Repo name change and other miscellaneous edits (#588)

doc/sphinx/source/howto/execute.rst

@@ -5,7 +5,7 @@ Execute a Computation
 ######################
 
 This section explains how to manually perform the steps that would normally be 
-performed by a framework :term:`bridge` to execute a computation. Intel® nGraph 
+performed by a framework :term:`bridge` to execute a computation. Intel® nGraph++ 
 library is targeted toward automatic construction; it is far easier for a 
 processing unit (GPU, CPU, or an `Intel Nervana NNP`_) to run a computation than 
 it is for a user to map out how that computation happens. Unfortunately, things 

doc/sphinx/source/howto/index.rst

@@ -3,19 +3,26 @@
 How to 
 ======
 
-.. note:: This section is aimed at intermediate-level developers working with
-   the nGraph library. It assumes a developer has understanding of the concepts 
-   in the previous sections. It does not assume knowledge of any particular 
-   frontend framework. 
+.. toctree::
+   :maxdepth: 1
+   :caption: How to 
+
+   execute.rst
+    
 
 The "How to" articles in this section explain how to do specific tasks with the 
-Intel nGraph library. The recipes are all framework agnostic; in other words, 
+Intel nGraph++ library. The recipes are all framework agnostic; in other words, 
 if an entity (framework or user) wishes to make use of target-based computational 
 resources, it can either:
 
 * Do the tasks programatically through the framework, or 
 * Provide a clear model definition with documentation for the computational 
   resources needed. 
+
+.. note:: This section is aimed at intermediate-level developers working with
+   the nGraph++ library. It assumes a developer has understanding of the concepts 
+   in the previous sections. It does not assume knowledge of any particular 
+   frontend framework. 
   
 Since our primary audience is developers who are pushing the boundaries of deep 
 learning systems, we go beyond the use of deep learning primitives, and include 
@@ -41,9 +48,3 @@ backend engineers, and others.  We welcome contributions from the community and
 invite you to experiment with the variety of hybridization and performance 
 extractions available through the nGraph library.    
 
-.. toctree::
-   :maxdepth: 1
-   :caption: How-to 
-
-   execute.rst
-    
\ No newline at end of file

doc/sphinx/source/index.rst

@@ -13,26 +13,29 @@
 .. limitations under the License.
 .. ---------------------------------------------------------------------------
 
-#############################
-Intel nGraph library project
-#############################
+########################
+Intel nGraph++ library
+########################
 
-Welcome to Intel nGraph, an open source C++ library for developers of 
-:abbr:`Deep Learning (DL)` (DL) systems. Here you will find a suite 
-of components, APIs, and documentation that can be used to compile 
-and run :abbr:`Deep Neural Network (DNN)` (DNN) models defined in a 
-variety of frameworks.  
+Welcome to Intel® nGraph™, an open source C++ library for developers of 
+:abbr:`Deep Learning (DL)` (DL) systems. Here you will find a suite of 
+components, APIs, and documentation that can be used to compile and run 
+:abbr:`Deep Neural Network (DNN)` (DNN) models defined in a variety of 
+frameworks.  
 
 .. figure:: graphics/ngraph-hub.png  
 
 
-For this early release, we provide :doc:`framework-integration-guides` to compile 
-and run MXNet and TensorFlow-based projects.
+For this early release, we've provided :doc:`framework-integration-guides` to 
+compile and run MXNet\* and TensorFlow\*-based projects.
 
-The nGraph library translates a framework’s representation of computations into 
-an :abbr:`Intermediate Representation (IR)` designed to promote computational 
+.. note:: The library code is under active development as we're continually 
+   adding support for more ops, more frameworks, and more backends. 
+
+The nGraph++ library translates a framework’s representation of computations 
+into an :abbr:`Intermediate Representation (IR)` that promotes computational 
 efficiency on target hardware. Initially-supported backends include Intel 
-Architecture CPUs (CPU), the Intel® Nervana Neural Network Processor™ (NNP), 
+Architecture CPUs (``CPU``), the Intel® Nervana Neural Network Processor™ (NNP), 
 and NVIDIA\* GPUs. Currently-supported compiler optimizations include efficient 
 memory management and data layout abstraction. 
 

doc/sphinx/source/installation.rst

 .. installation:
 
-###################################
-Install the Intel® nGraph™ library 
-###################################
+########
+Install 
+########
 
 Build Environments
 ==================
 
-The |release| version of |project| supports Linux\*-based systems which 
-have recent updates of the following packages and prerequisites: 
+The |release| version of |project| supports Linux\*-based systems which have 
+recent updates of the following packages and prerequisites: 
 
 .. csv-table::
    :header: "Operating System", "Compiler", "Build System", "Status", "Additional Packages"
@@ -25,15 +25,15 @@ Other configurations may work, but aren't tested; on Ubuntu 16.04 with
 below. This gets a pre-built tarball of LLVM+Clang from `llvm.org`_, and will
 substantially reduce build time.
 
-If using ``gcc-4.8``, it may be necessary to add symlinksfrom ``gcc`` to
+If using ``gcc-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. (You should NOT supply the `-DNGRAPH_USE_PREBUILT_LLVM` 
+flags when building. (You **should 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.)
 
-Support for macOS is limited; see the `macOS development prerequisites`_ 
-section at the end of this page for details.
+Support for macOS is limited; see the `macOS development`_ section at the end of 
+this page for details.
 
 
 Installation Steps
@@ -44,11 +44,10 @@ install ``ngraph_dist`` to the installing user's ``$HOME`` directory as
 the default location. See the :file:`CMakeLists.txt` file for more 
 information about how to change or customize this location.
 
-#.  (Optional) Since most of a developer's interaction with a frontend 
-    framework will take place locally through Pythonic APIs to the C++
-    library, you can set a reference placeholder for the documented source 
-    cloned from the repo. Create something like ``/opt/local`` and (with sudo 
-    permissions), give ownership of that directory to your user.  
+#.  (Optional) Create something like ``/opt/local`` and (with sudo permissions), 
+    give ownership of that directory to your user. Under this directory, you can 
+    add a placeholder for ``libraries`` to have a placeholder for the documented 
+    source cloned from the repo: 
 
     .. code-block:: console
 
@@ -62,62 +61,70 @@ information about how to change or customize this location.
    .. code-block:: console
 
       $ cd /opt/local/libraries
-      $ git clone git@github.com:NervanaSystems/private-ngraph-cpp.git
-      $ cd private-ngraph-cpp
+      $ git clone git@github.com:NervanaSystems/ngraph-cpp.git
+      $ cd ngraph-cpp
 
-#. Create a build directory outside of the ``private-ngraph-cpp/src`` directory 
-   tree; somewhere like ``private-ngraph-cpp/build``, for example.
+#. Create a build directory outside of the ``ngraph-cpp/src`` directory 
+   tree; somewhere like ``ngraph-cpp/build``, for example.
 
    .. code-block:: console
 
       $ mkdir build   
 
 #. ``$ cd`` to the build directory and generate the GNUMakefiles in the 
-   customary manner from within your ``build`` directory:
+   customary manner from within your ``build`` directory (remember to append the 
+   command with the prebuilt option, if needed):
 
    .. code-block:: console
 
-      $ cd build && cmake ../
+      $ cd build && cmake ../ [-DNGRAPH_USE_PREBUILT_LLVM=TRUE]
 
-#. Run ``$ make -j8`` and ``make install`` to install ``libngraph.so`` and the 
-   header files to the default location of ``$HOME/ngraph_dist``.
+#. (Optional) Run ``$ make [-jN]`` where :option:`-jN` specifies the number of 
+   cores. The example here uses a configuration of :option:`j8`, which is 
+   good for a system install using an Intel® Xeon® (CPU processor). This step 
+   is **not recommended** with Docker / VM installs. 
 
    .. code-block:: console
+      
+      $ make -j8
 
-      $ make -j8 && make install 
+#. Run ``make install`` to install ``libngraph.so`` and the header files to the 
+   default location of ``$HOME/ngraph_dist``
 
+   .. code-block:: console
 
-#. (Optional, requires `Sphinx`_.)  Run ``make html`` inside the  
-   ``doc/sphinx`` directory to build HTML docs for the nGraph library.    
+      $ make install
 
-#. (Optional, requires `doxygen`_.)  Run ``$ make htmldocs`` inside
-   the ``doc/sphinx`` directory to build HTML API docs inside the 
-   ``/docs/doxygen/`` directory. 
+#. (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 diagrams 
+   and collaboration diagrams can be found inside the ``/docs/doxygen/`` 
+   directory.    
 
-.. macos_development_prerequisites: 
+.. macos_development: 
 
-macOS Development Prerequisites
--------------------------------
+macOS development
+-----------------
 
-.. note:: If you are developing |nGl| projects on macOS*\, please be 
-   aware that this platform is officially unsupported.
+.. note:: The macOS*\ platform is officially unsupported.
 
 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 
+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
-  $ 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
+   $ brew install llvm@3.9
+   $ 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
 
 .. _doxygen: https://www.stack.nl/~dimitri/doxygen/
 .. _Sphinx:  http://www.sphinx-doc.org/en/stable/
-.. _NervanaSystems: https://github.com/NervanaSystems/private-ngraph-cpp/blob/master/README.md
+.. _breathe: https://breathe.readthedocs.io/en/latest/
 .. _llvm.org: https://www.llvm.org 
-
+.. _NervanaSystems: https://github.com/NervanaSystems/ngraph-cpp/blob/master/README.md
+.. _website docs: http://ngraph.nervanasys.com/index.html/index.html

doc/sphinx/source/testing-libngraph.rst

@@ -32,7 +32,7 @@ For this early |release| release, we're providing :doc:`framework-integration-gu
 for:
 
 * :doc:`MXNet<framework-integration-guides>` framework,  
-* :doc:`Tensorflow<framework-integration-guides>` framework, and
+* :doc:`TensorFlow<framework-integration-guides>` framework, and
 * neon™ `frontend framework`_.
 
 Integration guides for other frameworks are tentatively forthcoming.