path: root/tensorflow/contrib/cmake/README.md

TensorFlow CMake build
======================

CMAKE build is deprecated for TensorFlow. Please use `bazel` to build TF for all
platforms. For details, see the
[TensorFlow install guide](https://www.tensorflow.org/install/).

This directory contains CMake files for building TensorFlow on Microsoft
Windows. [CMake](https://cmake.org) is a cross-platform tool that can
generate build scripts for multiple build systems, including Microsoft
Visual Studio.

**N.B.** We provide Linux build instructions primarily for the purpose of
testing the build. We recommend using the standard Bazel-based build on
Linux.

Current Status
--------------

CMake can be used to build TensorFlow on Windows. See the [getting started documentation](https://www.tensorflow.org/install/source_windows)
for instructions on how to install a pre-built TensorFlow package on Windows.

### Current known limitations
* It is not possible to load a custom Op library.
* GCS file system is not supported.

## Building with CMake

The CMake files in this directory can build the core TensorFlow runtime, an
example C++ binary, and a PIP package containing the runtime and Python
bindings.

### Prerequisites

* CMake version 3.5 or later.

* [Git](https://git-scm.com)

* [SWIG](http://www.swig.org/download.html)

* Additional prerequisites for Microsoft Windows:
  - Visual Studio 2015
  - Python 3.5

* Additional prerequisites for Linux:
  - Python 2.7 or later
  - [Docker](https://www.docker.com/) (for automated testing)

* Python dependencies:
  - wheel
  - NumPy 1.11.0 or later

### Known-good configurations

* Microsoft Windows 10
  - Microsoft Visual Studio Enterprise 2015 with Visual C++ 2015
  - [Anaconda 4.1.1 (Python 3.5 64-bit)](https://www.anaconda.com/download/)
  - [Git for Windows version 2.9.2.windows.1](https://git-scm.com/download/win)
  - [swigwin-3.0.10](http://www.swig.org/download.html)
  - [NVidia CUDA Toolkit 8.0](https://developer.nvidia.com/cuda-downloads)
  - [NVidia CUDNN 5.1](https://developer.nvidia.com/cudnn)
  - [CMake 3.6](https://cmake.org/files/v3.6/cmake-3.6.3-win64-x64.msi)

* Ubuntu 14.04
  - Makefile generator
  - Docker 1.9.1 (for automated testing)

### Current known limitations
  - The Python package supports **Python 3.5 only**, because that is the only
    version for which standard Python binaries exist and those binaries are
    compatible with the TensorFlow runtime. (On Windows, the standard Python
    binaries for versions earlier than 3.5 were compiled with older compilers
    that do not have all of the features (e.g. C++11 support) needed to compile
    TensorFlow. We welcome patches for making TensorFlow work with Python 2.7
    on Windows, but have not yet committed to supporting that configuration.)

  - The following Python APIs are not currently implemented:
    * Loading custom op libraries via `tf.load_op_library()`. In order to use your
      custom op, please put the source code under the tensorflow/core/user_ops
      directory, and a shape function is required (not optional) for each op.
    * Path manipulation functions (such as `tf.gfile.ListDirectory()`) are not
      functional.

  - The `tf.contrib` libraries are not currently included in the PIP package.

  - The following operations are not currently implemented:
    * `DepthwiseConv2dNative`
    * `Digamma`
    * `Erf`
    * `Erfc`
    * `Igamma`
    * `Igammac`
    * `ImmutableConst`
    * `Lgamma`
    * `Polygamma`
    * `Zeta`

  - Google Cloud Storage support is not currently implemented. The GCS library
    currently depends on `libcurl` and `boringssl`, and the Windows version
    could use standard Windows APIs for making HTTP requests and cryptography
    (for OAuth). Contributions are welcome for this feature.

We are actively working on improving CMake and Windows support, and addressing
these limitations. We would appreciate pull requests that implement missing
ops or APIs.


Step-by-step Windows build
==========================

1.  Install the prerequisites detailed above, and set up your environment.

    *   When building with GPU support after installing the CUDNN zip file from
        NVidia, append its bin directory to your PATH environment variable. In
        case TensorFlow fails to find the CUDA dll's during initialization,
        check your PATH environment variable. It should contain the directory of
        the CUDA dlls and the directory of the CUDNN dll. For example:

        ```
        D:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin
        D:\local\cuda\bin
        ```

    *   When building with MKL support after installing
        [MKL](https://software.intel.com/en-us/mkl) from INTEL, append its bin
        directories to your PATH environment variable.

        In case TensorFlow fails to find the MKL dll's during initialization,
        check your PATH environment variable. It should contain the directory of
        the MKL dlls. For example:

        ```
        D:\Tools\IntelSWTools\compilers_and_libraries\windows\redist\intel64\mkl
        D:\Tools\IntelSWTools\compilers_and_libraries\windows\redist\intel64\compiler
        D:\Tools\IntelSWTools\compilers_and_libraries\windows\redist\intel64\tbb\vc_mt
        ```

    *   We assume that `cmake` and `git` are installed and in your `%PATH%`. If
        for example `cmake` is not in your path and it is installed in
        `C:\Program Files (x86)\CMake\bin\cmake.exe`, you can add this directory
        to your `%PATH%` as follows:

        ```
        D:\temp> set PATH="%PATH%;C:\Program Files (x86)\CMake\bin\cmake.exe"
        ```

2.  Clone the TensorFlow repository and create a working directory for your
    build:

    ```
    D:\temp> git clone https://github.com/tensorflow/tensorflow.git
    D:\temp> cd tensorflow\tensorflow\contrib\cmake
    D:\temp\tensorflow\tensorflow\contrib\cmake> mkdir build
    D:\temp\tensorflow\tensorflow\contrib\cmake> cd build
    D:\temp\tensorflow\tensorflow\contrib\cmake\build>
    ```

3.  Invoke CMake to create Visual Studio solution and project files.

    **N.B.** This assumes that `cmake.exe` is in your `%PATH%` environment
    variable. The other paths are for illustrative purposes only, and may be
    different on your platform. The `^` character is a line continuation and
    must be the last character on each line.

    ```
    D:\...\build> cmake .. -A x64 -Thost=x64 -DCMAKE_BUILD_TYPE=Release ^
    More? -DSWIG_EXECUTABLE=C:/tools/swigwin-3.0.10/swig.exe ^
    More? -DPYTHON_EXECUTABLE=C:/Users/%USERNAME%/AppData/Local/Continuum/Anaconda3/python.exe ^
    More? -DPYTHON_LIBRARIES=C:/Users/%USERNAME%/AppData/Local/Continuum/Anaconda3/libs/python35.lib
    ```

    To build with GPU support add "^" at the end of the last line above
    following with: `More? -Dtensorflow_ENABLE_GPU=ON ^ More?
    -DCUDNN_HOME="D:\...\cudnn"` To build with MKL support add "^" at the end of
    the last line above following with:

    ```
    More? -Dtensorflow_ENABLE_MKL_SUPPORT=ON ^
    More? -DMKL_HOME="D:\...\compilers_and_libraries"
    ```

    To enable SIMD instructions with MSVC, as AVX and SSE, define it as follows:

    ```
    More? -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX
    ```

    Note that the `-DCMAKE_BUILD_TYPE=Release` flag must match the build
    configuration that you choose when invoking `msbuild`. The known-good values
    are `Release` and `RelWithDebInfo`. The `Debug` build type is not currently
    supported, because it relies on a `Debug` library for Python
    (`python35d.lib`) that is not distributed by default.

    The `-Thost=x64` flag will ensure that the 64 bit compiler and linker is
    used when building. Without this flag, MSBuild will use the 32 bit toolchain
    which is prone to compile errors such as "compiler out of heap space".

    There are various options that can be specified when generating the solution
    and project files:

    *   `-DCMAKE_BUILD_TYPE=(Release|RelWithDebInfo)`: Note that the
        `CMAKE_BUILD_TYPE` option must match the build configuration that you
        choose when invoking MSBuild in step 4. The known-good values are
        `Release` and `RelWithDebInfo`. The `Debug` build type is not currently
        supported, because it relies on a `Debug` library for Python
        (`python35d.lib`) that is not distributed by default.

    *   `-Dtensorflow_BUILD_ALL_KERNELS=(ON|OFF)`. Defaults to `ON`. You can
        build a small subset of the kernels for a faster build by setting this
        option to `OFF`.

    *   `-Dtensorflow_BUILD_CC_EXAMPLE=(ON|OFF)`. Defaults to `ON`. Generate
        project files for a simple C++
        [example training program](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/tutorials/example_trainer.cc).

    *   `-Dtensorflow_BUILD_PYTHON_BINDINGS=(ON|OFF)`. Defaults to `ON`.
        Generate project files for building a PIP package containing the
        TensorFlow runtime and its Python bindings.

    *   `-Dtensorflow_ENABLE_GRPC_SUPPORT=(ON|OFF)`. Defaults to `ON`. Include
        gRPC support and the distributed client and server code in the
        TensorFlow runtime.

    *   `-Dtensorflow_ENABLE_SSL_SUPPORT=(ON|OFF)`. Defaults to `OFF`. Include
        SSL support (for making secure HTTP requests) in the TensorFlow runtime.
        This support is incomplete, and will be used for Google Cloud Storage
        support.

    *   `-Dtensorflow_ENABLE_GPU=(ON|OFF)`. Defaults to `OFF`. Include GPU
        support. If GPU is enabled you need to install the CUDA 8.0 Toolkit and
        CUDNN 5.1. CMake will expect the location of CUDNN in
        -DCUDNN_HOME=path_you_unzipped_cudnn.

    *   `-Dtensorflow_BUILD_CC_TESTS=(ON|OFF)`. Defaults to `OFF`. This builds
        cc unit tests. There are many of them and building will take a few
        hours. After cmake, build and execute the tests with `MSBuild
        /p:Configuration=RelWithDebInfo ALL_BUILD.vcxproj ctest -C
        RelWithDebInfo`

    *   `-Dtensorflow_BUILD_PYTHON_TESTS=(ON|OFF)`. Defaults to `OFF`. This
        enables python kernel tests. After building the python wheel, you need
        to install the new wheel before running the tests. To execute the tests,
        use `ctest -C RelWithDebInfo`

    *   `-Dtensorflow_BUILD_MORE_PYTHON_TESTS=(ON|OFF)`. Defaults to `OFF`. This
        enables python tests on serveral major packages. This option is only
        valid if this and tensorflow_BUILD_PYTHON_TESTS are both set as `ON`.
        After building the python wheel, you need to install the new wheel
        before running the tests. To execute the tests, use `ctest -C
        RelWithDebInfo`

    *   `-Dtensorflow_ENABLE_MKL_SUPPORT=(ON|OFF)`. Defaults to `OFF`. Include
        MKL support. If MKL is enabled you need to install the
        [Intel Math Kernal Library](https://software.intel.com/en-us/mkl). CMake
        will expect the location of MKL in -MKL_HOME=path_you_install_mkl.

    *   `-Dtensorflow_ENABLE_MKLDNN_SUPPORT=(ON|OFF)`. Defaults to `OFF`.
        Include MKL DNN support. MKL DNN is [Intel(R) Math Kernel Library for
        Deep Neural Networks (Intel(R)
        MKL-DNN)](https://github.com/intel/mkl-dnn). You have to add
        `-Dtensorflow_ENABLE_MKL_SUPPORT=ON` before including MKL DNN support.

4.  Invoke MSBuild to build TensorFlow.

    Set up the path to find MSbuild: `D:\temp> "C:\Program Files (x86)\Microsoft
    Visual Studio 14.0\VC\bin\amd64\vcvarsall.bat"`

    To build the C++ example program, which will be created as a `.exe`
    executable in the subdirectory `.\Release`:

    ```
    D:\...\build> MSBuild /p:Configuration=Release tf_tutorials_example_trainer.vcxproj
    D:\...\build> Release\tf_tutorials_example_trainer.exe
    ```

    To build the PIP package, which will be created as a `.whl` file in the
    subdirectory `.\tf_python\dist`:

    ```
    D:\...\build> MSBuild /p:Configuration=Release tf_python_build_pip_package.vcxproj
    ```

Linux Continuous Integration build
==================================

This build requires [Docker](https://www.docker.com/) to be installed on the
local machine.

```bash
$ git clone --recursive https://github.com/tensorflow/tensorflow.git
$ cd tensorflow
$ tensorflow/tools/ci_build/ci_build.sh CMAKE tensorflow/tools/ci_build/builds/cmake.sh
```

That's it. Dependencies included.