.. buildlb.rst: ###################### Build the C++ Library ###################### * :ref:`ubuntu` * :ref:`centos` Build Environments ================== Release |release| of |project| supports Linux\*-based systems with the following packages and prerequisites: .. csv-table:: :header: "Operating System", "Compiler", "Build System", "Status", "Additional Packages" :widths: 25, 15, 25, 20, 25 :escape: ~ CentOS 7.4 64-bit, GCC 4.8, CMake 3.4.3, supported, ``wget zlib-devel ncurses-libs ncurses-devel patch diffutils gcc-c++ make git perl-Data-Dumper`` Ubuntu 16.04 or 18.04 (LTS) 64-bit, Clang 3.9, CMake 3.5.1 + GNU Make, supported, ``build-essential cmake clang-3.9 clang-format-3.9 git curl zlib1g zlib1g-dev libtinfo-dev unzip autoconf automake libtool`` Clear Linux\* OS for Intel Architecture, Clang 5.0.1, CMake 3.10.2, experimental, bundles ``machine-learning-basic dev-utils python3-basic python-basic-dev`` Other configurations may work, but should be considered experimental with limited support. On Ubuntu 16.04 with gcc-5.4.0 or clang-3.9, for example, we recommend adding ``-DNGRAPH_USE_PREBUILT_LLVM=TRUE`` to the cmake command in step 4 below. This fetches a pre-built tarball of LLVM+Clang from llvm.org, and it will substantially reduce build time. If using ``gcc`` version 4.8, it may be necessary to add symlinks from ``gcc`` to ``gcc-4.8``, and from ``g++`` to ``g++-4.8``, in your :envvar:`PATH`, even if you explicitly specify the ``CMAKE_C_COMPILER`` and ``CMAKE_CXX_COMPILER`` flags when building. (**Do NOT** supply the ``-DNGRAPH_USE_PREBUILT_LLVM`` flag in this case, because the prebuilt tarball supplied on llvm.org is not compatible with a gcc 4.8-based build.) Installation Steps ================== .. important:: The default :program:`cmake` procedure (no build flags) will install ``ngraph_dist`` to an OS-level location like ``/usr/bin/ngraph_dist`` or ``/usr/lib/ngraph_dist``. Here we specify how to build locally to the location of ``~/ngraph_dist`` with the cmake target ``-DCMAKE_INSTALL_PREFIX=~/ngraph_dist``. All of the nGraph Library documentation presumes that ``ngraph_dist`` gets installed locally. The system location can be used just as easily by customizing paths on that system. See the :file:`ngraph/CMakeLists.txt` file to change or customize the default CMake procedure. .. _ubuntu: Ubuntu 16.04 ------------- The process documented here will work on Ubuntu\* 16.04 (LTS) or on Ubuntu 18.04 (LTS). #. (Optional) Create something like ``/opt/libraries`` and (with sudo), give ownership of that directory to your user. Creating such a placeholder can be useful if you'd like to have a local reference for APIs and documentation, or if you are a developer who wants to experiment with how to :doc:`../howto/execute` using resources available through the code base. .. code-block:: console $ sudo mkdir -p /opt/libraries $ sudo chown -R username:username /opt/libraries $ cd /opt/libraries #. Clone the `NervanaSystems` ``ngraph`` repo: .. code-block:: console $ git clone https://github.com/NervanaSystems/ngraph.git $ cd ngraph #. Create a build directory outside of the ``ngraph/src`` directory tree; somewhere like ``ngraph/build``, for example: .. code-block:: console $ mkdir build && cd build #. Generate the GNU Makefiles in the customary manner (from within the ``build`` directory). This command enables ONNX support in the library and sets the target build location at ``~/ngraph_dist``, where it can be found easily. .. code-block:: console $ cmake .. -DNGRAPH_ONNX_IMPORT_ENABLE=ON -DCMAKE_INSTALL_PREFIX=~/ngraph_dist **Other optional build flags** -- If running ``gcc-5.4.0`` or ``clang-3.9``, remember that you can also append ``cmake`` with the prebuilt LLVM option to speed-up the build. Another option if your deployment system has IntelĀ® Advanced Vector Extensions (IntelĀ® AVX) is to target the accelerations available directly by compiling the build as follows during the cmake step: ``-DNGRAPH_TARGET_ARCH=skylake-avx512``. .. code-block:: console $ cmake .. [-DNGRAPH_USE_PREBUILT_LLVM=TRUE] [-DNGRAPH_TARGET_ARCH=skylake-avx512] #. Run ``$ make`` and ``make install`` to install ``libngraph.so`` and the header files to ``~/ngraph_dist``: .. code-block:: console $ make # note: make -j <N> may work, but sometimes results in out-of-memory errors if too many compilation processes are used $ make install #. (Optional, requires `doxygen`_, `Sphinx`_, and `breathe`_). Run ``make html`` inside the ``doc/sphinx`` directory of the cloned source to build a copy of the `website docs`_ locally. The low-level API docs with inheritance and collaboration diagrams can be found inside the ``/docs/doxygen/`` directory. See the :doc:`project/doc-contributor-README` for more details about how to build documentation for nGraph. .. _centos: CentOS 7.4 ----------- The process documented here will work on CentOS 7.4. #. (Optional) Create something like ``/opt/libraries`` and (with sudo), give ownership of that directory to your user. Creating such a placeholder can be useful if you'd like to have a local reference for APIs and documentation, or if you are a developer who wants to experiment with how to :doc:`../howto/execute` using resources available through the code base. .. code-block:: console $ sudo mkdir -p /opt/libraries $ sudo chown -R username:username /opt/libraries #. Update the system with :command:`yum` and issue the following commands: .. code-block:: console $ sudo yum update $ sudo yum install zlib-devel install ncurses-libs ncurses-devel patch diffutils wget gcc-c++ make git perl-Data-Dumper #. Install Cmake 3.4: .. code-block:: console $ wget https://cmake.org/files/v3.4/cmake-3.4.3.tar.gz $ tar -xzvf cmake-3.4.3.tar.gz $ cd cmake-3.4.3 $ ./bootstrap --system-curl --prefix=~/cmake $ make && make install #. Clone the `NervanaSystems` ``ngraph`` repo via HTTPS and use Cmake 3.4.3 to build nGraph Libraries to ``~/ngraph_dist``. This command enables ONNX support in the library (optional). .. code-block:: console $ cd /opt/libraries $ git clone https://github.com/NervanaSystems/ngraph.git $ cd ngraph && mkdir build && cd build $ ~/cmake/bin/cmake .. -DCMAKE_INSTALL_PREFIX=~/ngraph_dist -DNGRAPH_ONNX_IMPORT_ENABLE=ON $ make && sudo make install macOS\* development -------------------- .. note:: Although we do not currently offer full support for the macOS platform, some configurations and features may work. The repository includes two scripts (``maint/check-code-format.sh`` and ``maint/apply-code-format.sh``) that are used respectively to check adherence to ``libngraph`` code formatting conventions, and to automatically reformat code according to those conventions. These scripts require the command ``clang-format-3.9`` to be in your ``PATH``. Run the following commands (you will need to adjust them if you are not using bash): .. code-block:: bash $ brew install llvm@3.9 automake $ mkdir -p $HOME/bin $ ln -s /usr/local/opt/llvm@3.9/bin/clang-format $HOME/bin/clang-format-3.9 $ echo 'export PATH=$HOME/bin:$PATH' >> $HOME/.bash_profile Testing the build ================= The |InG| library code base uses GoogleTest's\* `googletest framework`_ for unit tests. The ``cmake`` command from the :doc:`buildlb` guide automatically downloaded a copy of the needed ``gtest`` files when it configured the build directory. To perform unit tests on the install: #. Create and configure the build directory as described in our :doc:`buildlb` guide. #. Enter the build directory and run ``make check``: .. code-block:: console $ cd build/ $ make check Compile a framework with ``libngraph`` ====================================== After building and installing nGraph on your system, there are two likely paths for what you'll want to do next: either compile a framework to run a DL training model, or load an import of an "already-trained" model for inference on an Intel nGraph-enabled backend. For the former case, this early |version|, :doc:`framework-integration-guides`, can help you get started with a training a model on a supported framework. * :doc:`MXNet<framework-integration-guides>` framework, * :doc:`TensorFlow<framework-integration-guides>` framework, and * :doc:`neon<framework-integration-guides>` framework, For the latter case, if you've followed a tutorial from `ONNX`_, and you have an exported, serialized model, you can skip the section on frameworks and go directly to our :doc:`../howto/import` documentation. Please keep in mind that both of these are under continuous development, and will be updated frequently in the coming months. Stay tuned! .. _doxygen: https://www.stack.nl/~dimitri/doxygen/ .. _Sphinx: http://www.sphinx-doc.org/en/stable/ .. _breathe: https://breathe.readthedocs.io/en/latest/ .. _llvm.org: https://www.llvm.org .. _NervanaSystems: https://github.com/NervanaSystems/ngraph/blob/master/README.md .. _googletest framework: https://github.com/google/googletest.git .. _ONNX: http://onnx.ai .. _website docs: http://ngraph.nervanasys.com/docs/latest/