编译 TensorFlow 的 C/C++ 接口

2023-08-11 22:12:22

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 -

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

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

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

        )

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;

}

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

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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码农公寓

1. 安装 Bazel

2. 编译 TensorFlow 库

3. 编译其他依赖

4. 测试

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