Tensorflow 本地编译

最新推荐文章于 2025-06-14 19:00:59 发布

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最新推荐文章于 2025-06-14 19:00:59 发布

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分类专栏： tensorflow 文章标签： tensroflow c++

本文链接：https://blog.youkuaiyun.com/weixin_43214624/article/details/87167237

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本文详细介绍了如何在CentOS环境下使用Bazel编译TensorFlow源码，并提供了C++调用TensorFlow的完整步骤与示例代码。从配置Bazel环境、安装依赖项，到编译TensorFlow并生成动态库，再到C++项目中引入TensorFlow，全程实战指导。

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编译流程

安装bazel (CentOS)

方法1

vim /etc/yum.repos.d/bazel.repo
拷贝下面内容

[vbatts-bazel]
name=Copr repo for bazel owned by vbatts
baseurl=https://copr-be.cloud.fedoraproject.org/results/vbatts/bazel/epel-7-$basearch/
type=rpm-md
skip_if_unavailable=True
gpgcheck=1
gpgkey=https://copr-be.cloud.fedoraproject.org/results/vbatts/bazel/pubkey.gpg
repo_gpgcheck=0
enabled=1
enabled_metadata=1

yum install bazel

方法2
到 https://github.com/bazelbuild/bazel/releases 下载对应的安装文件 bazel-0.28.1-installer-linux-x86_64.sh
在这里插入图片描述

编译

编译前查看机器支持的cpu指令集： gcc -march=native -Q --help=target|grep march
参考 https://tensorflow.google.cn/install/source 安装依赖项

git clone -b r1.14 https://github.com/tensorflow/tensorflow.git
cd tensorflow
./configure
bazel build --config=monolithic //tensorflow:libtensorflow_cc.so 或者 bazel build --config=opt //tensorflow:libtensorflow_cc.so 或者 bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse4.2 //tensorflow:libtensorflow_cc.so
sudo apt-get install autoconfig automake
cd tensorflow/contrib/makefile
./build_all_linux.sh

c++调用

c++需要加入include_directories的头文件：
./tensorflow/tensorflow/
./tensorflow/third_party/
./tensorflow/tensorflow/contrib/makefile/gen/protobuf/include/
./tensorflow/tensorflow/contrib/makefile/downloads/eigen/
./tensorflow/tensorflow/contrib/makefile/downloads/absl/
./tensorflow/bazel-genfiles
./tensorflow/tensorflow/contrib/makefile/gen/proto
./tensorflow/tensorflow/contrib/makefile/gen/host_obj/tensorflow/core
需要链接的so
./tensorflow/bazel-bin/tensorflow/libtensorflow_cc.so
cpp调用

#include <stdio.h>
#include <iostream>
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>
using namespace tensorflow;
using namespace std;
int main(int argc, char const *argv[])
{        
    std::cout << "status" << std::endl;
    Session* session;    
    Status status = NewSession(SessionOptions(), &session);    
    if (!status.ok()) 
    {
        std::cout << status.ToString() << "\n";
        return 0;
    }
    return 0;
}

cmake_minimum_required(VERSION 2.8)

project( tf_example )

IF(NOT CMAKE_BUILD_TYPE)
   SET(CMAKE_BUILD_TYPE Release)
ENDIF()

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -std=c++11 -W")

include_directories(
                   ${PROJECT_SOURCE_DIR}/include 
                   ${PROJECT_SOURCE_DIR}/include/include 
                   ${PROJECT_SOURCE_DIR}/include/tensorflow/ 
                   ${PROJECT_SOURCE_DIR}/include/bazel-genfiles/ 
                   ${PROJECT_SOURCE_DIR}/include/absl/
                   ${PROJECT_SOURCE_DIR}/include/eigen/
                  )

link_directories(${PROJECT_SOURCE_DIR}/3rdparty)

set(CODE_DIRS ${PROJECT_SOURCE_DIR}/src)

set(SOURCE_DIR ${CODE_DIRS}/lib)
set(TEST_DIR ${CODE_DIRS}/test)

set(EXECUTABLE_OUTPUT_PATH ${PROJECT_BINARY_DIR}/bin)
set(LIBRARY_OUTPUT_PATH ${PROJECT_BINARY_DIR}/lib)

add_subdirectory(${TEST_DIR} ${EXECUTABLE_OUTPUT_PATH})

项目git地址

https://github.com/zqp2009happy/tensorflow_c

一些有用的资料

官方手册 https://tensorflow.google.cn/guide/
cpp调用教程项目 https://github.com/rockingdingo/tensorflow-tutorial
官方模型 https://github.com/tensorflow/models
karas手册 https://keras.io/getting-started/functional-api-guide/