Many updates to latest nGraph Architecture and feature docs (#1953)

* editing docs

* more doc updates

* Cleanup theme, update backends for PlaidML, remove stale font

* Add PlaidML description and doc update that should have been added with PR 1888

* Add PlaidML description and doc update that should have been added with PR 1888

* Latest release doc updates

* Add PlaidML description and doc update for PR 1888
* Update glossary with tensor description and quantization def
* Refactor landpage with QuickStart guides
* Add better details about nGraph features and roadmap

* Placeholder detail for comparison section

* Add section link

* order sections alphabetically for now

* update compiler illustration

* Address feedback from doc review

* Update illustration wording

* Formatting and final edits

* keep tables consistent

* Clarify doc on bridge and compiler docs

* Clarify doc on bridge and compiler docs

* yay for more feedback and improvements

* edit with built doc

* Fix typo

* Another phase of PR review editing

* Final review comment resolved

doc/sphinx/ngraph.doxyfile

@@ -15,8 +15,8 @@
 
 
 
-PROJECT_NAME     = "Intel® nGraph™ library API docs"
-PROJECT_BRIEF    = "C++ code reference for the Intel® nGraph™ library"
+PROJECT_NAME     = "Intel® nGraph Library and API docs"
+PROJECT_BRIEF    = "Code reference for the Intel® nGraph C++ Library"
 
 OUTPUT_DIRECTORY = @CMAKE_CURRENT_BINARY_DIR@
 INPUT            = @CMAKE_SOURCE_DIR@/src
@@ -1083,7 +1083,7 @@ HTML_EXTRA_FILES       =
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-HTML_COLORSTYLE_HUE    = 220
+HTML_COLORSTYLE_HUE    = 193
 
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 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_COLORSTYLE_SAT    = 100
+HTML_COLORSTYLE_SAT    = 215
 
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 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-TREEVIEW_WIDTH         = 550
+TREEVIEW_WIDTH         = 248
 
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@@ -1381,7 +1381,7 @@ EXT_LINKS_IN_WINDOW    = NO
 # Minimum value: 8, maximum value: 50, default value: 10.
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-FORMULA_FONTSIZE       = 10
+FORMULA_FONTSIZE       = 11
 
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@@ -1895,10 +1895,9 @@ HAVE_DOT               = YES
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-# Minimum value: 0, maximum value: 32, default value: 0.
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-DOT_NUM_THREADS        = 0
+DOT_NUM_THREADS        = 32
 
 # When you want a differently looking font in the dot files that doxygen
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@@ -1908,21 +1907,21 @@ DOT_NUM_THREADS        = 0
 # The default value is: Helvetica.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTNAME           = NeoSansIntel
+DOT_FONTNAME           = "NeoSansIntel-Regular"
 
 # The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
 # dot graphs.
 # Minimum value: 4, maximum value: 24, default value: 10.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTSIZE           = 12
+DOT_FONTSIZE           = 10
 
 # By default doxygen will tell dot to use the default font as specified with
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 # the path where dot can find it using this tag.
 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_FONTPATH           = ngraph_theme/static/fonts/NeoSansIntel
+DOT_FONTPATH           = ngraph_theme/static/fonts/
 
 # If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
 # each documented class showing the direct and indirect inheritance relations.
@@ -2097,7 +2096,7 @@ DIAFILE_DIRS           =
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 # This tag requires that the tag HAVE_DOT is set to YES.
 
-DOT_GRAPH_MAX_NODES    = 512
+DOT_GRAPH_MAX_NODES    = 1024
 
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@@ -2109,7 +2108,7 @@ DOT_GRAPH_MAX_NODES    = 512
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 # This tag requires that the tag HAVE_DOT is set to YES.
 
-MAX_DOT_GRAPH_DEPTH    = 5
+MAX_DOT_GRAPH_DEPTH    = 2
 
 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
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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 href="http://ngraph.nervanasys.com/docs/latest/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

@@ -577,7 +577,7 @@ a .fa, a .rst-content .admonition-title, .rst-content a .admonition-title, a .rs
   display: block;
   background: #638470;
   margin: -5px;
-  font-family: 'Faustina', sans;
+  font-family: 'RobotoSlab', sans;
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   margin-bottom: 0.33em;
 }
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   text-decoration: none;
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-  font-family: "NeoSansIntel", "Nanum Gothic", Arial, sans;
+  font-family: "NeoSansIntel", "RobotoSlab", Arial, sans;
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@@ -1147,7 +1147,7 @@ input {
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-  font-family: "NeoSansIntel", "Nanum Gothic", Arial, sans;
+  font-family: "NeoSansIntel", "RobotoSlab", Arial, sans;
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   font-size: 80%;
-  font-family: "NeoSansIntel", "Nanum Gothic", Arial, sans;
+  font-family: "NeoSansIntel", "RobotoSlab", Arial, sans;
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-  font-family: "Nanum Gothic", Arial, sans;
+  font-family: "RobotoSlab", Arial, sans;
 }
 
 select, textarea {
@@ -1610,17 +1610,17 @@ input[type="radio"][disabled], input[type="checkbox"][disabled] {
 }
 
 a {
-  color: #5c8583;
+  color: #a08105;
   text-decoration: none;
   cursor: pointer;
 }
 a:hover {
-  color: #5c8583;
-  background-color: #dce7e6;
+  color: #a08105;
+  text-decoration: underline;
 
 }
 a:visited {
-  color: #5c8583;
+  color: #a08105;
 }
 
 html {
@@ -1629,9 +1629,9 @@ html {
 }
 
 body {
-  font-family: "Nanum Gothic", Arial, sans-serif;
+  font-family: "RobotoSlab", Sans, sans-serif;
   font-weight: normal;
-  color: #404040;
+  color: #38403f;
   min-height: 100%;
   overflow-x: hidden;
   background: #edf0f2;
@@ -1709,7 +1709,7 @@ a.wy-text-neutral:hover {
 h1, h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend {
   margin-top: 0.33em;
   font-weight: 700;
-  font-family: "NeoSansIntel", "Nanum Gothic", arial, sans;
+  font-family: "NeoSansIntel", "RobotoSlab", arial, sans;
 }
 
 p {
@@ -1764,8 +1764,8 @@ code, .rst-content tt, .rst-content code {
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   font-size: 100%;
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-  color: #E74C3C;
+  font-family: Inconsolata-Regular, "Lucida Console", "Lucida Sans Typewriter", "DejaVu Sans Mono", "Bitstream Vera Sans Mono", "Liberation Mono", "Nimbus Mono L", "Courier New", Courier, monospace;
+  color: #253237;
   overflow-x: auto;
 }
 code.code-large, .rst-content tt.code-large {
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-  border: 0.05em solid #dfeef1;
-  padding: 0.01em;
+pre, .codeblock, .rst-content .literal-block, .rst-content pre.literal-block, div[class^='highlight'] {
+  border: 0.079em solid #ebe9e0;
   caption-side: bottom; 
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 }
 
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+  font-family: "NeoSansIntel-Light", sans, monospace;
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   display: block;
   overflow: wrap;
-  color: #615f5e;
+  color: #83827d;
 }
 
   .linenodiv pre {
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   background: #1f1d1d;
   border-top: solid 10px #5f5f5f;
-  font-family: "NeoSansIntel", "Nanum Gothic", "Helvetica Neue", Arial, sans;
+  font-family: "NeoSansIntel", "RobotoSlab", "Helvetica Neue", Arial, sans;
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 }
 .rst-versions a {
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 }
 .rst-content .sidebar .sidebar-title {
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-  font-family: "NeoSansIntel", 'Nanum Gothic', arial, sans-serif;
+  font-family: "NeoSansIntel", 'RobotoSlab', arial, sans-serif;
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 }
 .rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
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-  color: #528481;
+  color: #0C7881;
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 .rst-content tt.xref, a .rst-content tt, .rst-content tt.xref, .rst-content code.xref, a .rst-content tt, a .rst-content code {
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 }
 @font-face {
-  font-family: "Nanum Gothic";
+  font-family: "RobotoSlab";
   font-style: normal;
   font-weight: 500;
-  src: local("NanumGothic Regular"), local("NanumGothic-Regular"), url(../fonts/NanumGothic-Regular.ttf) format("truetype");
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 }
 @font-face {
-  font-family: "Nanum Gothic Coding";
+  font-family: "NanumGothic Coding";
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@@ -2765,6 +2764,7 @@ span[id*='MathJax-Span'] {
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   margin-bottom: 0;
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+  font-family: "Neo Sans Intel", sans;
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@@ -2784,7 +2784,7 @@ span[id*='MathJax-Span'] {
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 .wy-menu-vertical li.current a {
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 .wy-menu-vertical a:active {
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+  background-color: #84AEAD;
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 }
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-    background: #fcfcfc;
+    background: #0C7881;
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doc/sphinx/source/framework-integration-guides.rst

@@ -9,9 +9,15 @@ Integrate Supported Frameworks
 * :ref:`neon_intg`
 
 A framework is "supported" when there is a framework :term:`bridge` that can be 
-cloned from one of our GitHub repos and built to connect to a supported backend
-with nGraph, all the while maintaining the framework's programmatic or user 
-interface. Current bridge-enabled frameworks include TensorFlow* and MXNet*. 
+cloned from one of our GitHub repos and built to connect to nGraph device backends, 
+all the while maintaining the framework's programmatic or user interface. Bridges 
+currently exist for the TensorFlow\* and MXNet\* frameworks. 
+
+.. figure:: graphics/bridge-to-graph-compiler.png
+    :width: 733px
+    :alt: JiT compiling of a computation
+
+    :abbr:`Just-in-Time (JiT)` Compiling for computation
 
 Once connected via the bridge, the framework can then run and train a deep 
 learning model with various workloads on various backends using nGraph Compiler 
@@ -23,12 +29,12 @@ as an optimizing compiler available through the framework.
 MXNet\* bridge
 ===============
 
-#. See the README on `nGraph-MXNet`_ Integration for how to enable the bridge.
+* See the README on `nGraph-MXNet`_ Integration for how to enable the bridge.
 
-#. (Optional) For experimental or alternative approaches to distributed training
-   methodologies, including data parallel training, see the MXNet-relevant sections
-   of the docs on :doc:`distr/index` and :doc:`How to <howto/index>` topics like
-   :doc:`howto/distribute-train`. 
+* Optional: For experimental or alternative approaches to distributed training
+  methodologies, including data parallel training, see the MXNet-relevant sections
+  of the docs on :doc:`distr/index` and :doc:`How to <howto/index>` topics like
+  :doc:`howto/distribute-train`. 
 
 
 .. _tensorflow_intg:

doc/sphinx/source/frameworks/generic.rst

 .. frameworks/generic.rst
 
 
-Working with generic frameworks
-###############################
+Working with other frameworks
+##############################
 
 An engineer may want to work with a deep learning framework that does not yet 
 have bridge code written. For non-supported or “generic” frameworks, it is 
@@ -11,7 +11,7 @@ and/or to design and document a user interface (UI) with specific runtime
 options for whatever custom use case they need. 
 
 The two primary tasks that can be accomplished in the “bridge code” space of the 
-nGraph Abstraction layer are: (1) compiling a dataflow graph and (2) executing 
+nGraph abstraction layer are: (1) compiling a dataflow graph and (2) executing 
 a pre-compiled graph. See the :doc:`../framework-integration-guides` for how we 
 have built bridges with other frameworks. For more in-depth help in writing 
 graph optimizations and bridge code, we provide articles on how to 
@@ -20,8 +20,8 @@ target various compute resources using nGraph when a framework provides some
 inputs to be computed.
 
 
-Activate nGraph |trade| on generic frameworks
-=============================================
+Integrating nGraph with new frameworks
+======================================
 
 This section details some of the *configuration options* and some of the 
 *environment variables* that can be used to tune for optimal performance when 
@@ -40,9 +40,10 @@ backends.
    Other, Not yet, Ask
 
 
-Regardless of the framework, after the :doc:`../buildlb`, a good place to start 
-usually involves making the libraries available to the framework. On Linux\* 
-systems, that command tends to looks something like: 
+Regardless of the framework, after the :doc:`../buildlb` step, a good place 
+to start usually involves making the libraries available to the framework. On 
+Linux\* systems built on Intel® Architecture, that command tends to looks 
+something like: 
 
 .. code-block:: console
 

doc/sphinx/source/glossary.rst

@@ -53,6 +53,10 @@ Glossary
       In the context of a function graph, a "parameter" refers to what
       "stands in" for an argument in an ``op`` definition.
 
+   quantization
+
+      Quantization refers to the conversion of numerical data into a lower-precision representation. Quantization is often used in deep learning to reduce the time and energy needed to perform computations by reducing the size of data transfers and the number of steps needed to perform a computation. This improvement in speed and energy usage comes at a cost in terms of numerical accuracy, but deep learning models are often able to function well in spite of this reduced accuracy. 
+
    result
 
       In the context of a function graph, the term "result" refers to
@@ -104,6 +108,9 @@ Glossary
       Tensors are maps from *coordinates* to scalar values, all of the
       same type, called the *element type* of the tensor.
 
+      .. figure:: graphics/descriptor-of-tensor.png
+         :width: 559px
+
    
    Tensorview 
 

doc/sphinx/source/index.rst

@@ -15,55 +15,52 @@
 
 
 .. This documentation is available online at 
-.. http://ngraph.nervanasys.com/docs/latest/
+.. https://ngraph.nervanasys.com/docs/latest
 
 
-########
-nGraph™ 
-########
-
-Welcome to the nGraph™ documentation site. nGraph is an open-source C++ library 
-and runtime / compiler suite for :abbr:`Deep Learning (DL)` ecosystems. Our goal 
-is to empower algorithm designers, data scientists, framework architects, 
-software engineers, and others with the means to make their work :ref:`portable`, 
-:ref:`adaptable`, and :ref:`deployable` across the most modern 
-:abbr:`Machine Learning (ML)` hardware available today: optimized Deep Learning
-computation devices.
+Welcome
+=======
 
 .. figure:: graphics/599px-Intel-ngraph-ecosystem.png
-   :width: 599px   
-  
+   :width: 599px
 
-.. _portable:
 
-Portable
-========
+nGraph is an open-source C++ library, compiler, and runtime accelerator for 
+software engineering in the :abbr:`Deep Learning (DL)` ecosystem. nGraph 
+simplifies development and makes it possible to design, write, compile, and
+deploy :abbr:`Deep Neural Network (DNN)`-based solutions. A more detailed 
+explanation of the feature set of nGraph Compiler, as well as a high-level 
+overview, can be found on our project :doc:`project/about`. 
+
+
+.. _quickstart:
 
-One of nGraph's key features is **framework neutrality**. While we currently 
-support :doc:`three popular <framework-integration-guides>` frameworks with 
-pre-optimized deployment runtimes for training :abbr:`Deep Neural Network (DNN)`, 
-models, you are not limited to these when choosing among frontends. Architects 
-of any framework (even those not listed above) can use our documentation for how
-to :doc:`compile and run <howto/execute>` a training model and design or tweak 
-a framework to bridge directly to the nGraph compiler. With a *portable* model 
-at the core of your :abbr:`DL (Deep Learning)` ecosystem, it's no longer 
-necessary to bring large datasets to the model for training; you can take your 
-model -- in whole, or in part -- to where the data lives and save potentially 
-significant or quantifiable machine resources.  
+Quick Start
+===========
 
+We have many documentation pages to help you get started.  
 
+* **TensorFlow or MXNet users** can get started with :doc:`framework-integration-guides`; see also: 
 
-.. _adaptable: 
+   * `TensorFlow bridge to nGraph`_ 
+   * `Compiling MXNet with nGraph`_ 
 
-Adaptable
-=========
+* **Data scientists** interested in the `ONNX`_ format will find the 
+  `nGraph ONNX companion tool`_ of interest. 
 
-We've recently begun support for the `ONNX`_ format. Developers who already have 
-a "trained" :abbr:`DNN (Deep Neural Network)` model can use nGraph to bypass 
-significant framework-based complexity and :doc:`import it <howto/import>` 
-to test or run on targeted and efficient backends with our user-friendly 
-Python-based API. See the `ngraph onnx companion tool`_ to get started. 
+* **Framework authors and architects** will likely want to :doc:`buildlb` 
+  and learn how nGraph can be used to :doc:`howto/execute`. For examples 
+  of generic configurations or optimizations available when designing or 
+  bridging a framework directly with nGraph, see :doc:`frameworks/generic`.
 
+* To start learning about nGraph's set of **Core ops** and how they can 
+  be used with Ops from other frameworks, go to :doc:`ops/index`.
+
+* **Optimization pass writers** will find :doc:`fusion/index` useful. Also 
+  look for our upcoming documentation on :term:`quantization`.
+
+* For details about **PlaidML integration** and other nGraph runtime APIs, 
+  see the section :doc:`programmable/index`.
 
 .. csv-table::
    :header: "Framework", "Bridge Available?", "ONNX Support?"
@@ -73,33 +70,7 @@ Python-based API. See the `ngraph onnx companion tool`_ to get started.
    MXNet, Yes, Yes
    PaddlePaddle, Coming Soon, Yes
    PyTorch, No, Yes
-   CNTK, No, Yes
-   Other, Custom, Custom
-
-
-.. _deployable:
-
-Deployable
-==========
-
-It's no secret that the :abbr:`DL (Deep Learning)` ecosystem is evolving 
-rapidly. Benchmarking comparisons can be blown steeply out of proportion by 
-subtle tweaks to batch or latency numbers here and there. Where traditional 
-GPU-based training excels, inference can lag and vice versa. Sometimes what we
-care about is not "speed at training a large dataset" but rather latency 
-compiling a complex multi-layer algorithm locally, and then outputting back to 
-an edge network, where it can be analyzed by an already-trained model. 
-
-Indeed, when choosing among topologies, it is important to not lose sight of 
-the ultimate deployability and machine-runtime demands of your component in
-the larger ecosystem. It doesn't make sense to use a heavy-duty backhoe to 
-plant a flower bulb. Furthermore, if you are trying to develop an entirely 
-new genre of modeling for a :abbr:`DNN (Deep Neural Network)` component, it 
-may be especially beneficial to consider ahead of time how portable and 
-mobile you want that model to be within the rapidly-changing ecosystem.  
-With nGraph, any modern CPU can be used to design, write, test, and deploy 
-a training or inference model. You can then adapt and update that same core 
-model to run on a variety of backends:  
+   Other, Write your own, Custom
 
 
 .. csv-table::
@@ -108,25 +79,16 @@ model to run on a variety of backends:
 
    Intel® Architecture Processors (CPUs), Yes, Yes
    Intel® Nervana™ Neural Network Processor (NNPs), Yes, Yes
-   AMD\* GPUs, via PlaidML, Yes
-   NVIDIA\* GPUs, via PlaidML, Some 
    Intel® Architecture GPUs, Yes, Yes 
+   AMD\* GPUs, via PlaidML, Yes
    :abbr:`Field Programmable Gate Arrays (FPGA)` (FPGAs), Coming soon, Yes
+   NVIDIA\* GPUs, via PlaidML, Some 
    Intel Movidius™ Myriad™ 2 (VPU), Coming soon, Yes
-   Other, Not yet, Ask
-
-The value we're offering to the developer community is empowerment: we are
-confident that Intel® Architecture already provides the best computational 
-resources available for the breadth of ML/DL tasks. We welcome ideas and 
-`contributions`_ from the community. 
-
-Further project details can be found on our :doc:`project/about` page, or see 
-our :doc:`buildlb` guide for how to get started.
 
 
 .. note:: The Library code is under active development as we're continually 
-   adding support for more kinds of DL models and ops, framework compiler 
-   optimizations, and backends.
+   adding support for more kinds of DL models and ops, compiler optimizations, 
+   and backend optimizations.
 
 
 =======
@@ -158,8 +120,11 @@ Indices and tables
    * :ref:`search`   
    * :ref:`genindex`
 
-     
+
+
+.. _nGraph ONNX companion tool: https://github.com/NervanaSystems/ngraph-onnx
 .. _ONNX: http://onnx.ai
-.. _ngraph onnx companion tool: https://github.com/NervanaSystems/ngraph-onnx
 .. _Movidius: https://www.movidius.com/
-.. _contributions: https://github.com/NervanaSystems/ngraph#how-to-contribute
\ No newline at end of file
+.. _contributions: https://github.com/NervanaSystems/ngraph#how-to-contribute
+.. _TensorFlow bridge to nGraph: https://github.com/NervanaSystems/ngraph-tf/blob/master/README.md
+.. _Compiling MXNet with nGraph: https://github.com/NervanaSystems/ngraph-mxnet/blob/master/NGRAPH_README.md
\ No newline at end of file

doc/sphinx/source/programmable/index.rst

@@ -12,10 +12,12 @@ Backends are responsible for function execution and value allocation. They
 can be used to :doc:`carry out a programmed computation<../howto/execute>`
 from a framework by using a CPU or GPU; or they can be used with an *Interpreter* 
 mode, which is primarily intended for testing, to analyze a program, or for a 
-framework developer to develop a custom UI or API. 
+framework developer to develop customizations. Experimental APIs to support 
+current and future nGraph Backends are also available; see, for example, the 
+section on :ref:`plaidml_`.
 
 
-.. figure:: ../graphics/runtime.png
+.. figure:: ../graphics/backend-dgm.png
    :width: 650px
 
 
@@ -25,7 +27,6 @@ framework developer to develop a custom UI or API.
 
 
 
-
 Tensor
 =======
 
@@ -35,3 +36,21 @@ Tensor
 
 
 
+.. _plaidml_:
+
+PlaidML
+========
+
+The nGraph ecosystem has recently added initial (experimental) support for `PlaidML`_,
+which is an advanced :abbr:`Machine Learning (ML)` library that can further
+accelerate training models built on GPUs. When you select the ``PlaidML`` option
+as a backend, it behaves as an advanced tensor compiler that can further speed up
+training with large data sets.
+
+.. doxygenclass:: ngraph::runtime::plaidml::PlaidML_Backend
+   :project: ngraph
+   :members:
+
+
+
+.. _PlaidML: https://github.com/plaidml

doc/sphinx/source/project/index.rst

@@ -6,7 +6,6 @@ More about nGraph
 
 This section contains documentation about the project and how to contribute.
 
-
 .. toctree::
    :maxdepth: 1