編譯 TensorFlow 的 C/C++ 接口
阿新 • • 發佈：2018-10-08
cout 一個 tensor 軟件源 amd flow 目錄 enabled 精彩
TensorFlow 的 Python 接口由於其方便性和實用性而大受歡迎，但實際應用中我們可能還需要其它編程語言的接口，本文將介紹如何編譯 TensorFlow 的 C/C++ 接口。
安裝環境：
Ubuntu 16.04
Python 3.5
CUDA 9.0
cuDNN 7
Bazel 0.17.2
TensorFlow 1.11.0
1. 安裝 Bazel
安裝 JDK
sudo apt-get install openjdk-8-jdk- 添加 Bazel 軟件源
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 -
安裝並更新 Bazel
sudo apt-get update && sudo apt-get install bazel點此查看 Bazel 官方安裝指南
2. 編譯 TensorFlow 庫
點此下載 TensorFlow 源碼
- 進入源碼根目錄，運行
./configure進行配置。可參考 官網 -> Build from source -> View sample configuration session 設置，主要是 Python 的路徑、CUDA 和 CUDNN 的版本和路徑以及顯卡的計算能力 可點此查看 。以下是我的配置過程，僅供參考。
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
- 進入 tensorflow 目錄進行編譯，編譯成功後，在 /bazel-bin/tensorflow 目錄下會出現 libtensorflow_cc.so 文件
C版本： bazel build :libtensorflow.so
C++版本： bazel build :libtensorflow_cc.so3. 編譯其他依賴
進入 tensorflow/contrib/makefile 目錄下，運行
./build_all_linux.sh，成功後會出現一個gen文件夾若出現如下錯誤 /autogen.sh: 4: autoreconf: not found ，安裝相應依賴即可
sudo apt-get install autoconf automake libtool
4. 測試
- 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
)- 創建回話
#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- 若提示缺少某些頭文件則在 tensorflow 根目錄下搜索具體路徑，然後添加到 Cmakelist 裏面即可。
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編譯 TensorFlow 的 C/C++ 接口