How to build and use Google TensorFlow C api

Question

I m really eager to start using Google s new Tensorflow library in C    The website and docs are just really unclear in terms of how to build the project s C   API and I don t know where to start    Can someone with more experience help by discovering and sharing a guide to using tensorflow s C   API

User · Answer

First  after installing protobuf and eigen  you d like to build Tensorflow     configure bazel build   tensorflow libtensorflow cc so   Then Copy the following include headers and dynamic shared library to  usr local lib and  usr local include   mkdir  usr local include tf cp -r bazel-genfiles   usr local include tf  cp -r tensorflow  usr local include tf  cp -r third party  usr local include tf  cp -r bazel-bin libtensorflow cc so  usr local lib    Lastly  compile using an example   g   -std c  11 -o tf example   -I usr local include tf   -I usr local include eigen3   -g -Wall -D DEBUG -Wshadow -Wno-sign-compare -w    -L usr local lib libtensorflow cc    pkg-config --cflags --libs protobuf  -ltensorflow cc tf example cpp

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answers above are good enough to show how to build the library  but how to collect the headers are still tricky  here I share the little script I use to copy the necessary headers    SOURCE is the first param  which is the tensorflow source build  direcoty  DST is the second param  which is the include directory holds the collected headers   eg  in cmake  include directories   collected headers here        bin bash  SOURCE  1 DST  2 echo  -- target dir is  DST  echo  -- source dir is  SOURCE   if    -e  DST    then     echo  clean  DST      rm -rf  DST     mkdir  DST fi     1  copy the source code c   api needs mkdir -p  DST tensorflow cp -r  SOURCE tensorflow core  DST tensorflow cp -r  SOURCE tensorflow cc  DST tensorflow cp -r  SOURCE tensorflow c  DST tensorflow    2  copy the generated code  put them back to   the right directories along side the source code if    -e  SOURCE bazel-genfiles tensorflow    then     prefix   SOURCE bazel-genfiles tensorflow      from   expr   echo -n  prefix   wc -m    1         eg  compiled protobuf files     find  SOURCE bazel-genfiles tensorflow -type f   while read line do          echo  procese file -- gt   line          line len   echo -n  line   wc -m          filename   echo  line   rev   cut -d    -f1   rev           filename len   echo -n  filename   wc -m          to   expr  line len -  filename len           target dir   echo  line   cut -c from- to           echo    filename  copy  line  DST tensorflow  target dir          cp  line  DST tensorflow  target dir     done fi     3  copy third party files  Why    In the tf source code  you can see  include  third party       so you need it cp -r  SOURCE third party  DST    4  these headers are enough for me now    if your compiler complains missing headers  maybe you can find it in bazel-tensorflow external cp -RLf  SOURCE bazel-tensorflow external eigen archive Eigen  DST cp -RLf  SOURCE bazel-tensorflow external eigen archive unsupported  DST cp -RLf  SOURCE bazel-tensorflow external protobuf archive src google  DST cp -RLf  SOURCE bazel-tensorflow external com google absl absl  DST

User · Answer

One alternative to using Tensorflow C   API I found is to use cppflow    It s a lightweight C   wrapper around Tensorflow C API  You get very small executables and it links against the libtensorflow so already compiled file  There are also examples of use and you use CMAKE instead of Bazel

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If you are thinking into using Tensorflow c   api on a standalone package you probably will need tensorflow cc so   There is also a c api version tensorflow so   to build the c   version you can use   bazel build -c opt   tensorflow libtensorflow cc so   Note1  If you want to add intrinsics support you can add this flags as  --copt -msse4 2 --copt -mavx  Note2  If you are thinking into using OpenCV on your project as well  there is an issue when using both libs together  tensorflow issue  and you should use --config monolithic   After building the library you need to add it to your project  To do that you can include this paths   tensorflow tensorflow bazel-tensorflow external eigen archive tensorflow bazel-tensorflow external protobuf archive src tensorflow bazel-genfiles   And link the library to your project   tensorflow bazel-bin tensorflow libtensorflow framework so  unused if you build with --config monolithic  tensorflow bazel-bin tensorflow libtensorflow cc so   And when you are building your project you should also specify to your compiler that you are going to use c  11 standards   Side Note  Paths relative to tensorflow version 1 5  You may need to check if in your version anything changed    Also this link helped me a lot into finding all this infos  link

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Tensorflow itself only provides very basic examples about C   APIs  Here is a good resource which includes examples of datasets  rnn  lstm  cnn and more  tensorflow c   examples

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I use a hack workaround to avoid having to build the whole TF library myself  which saves both time  it s set up in 3 minutes   disk space  installing dev dependencies  and size of the resulting binary   It s officially unsupported  but works well if you just want to quickly jump in   Install TF through pip  pip install tensorflow or pip install tensorflow-gpu   Then find its library  pywrap tensorflow so  TF 0   - 1 0  or  pywrap tensorflow internal so  TF 1 1    In my case  Ubuntu  it s located at  usr local lib python2 7 dist-packages tensorflow python  pywrap tensorflow so  Then create a symlink to this library called lib pywrap tensorflow so somewhere where your build system finds it  e g   usr lib local   The prefix lib is important  You can also give it another lib  so name - if you call it libtensorflow so  you may get better compatibility with other programs written to work with TF   Then create a C   project as you are used to  CMake  Make  Bazel  whatever you like     And then you re ready to just link against this library to have TF available for your projects  and you also have to link against python2 7 libraries   In CMake  you e g  just add target link libraries target  pywrap tensorflow python2 7    The C   header files are located around this library  e g  in  usr local lib python2 7 dist-packages tensorflow include    Once again  this way is officially unsupported and you may run in various issues  The library seems to be statically linked against e g  protobuf  so you may run in odd link-time or run-time issues  But I am able to load a stored graph  restore the weights and run inference  which is IMO the most wanted functionality in C

User · Answer

If you wish to avoid both building your projects with Bazel and generating a large binary  I have assembled a repository instructing the usage of the TensorFlow C   library with CMake   You can find it here   The general ideas are as follows    Clone the TensorFlow repository  Add a build rule to tensorflow BUILD  the provided ones do not include all of the C   functionality   Build the TensorFlow shared library  Install specific versions of Eigen and Protobuf  or add them as external dependencies  Configure your CMake project to use the TensorFlow library

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If you don t mind using CMake  there is also tensorflow cc project that builds and installs TF C   API for you  along with convenient CMake targets you can link against  The project README contains an example and Dockerfiles you can easily follow

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If you don t want to build Tensorflow yourself and your operating system is Debian or Ubuntu  you can download prebuilt packages with the Tensorflow C C   libraries  This distribution can be used for C C   inference with CPU  GPU support is not included   https   github com kecsap tensorflow cpp packaging releases  There are instructions written how to freeze a checkpoint in Tensorflow  TFLearn  and load this model for inference with the C C   API   https   github com kecsap tensorflow cpp packaging blob master README md  Beware  I am the developer of this Github project

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To add to  mrry s post  I put together a tutorial that explains how to load a TensorFlow graph with the C   API  It s very minimal and should help you understand how all of the pieces fit together  Here s the meat of it   Requirements    Bazel installed Clone TensorFlow repo   Folder structure    tensorflow tensorflow  project name   tensorflow tensorflow  project name   project name  cc  e g  https   gist github com jimfleming 4202e529042c401b17b7  tensorflow tensorflow  project name  BUILD   BUILD   cc binary      name     lt project name gt        srcs      lt project name gt  cc        deps                tensorflow core tensorflow             Two caveats for which there are probably workarounds    Right now  building things needs to happen within the TensorFlow repo  The compiled binary is huge  103MB     https   medium com  jimfleming loading-a-tensorflow-graph-with-the-c-api-4caaff88463f

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To get started  you should download the source code from Github  by following the instructions here  you ll need Bazel and a recent version of GCC    The C   API  and the backend of the system  is in tensorflow core  Right now  only the C   Session interface  and the C API are being supported  You can use either of these to execute TensorFlow graphs that have been built using the Python API and serialized to a GraphDef protocol buffer  There is also an experimental feature for building graphs in C    but this is currently not quite as full-featured as the Python API  e g  no support for auto-differentiation at present   You can see an example program that builds a small graph in C   here   The second part of the C   API is the API for adding a new OpKernel  which is the class containing implementations of numerical kernels for CPU and GPU  There are numerous examples of how to build these in tensorflow core kernels  as well as a tutorial for adding a new op in C

User · Answer

You can use this ShellScript to install  most  of it s dependencies  clone  build  compile and get all the necessary files into    src includes folder   https   github com node-tensorflow node-tensorflow blob master tools install sh

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