Can I install gcov tool in windows?
Or can I take plug-in of this tool in eclipse in windows?
I am very new to this tool. I don't have any idea about this tool. Please help me to get the information of this tool.
please tell me the requirements to run this tool on my machine.
Where can I get more information about gcov and also please provide a link from where I can download this tool.
Does gcov tool accepts only c/c++ files or it also takes header files(*.h) as input?
Can I install gcov tool in windows?
Yes. We use gcc to build QNX projects in Windows.
Where can I get more information about gcov and also pls provide a link from where I can download this tool.
gcov is a part gcc,
Does gcov tool accepts only c/c++ files or it also takes header files(*.h) as input?
gcov accepts cpp, and h too
gcov is part of the gcc tools. If you are building and linking with gcc on Windows (which is fairly unusual), you could use it. Since it is part of the compiler, you cannot add it as a plugin to your IDE, though there are probably plugins that allow you to interact with the data that it produces.
It sounds to me like you need to figure out what you are trying to do. gperftools or valgrind might be better suited to your needs.
You can use eclipse CDT. It has built in gcov plugin.
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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 ran clang++ -v testfile.cpp and found that many standard headers were missing from the directory C:\LLVM\lib\clang\3.9.0\include. I downloaded a pre-built binary of clang 3.9.0 for 32 bit windows from this link.
Can someone please help me sort out this mess and explain me why the standard libraries are missing in the pre-build version of clang? I've searched the web for hours to get the answer and solution to this problem but couldn't find one. Thanks in advance.
why the standard libraries are missing in the pre-build version of clang?
Your Windows binary download comprises only binary build tools
plus a handful of clang-specific headers because you are supposed
to use clang, on Windows, in lieu of another native compiler that provides your
standard library. Similarly if you install clang on Linux you'll build against
the GCC standard library by default.
Your internet search seemingly failed to lead you to Installing clang++ to compile and link on Windows, which
explains how to integrate clang with the mingw-w64 GCC standard library for 32- and/or 64-bit work
in the manner that clang for Windows expects and supports.
I just build clang on Windows following this. To make it kind of complete it seems the compiler lldb should be made also.
How do I build lldb with mingw? Or it should be build with clang?
The people who work on the Windows port of lldb use Visual Studio. The instructions for building lldb on Windows are here:
http://lldb.llvm.org/build.html#BuildingLldbOnWindows
Basically follow the build instructions for Linux:
http://lldb.llvm.org/build.html#BuildingLldbOnLinux
I recommend you to use ninja for windows instead of make (it's faster) but that's up to you.
Preferably use a mingw build of python. You find instructions on this here:
Build Python with Mingw and gcc
Alternatively you can use cmake for python build:
https://github.com/python-cmake-buildsystem/python-cmake-buildsystem
In the chapter "Building LLDB" itself use the latest stable source from llvm, cland and lldb and rename the folder as shown in the directory hierarchy image.
If you want to try building llvm and clang with clang, try to put a build of clang + llvm in your path (and avoid VC + gcc). In this case you might need a clang-build of python too. I did not try this out.
If you get build errors try to use the headers from predef.sf.net.
I am trying to build an OS image for TI OMAP4 Pandaboard. The downloaded BSP can be built but very limited without gcc g++ compiler. I think it much difficult to add the tool chain in QNX Momentics IDE, because there are so many files to be added. Can I manually modify the buildfile to do it? If possible, please give me an example. Thanks in advance.
No, it is not possible to run g++ on your TI OMAP4 Pandaboard (unless you build g++ from sources for the ARM platform using the existing QNX toolchain running on an X86 platform).
Why not possible: QNX releases their build tools only for X86-based hosts. The currently supported host OS-es include some variants of Windows, Linux and QNX but the precondition is that the host hardware is X86-based.
Likely you do not actually want to build your library on the target hardware; it should not matter where you actually do the build (except in very special cases where you build some source code based on user input, etc.)
What you need to do is build your library on your development host using the ARM toolchain (QCC if you want to use the high-level tools; ntoarmv7-g++ if you want to use the familiar g++ interface). Once you have your binary you can include it in the .ifs file. You just need to include a line in the .build file, similar to the following example:
/path/on/targetfs/yourbinary=/path/on/buildmachine/yourbinary
If your build environment is configured so that mkifs finds your binary then you can omit the "path/on/buildmachine" part.
If you are fine with having the binary on your target under /proc/boot then you can omit the "/path/on/targetfs/ part as well.
For ease of development it would usually be more convenient for you to store your binary on the SD card with a FAT filesystem. Then you can just copy your binary to the SD without having to rebuild the .ifs file.
Finally, once you get experienced you will want to export a part of your host-machine's filesystem via CIFS or NFS and mount it directly from your target. This will save all the trouble of having to copy files (and, possibly, reboot the target) in each build cycle. But this is far off from your original question.
I think you are trying to get the QNX C/C++ compiler to run on your target board. Correct?
If so, rather than installing the Runtime Kit, you install the QNX Software Development Platform and you should be good to go.
You can also use the System Builder to customize your QNX OS, but this is going to be harder than just using the QNX SDP.
One other note: QNX uses qcc for C and QCC for C++ instead of gcc. They both use gcc under the hood, but to compile on QNX, use qcc instead of gcc.
I would like to install the fsharp compiler from Github on my Debian system, and the usual way would be to create a deb package first and then install it (so it is possible to uninstall it later, etc.). What is the easiest way to achieve this? All the examples of how to use dh_make assume you have a source tar.gz appropriately named, whereas I don't. Also I need to use some prefix for the autogen script:
./autogen.sh --prefix=/usr
I am not sure it this makes the task any more difficult.
This should actually be fairly simple to achieve with a binary package - which will also be cross-platform because the F# compiler itself is written in F#. The compiler itself is fairly standalone and depends only on a few BCL libraries. There are versions that run on Mono.
More important than installing the compiler is the integration with your platform's build system(s). Microsoft ships a Microsoft.FSharp.targets file for MSBuild, I don't know whether that will work with Mono's xBuild.
I have put together a blog post that explains where to find the various bits that make up the F# compiler and how to package them to compile on a platform that has only .NET and MSBuild (AppHarbor in my case), which you may find helpful.