Compiling C/C + + interface of TensorFlow

Keywords: C++ Makefile Python sudo Session

TensorFlow's Python interface is popular because of its convenience and practicability, but we may need other programming language interfaces in practical application. This article will introduce how to compile TensorFlow's C/C + + interface.

Installation environment:
Ubuntu 16.04
Python 3.5
CUDA 9.0
cuDNN 7
Bazel 0.17.2
TensorFlow 1.11.0

1. Install Bazel

Install JDK sudo apt get install openjdk-8-jdk
Add Bazel software source

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 -

Install and update bazel sudo apt get update & & sudo apt get install bazel
Click here to see the official Bazel installation guide

2. Compile TensorFlow Library

Click here to download TensorFlow source code
Enter the source root directory and run. / configure to configure. May refer to Official website - > build from source - > view sample configuration session Settings, mainly Python path, CUDA and CUDNN version and path, and the computing power of graphics card Click here to view . The following is my configuration process for reference only.

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

Enter tensorflow directory for compilation. After the compilation is successful, the file libtensorflow · cc.so will appear in / bazel bin / tensorflow directory

Version C: bazel build: libtensorflow.so
 C + + version: bazel build: libtensorflow? Cc.so

3. Compile other dependencies

Enter the tensorflow/contrib/makefile directory, run. / build · all · linux.sh, and a gen folder will appear after success
If the following error occurs / autogen.sh: 4: autoreconf: not found, install the corresponding dependency to sudo apt get install Autoconf automake libtool

4. test

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
        )

Create reply

#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;
}

View TensorFlow version

#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

If you are prompted that some header files are missing, search the specific path in the root directory of tensorflow, and then add them to the Cmakelist.

For more highlights, please pay attention to "seniusen"!

Posted by - - NC - - on Sun, 15 Dec 2019 08:49:31 -0800

Programmer Group

Compiling C/C + + interface of TensorFlow

1. Install Bazel

2. Compile TensorFlow Library

3. Compile other dependencies

4. test

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