installed in /usr/local/cuda-11.3/
#......把cuda添加到环境变量
vim ~/.bashrc把下面拷贝到 .bashrc里面
# cuda v11.3
export PATH/usr/local/cuda-11.3/bin${PATH::${PATH}}
export LD_LIBRARY_PATH/usr/local/cuda-11.3/lib64${LD_LIBRARY_PATH::${LD_LIBRARY_PATH}}
export CUDA_HOME/usr/local/cuda-11.3刷新环境变量和验证
source ~/.bashrc
nvcc -Vbash窗口打印如下信息表示cuda11.3安装正常
nvcc: NVIDIA (R) Cuda compiler driverbr
Copyright (c) 2005-2021 NVIDIA Corporationbr
Built on Sun_Mar_21_19:15:46_PDT_2021br
Cuda compilation tools, release 11.3, V11.3.58br
Build cuda_11.3.r11.3/compiler.29745058_0br1.2.3 安装 cudnn8.2
进入网站https://developer.nvidia.com/rdp/cudnn-archive选择 Download cuDNN v8.2.0 (April 23rd, 2021), for CUDA 11.x选择 cuDNN Library for Linux (x86_64)你将会下载这个压缩包: “cudnn-11.3-linux-x64-v8.2.0.53.tgz”
# 解压
tar -zxvf cudnn-11.3-linux-x64-v8.2.0.53.tgz将cudnn的头文件和lib拷贝到cuda11.3的安装目录下
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod ar /usr/local/cuda/include/cudnn.h
sudo chmod ar /usr/local/cuda/lib64/libcudnn*1.2.4 下载 tensorrt8.4.2.4
本教程中tensorrt只需要下载\、解压即可不需要安装。
进入网站 https://developer.nvidia.cn/nvidia-tensorrt-8x-download 网站更新2023.12https://developer.nvidia.com/nvidia-tensorrt-8x-download 顺便法克 Nvidia把这个打勾 I Agree To the Terms of the NVIDIA TensorRT License Agreement选择: TensorRT 8.4 GA Update 1选择: TensorRT 8.4 GA Update 1 for Linux x86_64 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6 and 11.7 TAR Package你将会下载这个压缩包: “TensorRT-8.4.2.4.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz”
# 解压
tar -zxvf TensorRT-8.4.2.4.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz
# 快速验证一下tensorrtcudacudnn是否安装正常
cd TensorRT-8.4.2.4/samples/sampleMNIST
make
cd ../../bin/导出tensorrt环境变量(it is important!)注将LD_LIBRARY_PATH:后面的路径换成你自己的后续编译onnx模型的时候也需要执行下面第一行命令
export LD_LIBRARY_PATH$LD_LIBRARY_PATH:/home/xxx/temp/TensorRT-8.4.2.4/lib
./sample_mnistbash窗口打印类似如下图的手写数字识别表明cudacudnntensorrt安装正常
二、编写CMakeLists.txt
由于tensorrt依赖cuda cudnn所以我们先新建common.cmake文件如下并在文件中声明相关库的头文件、lib路径等。
# set
set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -Wno-deprecated-declarations)
# find thirdparty
find_package(CUDA REQUIRED)
list(APPEND ALL_LIBS ${CUDA_LIBRARIES} ${CUDA_cublas_LIBRARY} ${CUDA_nppc_LIBRARY} ${CUDA_nppig_LIBRARY} ${CUDA_nppidei_LIBRARY} ${CUDA_nppial_LIBRARY})# include cudas header
list(APPEND INCLUDE_DRIS ${CUDA_INCLUDE_DIRS})set(TensorRT_ROOT /home/xxxxxx/TensorRT-8.4.2.4)find_library(TRT_NVINFER NAMES nvinfer HINTS ${TensorRT_ROOT} PATH_SUFFIXES lib lib64 lib/x64)
find_library(TRT_NVINFER_PLUGIN NAMES nvinfer_plugin HINTS ${TensorRT_ROOT} PATH_SUFFIXES lib lib64 lib/x64)
find_library(TRT_NVONNX_PARSER NAMES nvonnxparser HINTS ${TensorRT_ROOT} PATH_SUFFIXES lib lib64 lib/x64)
find_library(TRT_NVCAFFE_PARSER NAMES nvcaffe_parser HINTS ${TensorRT_ROOT} PATH_SUFFIXES lib lib64 lib/x64)
find_path(TENSORRT_INCLUDE_DIR NAMES NvInfer.h HINTS ${TensorRT_ROOT} PATH_SUFFIXES include)
list(APPEND ALL_LIBS ${TRT_NVINFER} ${TRT_NVINFER_PLUGIN} ${TRT_NVONNX_PARSER} ${TRT_NVCAFFE_PARSER})# include tensorrts headers
list(APPEND INCLUDE_DRIS ${TENSORRT_INCLUDE_DIR})set(SAMPLES_COMMON_DIR ${TensorRT_ROOT}/samples/common)
list(APPEND INCLUDE_DRIS ${SAMPLES_COMMON_DIR})
message(STATUS ***INCLUDE_DRIS*** ${INCLUDE_DRIS})
message(STATUS ALL_LIBS: ${ALL_LIBS})
有一点需要特别注意上述文件中set(TensorRT_ROOT /home/xxxxxx/TensorRT-8.4.2.4)表示设置tensorrt的路径你修改为自己的实际路径就行下面再新建CMakeLists.txt文件在第5行文件中会包含上述common.cmake文件你根据自己实际情况修改路径。 这样就能将app_yolov8.cpp和一堆其他的.cpp和.cu文件包含进工程其中main函数在app_yolov8.cpp中。
cmake_minimum_required(VERSION 3.10)
set(CMAKE_BUILD_TYPE Debug)
#set(CMAKE_BUILD_TYPE Release)
PROJECT(yolov8 VERSION 1.0.0 LANGUAGES C CXX CUDA)
include(${CMAKE_CURRENT_SOURCE_DIR}/../cmake/common.cmake)
message(STATUS ${ALL_LIBS})
file(GLOB CPPS ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
list(REMOVE_ITEM CPPS app_yolov8.cpp)
list (LENGTH CPPS length)
find_package(OpenCV REQUIRED)
include_directories(${INCLUDE_DRIS} ${OpenCV_INCLUDE_DIRS} ${CMAKE_CURRENT_SOURCE_DIR})add_library(${PROJECT_NAME} SHARED ${CPPS})
target_link_libraries(${PROJECT_NAME} ${ALL_LIBS} ${OpenCV_LIBRARIES})set_property(TARGET ${PROJECT_NAME} PROPERTY CUDA_ARCHITECTURES 50 61 72 75)
target_compile_options(${PROJECT_NAME} PUBLIC $$COMPILE_LANGUAGE:CUDA:--default-stream per-thread -lineinfo; --use_fast_math --disable-warnings)add_executable(app_yolov8 app_yolov8.cpp)# NVCC
# target_link_libraries(detect ${PROJECT_NAME} ${ALL_LIBS} ${OpenCV_LIBRARIES} libgflags_nothreads.a)
target_link_libraries(app_yolov8 ${PROJECT_NAME} ${ALL_LIBS} ${OpenCV_LIBRARIES} )
上述的两个文件分别参考 common.cmake https://github.com/FeiYull/TensorRT-Alpha/blob/main/cmake/common.cmake CMakeLists.txt https://github.com/FeiYull/TensorRT-Alpha/blob/main/yolov8/CMakeLists.txt
三、TensorRT系列教程
TensorRT系列教程
