I'm interested in incorporating TensorFlow into a C++ server application built in Visual Studio on Windows 10 and I need to know if that's possible.
Google recently announced Windows support for TensorFlow: https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html
but from what I can tell this is just a pip install for the more commonly used Python package, and to use the C++ API you need to build the repo from source yourself: How to build and use Google TensorFlow C++ api
I tried building the project myself using bazel, but ran into issues trying to configure the build.
Is there a way to get TensorFlow C++ to work in native Windows (not using Docker or the new Windows 10 Linux subsystem, as I've seen others post about)?
Thanks,
Ian
It is certainly possible to use TensorFlow's C++ API on Windows, but it is not currently very easy. Right now, the easiest way to build against the C++ API on Windows would be to build with CMake, and adapt the CMake rules for the tf_tutorials_example_trainer project (see the source code here). Building with CMake will give you a Visual Studio project in which you can implement your C++ TensorFlow program.
Note that the tf_tutorials_example_trainer project builds a Console Application that statically links all of the TensorFlow runtime into your program. At present we have not written the necessary rules to create a reusable TensorFlow DLL, although this would be technially possible: for example, the Python extension is a DLL that includes the runtime, but does not export the necessary symbols to use TensorFlow's C or C++ APIs directly.
There is a detailed guide by Joe Antognini and a similar TensorFlow ReadMe at GitHub explaining the building of TensorFlow source via CMake. You also need to have SWIG installed on your machine which allows connecting C/C++ source with the Python scripting language. I did use Visual CMAKE (cmake-gui) with the screen capture shown below.
In the CMake configuration, I used Visual Studio 15 2017 compiler. Once this stage successfully completes, you can click on the Generate button to go ahead with the actual build process.
However, on Visual Studio 2015, when I attempted building via the "ALL_BUILD" project, the setup gave me "build tools for v141 cannot be found" error. This did not go away even when I attempted to retarget my solution. Finally, the solution got built successfully with Visual Studio 2017. You also need to manually set the SWIG_EXECUTABLE path in CMake before it successfully configures.
As indicated in the Antognini link, for me the build took about half an hour on a 16GB RAM, Core i7 machine. Once done, you might want to validate your build by attempting to run the tf_tutorials_example_trainer.exe file.
Hope this helps!
For our latest work on building TensorFlow C++ API on Windows, please look at this github page. This works on Windows 10, currently without CUDA support (only CPU).
PS:
Only the bazel build method works, because CMake is not supported and not maintained anymore, resulting in CMake configuration errors.
I had to use a downgraded version of my Visual Studio 2017 (from 15.7.5 to 15.4) by adding "VC++ 2017 version 15.4 v14.11 toolset" through the installer (Individual Components tab).
The cmake command which worked for me was:
cmake .. -A x64 -DCMAKE_BUILD_TYPE=Release ^
-T "v141,version=14.11" ^
-DSWIG_EXECUTABLE="C:/Program Files/swigwin-3.0.12/swig.exe" ^
-DPYTHON_EXECUTABLE="C:/Program Files/Python/python.exe" ^
-DPYTHON_LIBRARIES="C:/Program Files/Python/libs/python27.lib" ^
-Dtensorflow_ENABLE_GPU=ON ^
-DCUDNN_HOME="C:/Program Files/cudnn-9.2-windows10-x64-v7.1/cuda" ^
-DCUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0"
After the build, open tensorflow.sln in Visual Studio and build ALL_BUILD.
If you want to enable GPU computation, do check your Graphics Card here (Compute Capability > 3.5). Do remember to install all the packages (Cuda Toolkit 9.0, cuDNN, Python 3.7, SWIG, Git, CMake...) and add the paths to the environment variable in the beginning.
I made a README detailing how to I built the Tensorflow dll and .lib file for the C++ API on Windows with GPU support building from source with Bazel. Tensorflow version 1.14
The tutorial is step by step and starts at the very beginning, so you may have to scroll down past steps you have already done, like checking your hardware, installing Bazel etc.
Here is the url: https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows
Probably you will want to scroll all the way down to this part:
https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows#step-7-build-the-dll
It shows how to pass command to create .lib and .dll.
Then to test your .lib you should link it into your c++ project,
Then it will show you how to identify and fix the missing symbols using the TF_EXPORT macro
I am actively working on making this tutorial better so feel free to leave comments on this answer if you are having problems.
I recently use the pre-built OpenCV 3.1.0 on Windows which was downloaded from here. Actually, I followed the official installation.
The thing is that I find that the VideoCapture module of pre-built OpenCV processes video very slowly. It seems that it has no support of ffmpeg. And I find the official note:
To use the OpenCV library you have two options: Installation by Using the Pre-built Libraries or Installation by Making Your Own Libraries from the Source Files . While the first one is easier to complete, it only works if you are coding with the latest Microsoft Visual Studio IDE and doesn't take advantage of the most advanced technologies we integrate into our library.
It makes me curious about what is the actual build configuration of pre-built OpenCV 3.1.0 (or other versions) on Windows. No supports of TBB, IPP, Eigen, CUDA, etc...? I didn't find any clue on the internet. Anyone knows?
I'm trying (with no success) to cross-compile OpenCV on a embedded board.
I followed this: http://docs.opencv.org/doc/tutorials/introduction/crosscompilation/arm_crosscompile_with_cmake.html.
To summarize I installed the cross compilation tools for ARM, the I ran the cmake (providing the proper toolchain file), the make and the make install commands.
I next copied the lib/ include and bin/ directories with the opencv installation in the embedded board.
However, when I try to compile a simple hello world with opencv I get undefined reference to __gnu_thumb1_case_uqi and other similar symbols.
Does anybody faced this problem and know how to solve it?
Alternative approach
Just take some embedded distro like Buildroot, OpenEmbedded or Debian. They all provide OpenCV.
As I'm mostly experienced in Buildroot, I'd like to point to the newly introduced feature to keep your project separate from BR: http://nightly.buildroot.org/manual.html#outside-br-custom. This will give you a main idea on how to compile your software against BR's OpenCV.
I have created a HOG based human detection code using OpenCV. While the code is runs well on my Windows 7 system, I now need to port this on a WinCE07 platform. How do I compile OpenCV for WinCE07 platform.
OpenCV site has instruction to build OpenCV from source, see "Installation in Windows - Installation by Making Your Own Libraries from the Source Files".
Basically you need:
A suitable development environment for Windows CE (some years ago the environment was eMbedded Visual C++ 4.0, I do not know what Microsoft has now).
The source code of OpenCV.
CMake, it is the program for managing the build. CMake will create the projects and solutions files. Usually CMake automatically recognize your development environment.
sir,
I have tried my level best to install open cv library 2.2.0 version.but it has'nt suceeded
it shows that errors in linking the library hughigh,
whether we wanted to insatll the ipp library prior to install the opencv?
please help me ?..............
I can only recommend the install advice on the OpenCV page. If you are using a unix download the source of the library you want to install and then use cmake to install the library. That usually works fine for me.
Try installing the 1.1 version of OpenCV.
The 2.x version is brand new and as of Nov 1 2009 you will have difficulty finding documentation for that. The 1.1 version of OpenCV, on the other hand, is very well documented and you should have no trouble finding online tutorials for your platform that walk you through the installation process step-by-step.
As an aside: "IPP" refers to Intel's Performance Primitives. In the 1.1 version these are entirely optional. OpenCV does not require them. If you have the Performance Primitives installed, however, your OpenCV code may run faster. (For me it cut down my image processing time by a factor of five.) Once you get everything up and running you can purchase the IPP library from intel here: http://software.intel.com/en-us/intel-ipp/