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Building
========

The TensorRT Inference Server is built using Docker and the TensorFlow
and PyTorch containers from `NVIDIA GPU Cloud (NGC)
<https://ngc.nvidia.com>`_. Before building you must install Docker
and nvidia-docker and login to the NGC registry by following the
instructions in :ref:`section-installing-prebuilt-containers`.

.. _section-building-the-server:

Building the Server
-------------------

To build a release version of the TRTIS container, change directory to
the root of the repo and issue the following command::

  $ docker build --pull -t tensorrtserver .

Incremental Builds
^^^^^^^^^^^^^^^^^^

For typical development you will want to run the *build* container
with your local repo’s source files mounted so that your local changes
can be incrementally built. This is done by first building the
*tensorrtserver_build* container::

  $ docker build --pull -t tensorrtserver_build --target trtserver_build .

By mounting /path/to/tensorrtserver/src into the container at
/workspace/src, changes to your local repo will be reflected in the
container::

  $ nvidia-docker run -it --rm -v/path/to/tensorrtserver/src:/workspace/src tensorrtserver_build

Within the container you can perform an incremental server build
with::

  # cd /workspace
  # bazel build -c opt --config=cuda src/servers/trtserver
  # cp /workspace/bazel-bin/src/servers/trtserver /opt/tensorrtserver/bin/trtserver

Similarly, within the container you can perform an incremental build
of the C++ and Python client libraries and example executables with::

  # cd /workspace
  # bazel build -c opt --config=cuda src/clients/…
  # mkdir -p /opt/tensorrtserver/bin
  # cp bazel-bin/src/clients/c++/image_client /opt/tensorrtserver/bin/.
  # cp bazel-bin/src/clients/c++/perf_client /opt/tensorrtserver/bin/.
  # cp bazel-bin/src/clients/c++/simple_client /opt/tensorrtserver/bin/.
  # mkdir -p /opt/tensorrtserver/lib
  # cp bazel-bin/src/clients/c++/librequest.so /opt/tensorrtserver/lib/.
  # cp bazel-bin/src/clients/c++/librequest.a /opt/tensorrtserver/lib/.
  # mkdir -p /opt/tensorrtserver/pip
  # bazel-bin/src/clients/python/build_pip /opt/tensorrtserver/pip/.

Some source changes seem to cause bazel to get confused and not
correctly rebuild all required sources. You can force bazel to rebuild
all of the TRTIS source without requiring a complete rebuild of the
TensorFlow and Caffe2 components by doing the following before issuing
the above build command::

  # rm -fr bazel-bin/src

.. include:: client.rst
   :start-after: build-client-begin-marker-do-not-remove
   :end-before: build-client-end-marker-do-not-remove

Building the Documentation
--------------------------

The TRTIS documentation is found in the docs/ directory and is based
on `Sphinx <http://www.sphinx-doc.org>`_. `Doxygen
<http://www.doxygen.org/>`_ integrated with `Exhale
<https://github.com/svenevs/exhale>`_ is used for C++ API
docuementation.

To build the docs install the required dependencies::

  $ apt-get update
  $ apt-get install -y --no-install-recommends doxygen
  $ pip install --upgrade sphinx sphinx-rtd-theme nbsphinx exhale

To get the Python client library API docs the TensorRT Inference
Server Python package must be installed::

  $ pip install --upgrade tensorrtserver-*.whl

Then use Sphinx to build the documentation into the build/html
directory::

  $ cd docs
  $ make clean html