Doc validation, cleanup, remove stuff we are not using, etc

Licenses/sphinx-license.txt

@@ -33,79 +33,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 Licenses for incorporated software
 ==================================
 
-The included smartypants module, included as sphinx.util.smartypants,
-is available under the following license:
-
-----------------------------------------------------------------------
-SmartyPants_ license::
-
-    Copyright (c) 2003 John Gruber
-    (https://daringfireball.net/projects/smartypants/)
-    All rights reserved.
-
-    Redistribution and use in source and binary forms, with or without
-    modification, are permitted provided that the following conditions
-    are met:
-
-    *   Redistributions of source code must retain the above copyright
-        notice, this list of conditions and the following disclaimer.
-
-    *   Redistributions in binary form must reproduce the above
-        copyright notice, this list of conditions and the following
-        disclaimer in the documentation and/or other materials
-        provided with the distribution.
-
-    *   Neither the name "SmartyPants" nor the names of its
-        contributors may be used to endorse or promote products
-        derived from this software without specific prior written
-        permission.
-
-    This software is provided by the copyright holders and
-    contributors "as is" and any express or implied warranties,
-    including, but not limited to, the implied warranties of
-    merchantability and fitness for a particular purpose are
-    disclaimed. In no event shall the copyright owner or contributors
-    be liable for any direct, indirect, incidental, special,
-    exemplary, or consequential damages (including, but not limited
-    to, procurement of substitute goods or services; loss of use,
-    data, or profits; or business interruption) however caused and on
-    any theory of liability, whether in contract, strict liability, or
-    tort (including negligence or otherwise) arising in any way out of
-    the use of this software, even if advised of the possibility of
-    such damage.
-
-
-smartypants.py license::
-
-    smartypants.py is a derivative work of SmartyPants.
-
-    Redistribution and use in source and binary forms, with or without
-    modification, are permitted provided that the following conditions
-    are met:
-
-    *   Redistributions of source code must retain the above copyright
-        notice, this list of conditions and the following disclaimer.
-
-    *   Redistributions in binary form must reproduce the above
-        copyright notice, this list of conditions and the following
-        disclaimer in the documentation and/or other materials
-        provided with the distribution.
-
-    This software is provided by the copyright holders and
-    contributors "as is" and any express or implied warranties,
-    including, but not limited to, the implied warranties of
-    merchantability and fitness for a particular purpose are
-    disclaimed. In no event shall the copyright owner or contributors
-    be liable for any direct, indirect, incidental, special,
-    exemplary, or consequential damages (including, but not limited
-    to, procurement of substitute goods or services; loss of use,
-    data, or profits; or business interruption) however caused and on
-    any theory of liability, whether in contract, strict liability, or
-    tort (including negligence or otherwise) arising in any way out of
-    the use of this software, even if advised of the possibility of
-    such damage.
-----------------------------------------------------------------------
-
 The included JQuery JavaScript library is available under the MIT
 license:
 
@@ -132,34 +59,6 @@ OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
 WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 ----------------------------------------------------------------------
 
-The included Underscore JavaScript library is available under the MIT
-license:
-
-----------------------------------------------------------------------
-Copyright (c) 2009 Jeremy Ashkenas, DocumentCloud
-
-Permission is hereby granted, free of charge, to any person
-obtaining a copy of this software and associated documentation
-files (the "Software"), to deal in the Software without
-restriction, including without limitation the rights to use,
-copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the
-Software is furnished to do so, subject to the following
-conditions:
-
-The above copyright notice and this permission notice shall be
-included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
-OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
-HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
-WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
-OTHER DEALINGS IN THE SOFTWARE.
-
--------------------------------------------------------------------------------
 
 The included implementation of NumpyDocstring._parse_numpydoc_see_also_section
 was derived from code under the following license:

README.md

 # nGraph library
 
-Welcome to Intel® nGraph™, an open source C++ library and
-compiler. This project enables modern compute platforms to run and
-train Deep Neural Network (DNN) models. It is framework-neutral and
-supports a variety of backends used by Deep Learning (DL) frameworks.
+Welcome to Intel® nGraph™, an open source C++ library and compiler. 
+This project enables modern compute platforms to run and train Deep 
+Neural Network (DNN) models. It is framework-neutral and supports a 
+variety of backends used by Deep Learning (DL) frameworks.
 
 ![nGraph ecosystem][ngraph-ecosystem]
 
@@ -12,7 +12,7 @@ supports a variety of backends used by Deep Learning (DL) frameworks.
 See our [install] docs for how to get started. 
 
 For this early release, we provide [framework integration guides] to
-compile MXNet and TensorFlow-based projects.  If you already have a
+compile MXNet and TensorFlow-based projects. If you already have a
 trained model, we've put together a getting started guide for
 [how to import] a deep learning model and start working with the nGraph
 APIs.
@@ -28,16 +28,16 @@ We welcome community contributions to nGraph. If you have an idea how
 to improve the library:
 
 * Share your proposal via [GitHub issues].
-* Ensure you can build the product and run all the examples with your patch
-* In the case of a larger feature, create a test
-* Submit a [pull request]
+* Ensure you can build the product and run all the examples with your patch.
+* In the case of a larger feature, create a test.
+* Submit a [pull request].
 * We will review your contribution and, if any additional fixes or
   modifications are necessary, may provide feedback to guide you. When
-  accepted, your pull request will be merged the repository.
+  accepted, your pull request will be merged to the repository.
 
 [install]: http://ngraph.nervanasys.com/docs/latest/install.html
 [framework integration guides]: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html
 [Github issues]: https://github.com/NervanaSystems/ngraph/issues
 [pull request]: https://github.com/NervanaSystems/ngraph/pulls
 [how to import]: http://ngraph.nervanasys.com/docs/latest/howto/import.html
-[ngraph-ecosystem]: doc/sphinx/source/graphics/ngraph-ecosystem.png "nGraph Ecosystem"
+[ngraph-ecosystem]: doc/sphinx/source/graphics/ngraph-ecosystem3.png "nGraph Ecosystem"

doc/sphinx/ngraph_theme/footer.html

@@ -18,7 +18,7 @@
       {%- if hasdoc('copyright') %}
         {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; <a href="{{ path }}">Copyright</a> {{ copyright }}. {% endtrans %}
       {%- else %}
-        <span class="crt-size">{% trans copyright=copyright|e %}&copy; Copyright {{ copyright }}.</span> <br/><div class="brandnote"> Intel nGraph library contains trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. * Other names and brands may be claimed as the property of others; see <a class="reference internal" href="branding-notice.html">branding notice</a> for more information.</class>{% endtrans %}
+        <span class="crt-size">{% trans copyright=copyright|e %}&copy; Copyright {{ copyright }}.</span> <br/><div class="brandnote"> Intel nGraph library contains trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. * Other names and brands may be claimed as the property of others; see <a href="http://ngraph.nervanasys.com/docs/latest/branding-notice.html">branding notice</a> for more information.</class>{% endtrans %}
       {%- endif %}
     {%- endif %}
 

doc/sphinx/ngraph_theme/static/css/theme.css

@@ -1836,8 +1836,16 @@ div[class^='highlight'] td.code {
   width: 100%;
 }
 
+.wy-menu-vertical p.caption {
+  font-weight: bold;
+  text-transform: uppercase;
+  font-size: 110%;
+  color: #fff;
+  white-space: nowrap;
+}
+
 code, p.caption { 
-  font-family: Inconsolata, sans, monospace;
+  font-family: Consolas, sans, monospace;
   color: #A79992;
   font-size: 0.99em;
   line-height: 1.39em;

doc/sphinx/ngraph_theme/theme.conf

@@ -9,3 +9,4 @@ sticky_navigation = True
 logo_only =
 collapse_navigation = False
 display_version = True
+use_bower = FALSE

doc/sphinx/source/conf.py

@@ -81,6 +81,7 @@ language = 'en'
 # This patterns also effect to html_static_path and html_extra_path
 exclude_patterns = []
 
+
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'sphinx'
 
@@ -187,7 +188,7 @@ texinfo_documents = [
      'Miscellaneous'),
 ]
 
-html_add_permalinks = ""
+html_add_permalinks = "true"
 
 breathe_projects = {
     "ngraph": "../../doxygen/xml",

doc/sphinx/source/framework-integration-guides.rst

@@ -10,26 +10,28 @@ Framework Integration Guides
 
 .. _neon_intg:
 
-Neon |trade|
+neon |trade|
 ============
 
-Use ``neon`` as a frontend
----------------------------
+Use ``neon`` as a frontend for nGraph backends
+-----------------------------------------------
 
-``neon`` is a open source Deep Learning framework specifically designed to be 
-powered by |InG| backends.
+``neon`` is a open source Deep Learning framework.  For info about how to 
+interact and use a model with this framework, see the `ngraph-neon docs`_. 
+This section covers installation only. 
 
 .. important:: The numbered instructions below pick up from where 
-   the :doc:`install` installation instructions left off, and they presume 
-   that your system already has the library installed at ``$HOME/ngraph_dist`` as 
-   the default location. If the |nGl| code has not yet been installed to your 
-   system, you can follow the instructions on the `ngraph-neon python README`_ to 
-   install everything at once.  If the |nGl| code already is installed, 
+   the :doc:`install` instructions left off, and they presume that your system 
+   already has the library installed at ``$HOME/ngraph_dist`` as the default 
+   location. If the |nGl| code has not yet been installed to your system, you 
+   can follow the instructions on the `ngraph-neon python README`_ to install 
+   everything at once.  If the |nGl| code base already is installed on a system, 
+   use this process.
 
 
-
-#. Set the ``NGRAPH_CPP_BUILD_PATH`` and the ``LD_LIBRARY_PATH`` path to the location 
-   where you built the nGraph libraries:
+#. Set the ``NGRAPH_CPP_BUILD_PATH`` and the ``LD_LIBRARY_PATH`` path to the 
+   location where you built the nGraph libraries. (This example shows the default 
+   location):
 
    .. code-block:: bash
 
@@ -44,8 +46,8 @@ powered by |InG| backends.
       $ sudo apt-get install python3-pip 
 
 
-#. (Optionally) activate a virtualenv if you like working with virtualenvs: 
-   and go to the `python` subdirectory of the ``ngraph`` repo:
+#. (Optionally) activate a virtualenv if you like working with virtualenvs and 
+   go to the `python` subdirectory of the ``ngraph`` repo:
    
    .. code-block:: console
 
@@ -54,15 +56,17 @@ powered by |InG| backends.
       (venv)$ cd ngraph/python
       (venv)$ pip install -U .
 
-#. See `this file`_ if you want detail about how to run unit tests. To start 
-   working with models, see the `ngraph-neon repo's README`_ to start working
-   with models. 
+#. See `this file`_ if you want detail about how to run unit tests. See the 
+   documentation `ngraph-neon docs`_ to start working with models. 
 
 
 .. _mxnet_intg:
 
-Compile MXNet\* with ``libngraph``
-==================================
+MXNet\* 
+========
+
+Compile MXNet with nGraph
+--------------------------
 
 .. important:: These instructions pick up from where the :doc:`install`
    installation instructions left off, so they presume that your system already
@@ -146,8 +150,11 @@ Compile MXNet\* with ``libngraph``
 
 .. _tensorflow_intg:
 
-Build TensorFlow\* with an XLA plugin to ``libngraph``
-======================================================
+TensorFlow\* 
+=============
+
+Build with an XLA plugin to ``libngraph``
+------------------------------------------
 
 .. important:: These instructions pick up where the :doc:`install` 
    installation instructions left off, so they presume that your system already
@@ -274,4 +281,5 @@ your cloned version of `ngraph-tensorflow`_:
 .. _ngraph-tensorflow: https://github.com/NervanaSystems/ngraph-tensorflow
 .. _/examples/mnist: https://github.com/NervanaSystems/ngraph-tensorflow/tree/develop/tensorflow/compiler/plugin/ngraph/examples/mnist
 .. _ngraph-neon python README: https://github.com/NervanaSystems/ngraph/blob/master/python/README.md
-.. _ngraph-neon repo's README: https://github.com/NervanaSystems/ngraph-neon/blob/master/README.md
+.. _ngraph-neon repo's README: https://github.com/NervanaSystems/ngraph-neon/blob/master/README.md
\ No newline at end of file
+.. _ngraph-neon docs: https://github.com/NervanaSystems/ngraph-neon/tree/master/doc
\ No newline at end of file

doc/sphinx/source/index.rst

@@ -13,22 +13,22 @@
 .. limitations under the License.
 .. ---------------------------------------------------------------------------
 
-#####################
-Intel nGraph library
-#####################
+###############
+nGraph library
+###############
 
 Welcome to Intel® nGraph™, an open source C++ library and compiler. This 
-project enables modern compute platforms to run and train :abbr:`Deep Neural Network (DNN)`
+project enables modern compute platforms to run and train :abbr:`Deep Neural Network (DNN)` 
 models. It is framework-neutral and supports a variety of backends used by 
 :abbr:`Deep Learning (DL)` frameworks. 
 
-.. figure:: ../graphics/ngraph-ecosystem.png
+.. image:: graphics/ngraph-ecosys.png
    :width: 585px
 
 For this early release, we've provided :doc:`framework-integration-guides` to 
 compile and run MXNet\* and TensorFlow\*-based projects. If you already have 
-a trained model, we've got a section on How to :doc:`howto/import` that model
-start working with the nGraph APIs.  
+a trained model, see our section on How to :doc:`howto/import` and start working 
+with the nGraph APIs.  
 
 .. note:: The library code is under active development as we're continually 
    adding support for more ops, more frameworks, and more backends. 
@@ -46,19 +46,19 @@ Further project details can be found on our :doc:`project/about` page.
 
 =======
 
-Sections
-=========
+Contents
+========
 
 .. toctree::
    :maxdepth: 1
    :name: tocmaster
-   :caption: Table of Contents
+   :caption: Documentation
 
    install.rst
-   framework-integration-guides.rst
    graph-basics.rst
    howto/index.rst
    ops/index.rst
+   framework-integration-guides.rst
    project/index.rst
 
 

doc/sphinx/source/install.rst

@@ -40,7 +40,7 @@ The CMake procedure installs ``ngraph_dist`` to the installing user's ``$HOME``
 directory as the default location. See the :file:`CMakeLists.txt` file for 
 details about how to change or customize the install location.
 
-The process documented here will work on Ubuntu 16.04 (LTS)
+The process documented here will work on Ubuntu\* 16.04 (LTS)
 
 #. (Optional) Create something like ``/opt/libraries`` and (with sudo), 
    give ownership of that directory to your user. Creating such a placeholder 
@@ -83,7 +83,8 @@ The process documented here will work on Ubuntu 16.04 (LTS)
 
    .. 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   # note: make -j <N> may work, but sometimes results in out-of-memory 
+               # errors if too many compilation processes are used
 
 
 #. (Optional, requires `doxygen`_, `Sphinx`_, and `breathe`_). Run ``make html`` 
@@ -137,12 +138,12 @@ To perform unit tests on the install:
 Compile a framework with ``libngraph``
 ======================================
 
-After building and installing nGraph++ on your system, there are two likely 
+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 "already-trained" model for inference on an 
-Intel nGraph++ enabled backend
+training model, or load an import of an "already-trained" model for inference 
+on an Intel nGraph-enabled backend.
 
-For this former case, this early |release| release, :doc:`framework-integration-guides`, 
+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:`neon<framework-integration-guides>` framework,  
@@ -154,7 +155,7 @@ exported, serialized model, you can skip the section on frameworks and go direct
 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!  
+be updated frequently in the coming months. Stay tuned!  
 
 
 .. _doxygen: https://www.stack.nl/~dimitri/doxygen/
@@ -162,7 +163,6 @@ be updated frequently in the coming months.  Stay tuned!
 .. _breathe: https://breathe.readthedocs.io/en/latest/
 .. _llvm.org: https://www.llvm.org 
 .. _NervanaSystems: https://github.com/NervanaSystems/ngraph/blob/master/README.md
-.. _website docs: http://ngraph.nervanasys.com/index.html/index.html
 .. _googletest framework: https://github.com/google/googletest.git
 .. _ONNX: http://onnx.ai
-.. _frontend framework: http://neon.nervanasys.com/index.html/
+.. _website docs: http://ngraph.nervanasys.com/docs/latest/
\ No newline at end of file

doc/sphinx/source/project/about.rst

@@ -8,24 +8,24 @@ project enables modern compute platforms to run and train
 :abbr:`Deep Neural Network (DNN)` models. It is framework-neutral and supports 
 a variety of backends used by :abbr:`Deep Learning (DL)` frameworks. 
 
-.. figure:: graphics/ngraph-ecosystem.png
-   :width: 585px
-  
+.. figure:: ../graphics/ngraph-ecosys.png
+   :width: 585px 
+ 
 The nGraph library translates a framework’s representation of computations into 
 an :abbr:`Intermediate Representation (IR)` designed to promote computational 
 efficiency on target hardware. Initially-supported backends include Intel 
 Architecture CPUs, the Intel® Nervana Neural Network Processor™ (NNP), 
-and NVIDIA\* GPUs. Currently-supported compiler optimizations include efficient 
-memory management and data layout abstraction. 
+and NVIDIA\* GPUs. 
+
 
-Why is this needed?
---------------------
+Why was this needed?
+---------------------
 
 When Deep Learning (DL) frameworks first emerged as the vehicle for training 
-and inference models, they were designed around kernels optimized for a 
-particular platform. As a result, many backend details were being exposed in 
-the model definitions, making the adaptability and portability of DL models 
-to other or more advanced backends inherently complex and expensive.
+models, they were designed around kernels optimized for a particular platform. 
+As a result, many backend details were being exposed in the model definitions, 
+making the adaptability and portability of DL models to other, or more advanced 
+backends inherently complex and expensive.
 
 The traditional approach means that an algorithm developer cannot easily adapt 
 his or her model to different backends. Making a model run on a different 
@@ -35,36 +35,61 @@ to similar ops in the new framework, and finally make the necessary changes
 for the preferred backend configuration on the new framework.
 
 We designed the Intel nGraph project to substantially reduce these kinds of 
-engineering complexities. While optimized kernels for deep-learning primitives 
-are provided through the project and via libraries like Intel® Math Kernel 
-Library (Intel® MKL) for Deep Neural Networks (Intel® MKL-DNN), there are 
-several compiler-inspired ways in which performance can be further optimized.
-
-=======
-
-The *nGraph core* uses a strongly-typed and platform-neutral stateless graph 
-representation for computations. Each node, or *op*, in the graph corresponds
-to one step in a computation, where each step produces zero or more tensor
-outputs from zero or more tensor inputs.
-
-There is a *framework bridge* for each supported framework which acts as 
-an intermediary between the *ngraph core* and the framework. A *transformer* 
-plays a similar role between the ngraph core and the various execution 
-platforms.
-
-Transformers compile the graph using a combination of generic and 
-platform-specific graph transformations. The result is a function that
-can be executed from the framework bridge. Transformers also allocate
-and deallocate, as well as read and write tensors under direction of the
-bridge.
+engineering complexities. Our conpiler-inspired approach means that developers 
+have fewer constraints imposed by frameworks when working with their models; 
+they can pick and choose only the components they need to build custom algorithms 
+for advanced deep learning tasks. Furthermore, if working with a model that is 
+already trained (or close to being trained), or if they wish to pivot and add a 
+new layer to an existing model, the data scientist can :doc:`../howto/import` 
+and start working with :doc:`../ops/index` more quickly. 
+
+
+How does it work?
+------------------
+
+The *nGraph core* uses a **strongly-typed and platform-neutral stateless graph 
+representation** for computations. Each node, or *op*, in the graph corresponds
+to one :term:`step` in a computation, where each step produces zero or more 
+tensor outputs from zero or more tensor inputs. For a more detailed dive into 
+how this works, read our documentation :doc:`../howto/execute`.
+
+
+How do I connect it to a framework? 
+------------------------------------
+
+Currently, we offer *framework bridges* for some `widely-supported frameworks`_. 
+The bridge acts as an intermediary between the *ngraph core* and the framework,
+providing a means to use various execution platforms. The result is a function 
+that can be executed from the framework bridge.
+
+Given that we have no way to predict how many more frameworks might be invented
+for either model or framework-specific purposes, it would be nearly impossible 
+for us to create bridges for every framework that currently exists (or that will 
+exist in the future). Thus, the library provides a way for developers to write 
+or contribute "bridge code" for various frameworks.  We welcome such 
+contributions from the DL community.
+
+
+How do I connect a DL training or inference model to nGraph?
+-------------------------------------------------------------
+
+Framework bridge code is *not* the only way to connect a model (function graph) 
+to nGraph's :doc:`../ops/index`. We've also built an importer for models that 
+have been exported from a framework and saved as serialized file, such as ONNX. 
+To learn how to convert such serialized files to an nGraph model, please see 
+the :doc:`../howto/import` documentation.  
+
+
+What's next?
+-------------
   
-We developed Intel nGraph to simplify the realization of optimized deep 
-learning performance across frameworks and hardware platforms. You can
-read more about design decisions and what is tentatively in the pipeline
-for development in our `SysML conference paper`_.
-
-.. _frontend: http://neon.nervanasys.com/index.html/
-.. _SysML conference paper: https://arxiv.org/pdf/1801.08058.pdf
-.. _MXNet: http://mxnet.incubator.apache.org/
-.. _TensorFlow: https://www.tensorflow.org/
+We developed nGraph to simplify the realization of optimized deep learning 
+performance across frameworks and hardware platforms. You can read more about 
+design decisions and what is tentatively in the pipeline for development in 
+our `arXiv paper`_ from the 2018 SysML conference.
+
+
+.. _widely-supported frameworks: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html
+.. _arXiv paper: https://arxiv.org/pdf/1801.08058.pdf
+.. _Intel® MKL-DNN: https://github.com/intel/mkl-dnn