编译 TensorFlow 的 C/C++ 接口

TensorFlow 的 Python 接口由于其方便性和实用性而大受欢迎，但实际应用中我们可能还需要其它编程语言的接口，本文将介绍如何编译 TensorFlow 的 C/C++ 接口。

安装环境： Ubuntu 16.04 Python 3.5 CUDA 9.0 cuDNN 7 Bazel 0.17.2 TensorFlow 1.11.0

1. 安装 Bazel

• 安装 JDK sudo apt-get install openjdk-8-jdk

• 添加 Bazel 软件源

echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
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• 安装并更新 Bazel sudo apt-get update && sudo apt-get install bazel

• 点此查看 Bazel 官方安装指南

2. 编译 TensorFlow 库

• 点此下载 TensorFlow 源码

• 进入源码根目录，运行 ./configure 进行配置。可参考 官网 -> Build from source -> View sample configuration session 设置，主要是 Python 的路径、CUDA 和 CUDNN 的版本和路径以及显卡的计算能力可点此查看 。以下是我的配置过程，仅供参考。

You have bazel 0.17.2 installed.
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3.5


Found possible Python library paths:
  /usr/local/lib/python3.5/dist-packages
  /usr/lib/python3/dist-packages
Please input the desired Python library path to use.  Default is [/usr/local/lib/python3.5/dist-packages]

Do you wish to build TensorFlow with Apache Ignite support? [Y/n]: n
No Apache Ignite support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 


Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 


Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 


Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 


Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.

Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]: 


Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1]: 


Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: 


Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
	--config=mkl         	# Build with MKL support.
	--config=monolithic  	# Config for mostly static monolithic build.
	--config=gdr         	# Build with GDR support.
	--config=verbs       	# Build with libverbs support.
	--config=ngraph      	# Build with Intel nGraph support.
Configuration finished
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• 进入 tensorflow 目录进行编译，编译成功后，在 /bazel-bin/tensorflow 目录下会出现 libtensorflow_cc.so 文件

C版本： bazel build :libtensorflow.so
C++版本： bazel build :libtensorflow_cc.so
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3. 编译其他依赖

• 进入 tensorflow/contrib/makefile 目录下，运行 ./build_all_linux.sh ，成功后会出现一个gen文件夹

• 若出现如下错误 /autogen.sh: 4: autoreconf: not found ，安装相应依赖即可 sudo apt-get install autoconf automake libtool

4. 测试

• Cmaklist.txt

cmake_minimum_required(VERSION 3.8)
project(Tensorflow_test)

set(CMAKE_CXX_STANDARD 11)

set(SOURCE_FILES main.cpp)


include_directories(
        /media/lab/data/yongsen/tensorflow-master
        /media/lab/data/yongsen/tensorflow-master/tensorflow/bazel-genfiles
        /media/lab/data/yongsen/tensorflow-master/tensorflow/contrib/makefile/gen/protobuf/include
        /media/lab/data/yongsen/tensorflow-master/tensorflow/contrib/makefile/gen/host_obj
        /media/lab/data/yongsen/tensorflow-master/tensorflow/contrib/makefile/gen/proto
        /media/lab/data/yongsen/tensorflow-master/tensorflow/contrib/makefile/downloads/nsync/public
        /media/lab/data/yongsen/tensorflow-master/tensorflow/contrib/makefile/downloads/eigen
        /media/lab/data/yongsen/tensorflow-master/bazel-out/local_linux-py3-opt/genfiles
        /media/lab/data/yongsen/tensorflow-master/tensorflow/contrib/makefile/downloads/absl
)

add_executable(Tensorflow_test ${SOURCE_FILES})

target_link_libraries(Tensorflow_test
        /media/lab/data/yongsen/tensorflow-master/bazel-bin/tensorflow/libtensorflow_cc.so
        /media/lab/data/yongsen/tensorflow-master/bazel-bin/tensorflow/libtensorflow_framework.so
        )
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• 创建回话

#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>
#include <iostream>

using namespace std;
using namespace tensorflow;

int main()
{
    Session* session;
    Status status = NewSession(SessionOptions(), &session);
    if (!status.ok()) {
        cout << status.ToString() << "\n";
        return 1;
    }
    cout << "Session successfully created.\n";
    return 0;
}
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• 查看 TensorFlow 版本

#include <iostream>
#include <tensorflow/c/c_api.h>

int main() {
   std:: cout << "Hello from TensorFlow C library version" << TF_Version();
    return 0;
}

// Hello from TensorFlow C library version1.11.0-rc1
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• 若提示缺少某些头文件则在 tensorflow 根目录下搜索具体路径，然后添加到 Cmakelist 里面即可。

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