Fix ops, cleanup ToC, add latest illustrations (#3700)

* Fix ops, cleanup ToC, add latest illustrations

* PR feedback

doc/sphinx/ngraph_theme/layout.html

@@ -139,9 +139,9 @@
 
         <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
           {% block menu %}
-            {% set toctree = toctree(maxdepth=2, collapse=theme_collapse_navigation, includehidden=True) %}
+            {% set toctree = toctree(maxdepth=2, collapse=theme_collapse_navigation, includehidden=False) %}
             {% if toctree %}
-                {{ toctree }}
+            <span class="toctree-expand">{{ toctree }}</span>
             {% else %}
                 <!-- Local TOC -->
                 <div class="local-toc">{{ toc }}</div>

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

@@ -1622,6 +1622,7 @@ a.wy-text-neutral:hover {
 
 h1, h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend {
   margin-top: 0.33em;
+  padding-top: 0.31em;
   font-weight: 700;
   font-family: Nunito, 'Nunito Sans', "IntelClear-Regular", Lato, Helvetica, "Helvetica Neue", sans;
 }
@@ -1630,8 +1631,8 @@ p {
   line-height: 23px;
   padding-bottom: 0.11em;
   padding-top: 0.11em;
-  font-size: 1.19em;
-  margin-bottom: 0.27em;
+  font-size: 1.07em;
+  margin-bottom: 0.31em;
   margin-top: 0.27em;
   font-family: Nunito, 'Nunito Sans', Helvetica, 'Helvetica Neue', sans;
 }
@@ -1696,7 +1697,6 @@ code.code-large, .rst-content tt.code-large {
 
 .wy-plain-list-disc, .rst-content .section ul, .rst-content .toctree-wrapper ul, article ul {
   list-style: disc;
-  line-height: 24px;
   margin-bottom: 24px;
 }
 .wy-plain-list-disc li, .rst-content .section ul li, .rst-content .toctree-wrapper ul li, article ul li {
@@ -1721,7 +1721,6 @@ code.code-large, .rst-content tt.code-large {
 
 .wy-plain-list-decimal, .rst-content .section ol, .rst-content ol.arabic, article ol {
   list-style: decimal;
-  line-height: 24px;
   margin-bottom: 0.5em;
 }
 .wy-plain-list-decimal li, .rst-content .section ol li, .rst-content ol.arabic li, article ol li {
@@ -2428,8 +2427,9 @@ div[class^='highlight'] pre {
   line-height: 1.0em;
 }
 .rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
-  font-size: 101% !important;
-  color: #2e2b27;
+  font-size: 91% !important;
+  color: #152a58;
+  text-decoration: underline 2px dotted #cdcac5;
   background-color: #fff;
   line-height: 0.8955em;
 }
@@ -2654,9 +2654,9 @@ span[id*='MathJax-Span'] {
   /* border-right: 5px solid #999;*/
 }
 .wy-menu-vertical header, .wy-menu-vertical p.caption {
-  height: 33.1px;
+  height: 29.1px;
   display: inline-block;
-  line-height: 35.3px;
+  line-height: 31.3px;
   padding: 0.1 0.1 0.1 0.1;
   margin-left: 0.43em;
   padding-left: 0.23em;
@@ -2666,7 +2666,7 @@ span[id*='MathJax-Span'] {
   font-weight: bolder;
   border-top: 5px dotted #FFA400;
   border-bottom: 29px solid #d3d3d3;
-  font-size: 139%;
+  font-size: 131%;
   color: #393F4D;
   width: auto;
   white-space: nowrap;
@@ -2720,15 +2720,16 @@ span[id*='MathJax-Span'] {
   font-size: 0.8em;
   line-height: 1.6em;
   color: #393f4d;
+    max-height: 0;
+  overflow: hidden;
+
 }
 .wy-menu-vertical li.on a, .wy-menu-vertical li.current > a {
   font-family: Nunito, 'Nunito Sans', Helvetica, 'Helvetica Neue', sans;
-  position: relative;
-  padding-left: 1.1em;
+  padding-left: 0.55em;
   background-color: #fafbfd;
   color: #5c5955;
   border-right: 4px solid #FFA400;
-  margin-left: 23px;
 
 }
 .wy-menu-vertical li.on a:hover, .wy-menu-vertical li.current > a:hover {
@@ -2806,10 +2807,9 @@ span[id*='MathJax-Span'] {
 }
 .wy-menu-vertical a {
   display: inline-block;
-  line-height: 1.21em;
+  line-height: 0.93em;
   padding: 0.4045em 1.31em;
   display: block;
-  position: relative;
   font-size: 96%;
   font-family: Nunito, 'Nunito Sans', Helvetica, 'Helvetica Neue', sans;
 }
@@ -2830,6 +2830,8 @@ span[id*='MathJax-Span'] {
   color: #555;
 }
 
+
+
 .wy-side-nav-search {
   display: block;
   width: 300px;

doc/sphinx/source/backends/index.rst

@@ -9,7 +9,6 @@ Working with Backends
 * :ref:`ngraph_bridge`
 * :ref:`opencl`
 
-
 .. _what_is_backend:
 
 What is a backend?

doc/sphinx/source/features.rst

+.. features.rst
+
+.. _features:
+
+Features
+========
+
+What follows here are a few notable features of the nGraph Compiler stack. 
+
+..  as well as a brief illustration or demonstration of that feature.
+
+* **Fusion** -- Fuse multiple ops to to decrease memory usage.
+* **Data layout abstraction** -- Make abstraction easier and faster with nGraph 
+  translating element order to work best for a given or available device.
+* **Data reuse** -- Save results and reuse for subgraphs with the same input.
+* **Graph scheduling** -- Run similar subgraphs in parallel via multi-threading.
+* **Graph partitioning** -- Partition subgraphs to run on different devices to 
+  speed up computation; make better use of spare CPU cycles with nGraph.
+* **Memory management** -- Prevent peak memory usage by intercepting a graph 
+  with or by a "saved checkpoint," and to enable data auditing.
+

doc/sphinx/source/frameworks/generic-configs.rst → doc/sphinx/source/frameworks/generic_configs.rst

-.. frameworks/generic-configs.rst:
+.. frameworks/generic_configs.rst:
+
+.. _generic_configs: 
 
 Integrating new frameworks
 ==========================
@@ -20,7 +22,7 @@ something like:
 
 
 Find or display version
-=======================
+-----------------------
 
 If you're working with the :doc:`../python_api/index`, the following command 
 may be useful:
@@ -35,7 +37,7 @@ documentation.
 
 
 Activate logtrace-related environment variables
-===============================================
+-----------------------------------------------
 
 Another configuration option is to activate ``NGRAPH_CPU_DEBUG_TRACER``,
 a runtime environment variable that supports extra logging and debug detail. 

doc/sphinx/source/frameworks/index.rst

 .. frameworks/index.rst
 
-Using Frameworks
-================
+
+Working with Frameworks
+=======================
+
+.. include:: overview.rst
 
 .. toctree::
    :maxdepth: 1
@@ -10,3 +13,4 @@ Using Frameworks
    onnx_integ.rst 
    paddle_integ.rst
    tensorflow_connect.rst
+   generic_configs.rst

doc/sphinx/source/frameworks/overview.rst

+.. frameworks/overview.rst
+
+.. _fw_overview:
+
+Overview
+========
+
+A framework is "supported" with a framework :term:`bridge` that can be written or
+cloned and used to connect to nGraph device backends while maintaining the 
+framework's programmatic or user interface. A `bridge currently exists`_ for the 
+TensorFlow framework. We also have a bridge to do :doc:`paddle_integ`.  Intel 
+previously contributed work to an MXNet bridge; however, support for this 
+bridge is no longer active.
+
+`ONNX`_ on its own is not a framework; however, it can be used with nGraph's
+:doc:`../python_api/index` to import and execute ONNX models.
+
+.. figure:: ../graphics/overview-framework-bridges.svg
+   :width: 960px
+   :alt: JiT compiling of a computation
+
+   :abbr:`Just-in-Time (JiT)` Compiling for computation. nGraph `Core`
+   components are colored in blue.
+
+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
+as an optimizing compiler available through the framework.
+
+While a :abbr:`Deep Learning (DL)` :term:`framework` is ultimately meant for
+end use by data scientists, or for deployment in cloud container environments,
+nGraph Core ops and the nGraph C++ Library are designed for framework builders
+themselves. We invite anyone working on new and novel frameworks or neural
+network designs to explore our highly-modularized stack of components that
+can be implemented or integrated in countless ways.
+
+Please read this section if you are considering incorporating components from
+the nGraph Compiler stack in your framework or neural network design. Contents
+here are also useful if you are working on something built-from-scratch, or on
+an existing framework that is less widely-supported than the popular frameworks
+like TensorFlow and PyTorch.
+
+.. figure:: ../graphics/overview-translation-flow.svg
+   :width: 725px
+   :alt: Translation flow to nGraph function graph
+
+
+
+.. _bridge currently exists: https://github.com/tensorflow/ngraph-bridge/README.md
+.. _ONNX: http://onnx.ai/
+.. _tune the workload to extract best performance: https://ai.intel.com/accelerating-deep-learning-training-inference-system-level-optimizations
+.. _a few small: https://software.intel.com/en-us/articles/boosting-deep-learning-training-inference-performance-on-xeon-and-xeon-phi
\ No newline at end of file

doc/sphinx/source/frameworks/validated/list.rst

 .. frameworks/validated/list.rst: 
 
-#################################
-Validated workloads by framework
-#################################
+.. _validated:
 
-We validated performance [#f1]_ for the following TensorFlow\* and MXNet\* 
-workloads:
 
-* :ref:`tensorflow_valid`
-* :ref:`mxnet_valid`
-* :ref:`onnx_valid`
-* :ref:`testing_latency`
+Validated workloads
+###################
 
-.. _tensorflow_valid:
+We have validated performance [#f1]_ for the following workloads:
 
-TensorFlow 
-==========
+.. contents::
+   :local:
+
+.. _cpu_tensorflow:
+
+CPU Tensorflow
+==============
 
 .. csv-table::
-   :header: "TensorFlow Workload", "Genre of Deep Learning"
+   :header: "TensorFlow Workload", "Genre of Deep learning"
    :widths: 27, 53
    :escape: ~
 
@@ -28,12 +27,11 @@ TensorFlow
    Inception V4, Image recognition
    Inception-ResNetv2, Image recognition
    MobileNet v1, Image recognition
-   MobileNet v2, Image recognition
+   Faster RCNN, Object detection
    VGG16, Image recognition
    SSD-VGG16, Object detection
    SSD-MobileNetv1, Object detection
    R-FCN, Object detection
-   Faster RCNN, Object detection
    Yolo v2, Object detection
    Transformer-LT, Language translation
    Wide & Deep, Recommender system
@@ -44,40 +42,10 @@ TensorFlow
    A3C, Reinforcement learning
 
 
-.. _mxnet_valid:
-
-MXNet
-=====
+.. _cpu_onnx:
 
-.. csv-table::
-   :header: "MXNet Workload", "Genre of Deep Learning"
-   :widths: 27, 53
-   :escape: ~
-
-   Resnet50 v1, Image recognition
-   Resnet50 v2, Image recognition
-   DenseNet-121, Image recognition
-   InceptionV3, Image recognition
-   InceptionV4, Image recognition
-   Inception-ResNetv2, Image recognition
-   MobileNet v1, Image recognition
-   SqueezeNet v1 and v1.1, Image recognition
-   VGG16, Image recognition
-   Faster RCNN, Object detection
-   SSD-VGG16, Object detection
-   GNMT, Language translation
-   Transformer-LT, Language translation
-   Wide & Deep, Recommender system
-   WaveNet, Speech generation
-   DeepSpeech2, Speech recognition
-   DCGAN, Generative adversarial network
-   A3C, Reinforcement learning
-
-
-.. _onnx_valid:
-
-ONNX
-====
+CPU ONNX
+========
 
 Additionally, we validated the following workloads are functional through 
 `nGraph ONNX importer`_. ONNX models can be downloaded from the `ONNX Model Zoo`_.
@@ -87,15 +55,14 @@ Additionally, we validated the following workloads are functional through
    :widths: 27, 53
    :escape: ~
 
-   ResNet-50, Image recognition
-   ResNet-50-v2, Image recognition
    DenseNet-121, Image recognition
    Inception-v1, Image recognition
    Inception-v2, Image recognition
+   ResNet-50, Image recognition
    Mobilenet, Image recognition
    Shufflenet, Image recognition
    SqueezeNet, Image recognition
-   VGG-19, Image recognition
+   VGG-16, Image recognition
    ZFNet-512, Image recognition
    MNIST, Image recognition
    Emotion-FERPlus, Image recognition
@@ -106,6 +73,40 @@ Additionally, we validated the following workloads are functional through
    ArcFace, Face Detection and Recognition
 
 
+.. _gpu_tensorflow:
+
+GPU TensorFlow
+==============
+
+.. csv-table::
+   :header: "TensorFlow Workload", "Genre of Deep Learning"
+   :escape: ~
+
+
+   Resnet50 v2, Image recognition 
+   Inception V3, Image recognition
+   Inception V4, Image recognition
+   Inception-ResNetv2, Image recognition
+   VGG-16, Image recognition 
+
+
+.. _gpu_onnx:
+
+GPU ONNX
+========
+
+.. csv-table::
+   :header: "ONNX Workload", "Genre of Deep Learning"
+   :escape: ~
+
+   Inception V1, Image recognition 
+   Inception V2, Image recognition 
+   ResNet-50, Image recognition 
+   SqueezeNet, Image recognition 
+   
+
+
+
 .. important:: Please see Intel's `Optimization Notice`_ for details on disclaimers. 
 
 .. rubric:: Footnotes
@@ -133,4 +134,3 @@ Additionally, we validated the following workloads are functional through
    to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the 
    applicable product User and Reference Guides for more information regarding the specific 
    instruction sets covered by this notice.
-

doc/sphinx/source/graphics/overview-framework-bridges.svg

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\ No newline at end of file

doc/sphinx/source/index.rst

@@ -47,11 +47,16 @@ packages, scripts, and other files that use licensing.
 .. toctree::
    :maxdepth: 1
    :caption: Getting Started
-   :titlesonly:
+
+   introduction.rst
+   features.rst
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Framework Support
 
    frameworks/index.rst
    frameworks/validated/list.rst
-   frameworks/generic-configs.rst
 
 
 .. toctree::
@@ -68,15 +73,9 @@ packages, scripts, and other files that use licensing.
    
 .. toctree::
    :maxdepth: 1
-   :caption: nGraph Python API
+   :caption: APIs 
 
    python_api/index.rst
-
-   
-.. toctree::
-   :maxdepth: 1
-   :caption: Backend Developers
-
    backends/index.rst
    backends/cpp-api.rst
 
@@ -93,9 +92,7 @@ packages, scripts, and other files that use licensing.
    :caption: Project Metadata
 
    project/release-notes.rst
-   project/introduction.rst
    project/contribution-guide.rst
-   project/doc-contributor-README.rst
    project/index.rst
    project/extras/index.rst 
    glossary.rst

doc/sphinx/source/project/introduction.rst → doc/sphinx/source/introduction.rst

-.. project/introduction.rst:
+.. introduction.rst:
 
+.. _introduction:
 
 Introduction
 ############
@@ -47,7 +48,7 @@ However, kernel libraries have three main problems:
 nGraph Compiler addresses the first two problems, and nGraph Compiler combined
 with PlaidML addresses the third problem. nGraph applies graph-level
 optimizations by taking the computational graph from a deep learning framework
-such as TensorFlow\* and reconstructing it with nGraph's  
+such as TensorFlow and reconstructing it with nGraph's 
 :abbr: `IR (Intermediate Representation)`. nGraph IR centralizes computational 
 graphs from various frameworks and provides a unified way to connect backends 
 for targeted hardware. To address the third problem, nGraph is integrated with 
@@ -68,10 +69,13 @@ graph, but the choice of operations in the graph may not be optimal.
 
 .. _figure-A:
 
-.. figure:: ../graphics/kernel-problem-1.png
+.. figure:: graphics/kernel-problem-1.png
    :width: 100%
    :alt:
 
+   **Figure A**: The mathematical operations in a Deep Learning stack can be 
+   simplified significantly with a graph compiler
+
 
 The computation is constructed to execute ``(A+B)*C``. With nGraph, we can
 further optimize the graph to be represented as ``A*C``. From the first graph
@@ -94,10 +98,12 @@ diagram.
 
 .. _figure-B:
 
-.. figure:: ../graphics/kernel-problem-2.png
+.. figure:: graphics/kernel-problem-2.png
    :width: 100%
    :alt:
 
+   **Figure B**:  A many-to-many problem
+
 Each framework must be manually integrated with each hardware-specific kernel
 library. Additionally, each integration is unique to the framework and its set
 of deep learning operators, view on memory layout, feature set, etc. Each
@@ -129,10 +135,12 @@ work for what will ultimately be a fragile setup that is costly to maintain.
 
 .. _figure-C:
 
-.. figure:: ../graphics/kernel-problem-3.png
+.. figure:: graphics/kernel-problem-3.png
    :width: 100%
    :alt:
 
+   **Figure C**:  Inevitable scaling problem
+
 
 Integrating PlaidML with nGraph provides flexbility to support the latest deep
 learning models in the absence of hand-optimized kernels for new operations.

doc/sphinx/source/ops/avg_pool.rst

@@ -195,7 +195,7 @@ Backprop
 C++ Interface
 =============
 
-.. doxygenclass:: ngraph::op::AvgPool
+.. doxygenclass:: ngraph::op::v0::AvgPool
    :project: ngraph
    :members:
 

doc/sphinx/source/ops/avg_pool_backprop.rst

@@ -17,7 +17,7 @@ Description
 C++ Interface
 =============
 
-.. doxygenclass:: ngraph::op::AvgPoolBackprop
+.. doxygenclass:: ngraph::op::v0::AvgPoolBackprop
    :project: ngraph
    :members:
 

doc/sphinx/source/ops/convolution.rst

@@ -138,7 +138,7 @@ Batched, Padded, Dilated, Strided Convolution
 C++ Interface
 =============
 
-  .. doxygenclass:: ngraph::op::Convolution
+  .. doxygenclass:: ngraph::op::v0::Convolution
      :project: ngraph
      :members:
 

doc/sphinx/source/ops/max_pool.rst

@@ -78,6 +78,6 @@ Given an input data batch tensor :math:`T_{in}`, the output tensor is defined by
 C++ Interface
 =============
 
-.. doxygenclass:: ngraph::op::MaxPool
+.. doxygenclass:: ngraph::op::v0::MaxPool
    :project: ngraph
    :members:

doc/sphinx/source/project/about.rst

-.. about: 
-
-Architecture, Features, FAQs
-############################
-
-* :ref:`architecture`
-* :ref:`features`
-* :ref:`faq`
-* :ref:`whats_next`
-
-
-.. _architecture:
-
-nGraph Compiler stack architecture
-==================================
-
-The diagram below represents our current Beta release stack. In the
-diagram, nGraph components are colored in gray. Please note that the
-stack diagram is simplified to show how nGraph executes deep learning
-workloads with two hardware backends; however, many other deep learning
-frameworks and backends currently are functioning.
-
-.. figure:: ../graphics/stackngrknl-notice.png
-   :alt: 
-
-Bridge
-------
-
-Starting from the top of the stack, nGraph receives a computational
-graph from a deep learning framework such as TensorFlow\* or MXNet\*.
-The computational graph is converted to an nGraph internal
-representation by a bridge created for the corresponding framework.
-
-An nGraph bridge examines the whole graph to pattern match subgraphs
-which nGraph knows how to execute, and these subgraphs are encapsulated.
-Parts of the graph that are not encapsulated will default to framework
-implementation when executed.
-
-nGraph Core
------------
-
-nGraph uses a strongly-typed and platform-neutral
-``Intermediate Representation (IR)`` to construct a "stateless"
-computational graph. 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.
-
-This allows nGraph to apply its state of the art optimizations instead
-of having to follow how a particular framework implements op execution,
-memory management, data layouts, etc.
-
-In addition, using nGraph IR allows faster optimization delivery for
-many of the supported frameworks. For example, if nGraph optimizes
-ResNet\* for TensorFlow\ *, the same optimization can be readily applied
-to MXNet* or ONNX\* implementations of ResNet\*.
-
-Hybrid Transformer
-------------------
-
-Hybrid transformer takes the nGraph IR, and partitions it into
-subgraphs, which can then be assigned to the best-performing backend.
-There are two hardware backends shown in the stack diagram to
-demonstrate this graph partitioning. The Hybrid transformer assigns
-complex operations (subgraphs) to Intel® Nervana™ Neural Network
-Processor (NNP) to expedite the computation, and the remaining
-operations default to CPU. In the future, we will further expand the
-capabilities of Hybrid transformer by enabling more features, such as
-localized cost modeling and memory sharing.
-
-Once the subgraphs are assigned, the corresponding backend will execute
-the IR.
-
-Backends
---------
-
-Focusing our attention on the CPU backend, when the IR is passed to the
-Intel® Architecture (IA) transformer, it can be executed in two modes:
-Direct EXecution (DEX) and code generation (``codegen``).
-
-In ``codegen`` mode, nGraph generates and compiles code which can either
-call into highly optimized kernels like MKL-DNN or JITers like Halide.
-Although our team wrote kernels for nGraph for some operations, nGraph
-leverages existing kernel libraries such as MKL-DNN, Eigen, and MLSL.
-
-MLSL library is called when nGraph executes distributed training. At the
-time of the nGraph Beta release, nGraph achieved state of the art
-results for ResNet50 with 16 nodes and 32 nodes for TensorFlow\* and
-MXNet\*. We are excited to continue our work in enabling distributed
-training, and we plan to expand to 256 nodes in Q4 ‘18. Additionally, we
-are testing model parallelism in addition to data parallelism.
-
-The other mode of execution is Direct EXecution (DEX). In DEX mode,
-nGraph can execute the operations by directly calling associated kernels
-as it walks though the IR instead of compiling via ``codegen``. This
-mode reduces the compilation time, and it will be useful for training,
-deploying, and retraining a deep learning workload in production. In our
-tests, DEX mode reduced ResNet50 compilation time by 30X.
-
-nGraph further tries to speed up the computation by leveraging
-multi-threading and graph scheduling libraries such as OpenMP and TBB
-Flow Graph.
-
-.. _features:
-
-Features
-########
-
-nGraph performs a combination of device-specific and non-device-specific
-optimizations:
-
--  **Fusion** -- Fuse multiple ops to to decrease memory usage.
--  **Data layout abstraction** -- Make abstraction easier and faster
-   with nGraph translating element order to work best for a given or
-   available device.
--  **Data reuse** -- Save results and reuse for subgraphs with the same
-   input.
--  **Graph scheduling** -- Run similar subgraphs in parallel via
-   multi-threading.
--  **Graph partitioning** -- Partition subgraphs to run on different
-   devices to speed up computation; make better use of spare CPU cycles
-   with nGraph.
--  **Memory management** -- Prevent peak memory usage by intercepting a
-   graph with or by a "saved checkpoint," and to enable data auditing.
--  **Data layout abstraction** -- Make abstraction easier and faster
-   with nGraph translating element order to work best for whatever given
-   or available device.
-
-.. _faq:
-
-FAQs
-####
-
-Why nGraph? 
-===========
-
-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. 
-
-How does it work?
-=================
-
-The :doc:`nGraph Core <../ops/index>` uses a **strongly-typed** and 
-**platform-neutral** :abbr:`Intermediate Representation (IR)` to construct a 
-"stateless" graph. 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. 
-
-
-How do I connect a framework? 
-=============================
-
-The nGraph Library manages framework bridges for some of the more widely-known 
-frameworks. A bridge acts as an intermediary between the nGraph core and the 
-framework, and the result is a function that can be compiled from a framework. 
-A fully-compiled function that makes use of bridge code thus becomes a "function
-graph", or what we sometimes call an **nGraph graph**.  
-
-.. important:: See :doc:`../ops/index` to learn about Core Ops.
-
-Our design philosophy is that the graph is not a script for running kernels; 
-rather, our compilation will match ``ops`` to appropriate available kernels
-(or when available, such as with CPU cycles). Thus, we expect that adding of 
-new Core ops should be infrequent and that most functionality instead gets 
-added with new functions that build sub-graphs from existing core ops.   
-
-For a more detailed dive into how custom bridge code can be implemented, see our 
-documentation on how to :doc:`../core/constructing-graphs/execute`. To learn how TensorFlow and 
-MXNet currently make use of custom bridge code, see the section on 
-:doc:`../frameworks/index`.
-
-.. figure:: ../graphics/bridge-to-graph-compiler.png
-    :width: 733px
-    :alt: Compiling a computation
-
-    JiT Compiling for computation
-
-
-How do I run an inference model?
-================================
-
-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:`../core/constructing-graphs/import` documentation.  
-
-
-.. _whats_next:
-
-What's next?
-############
-  
-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.
-
-
-.. _arXiv paper: https://arxiv.org/pdf/1801.08058.pdf
-.. _ONNX: http://onnx.ai
-.. _NNVM: https://github.com/dmlc/nnvm
-.. _nGraph ONNX companion tool: https://github.com/NervanaSystems/ngraph-onnx
-.. _Intel® MKL-DNN: https://github.com/intel/mkl-dnn
-.. _Movidius: https://developer.movidius.com/
-

doc/sphinx/source/project/contribution-guide.rst

 .. project/contribution-guide.rst:
 
 
-.. contribution_guide:
+.. _contribution_guide:
 
 ##################
 Contribution guide
 ##################
 
+
+.. contents:: 
+
 License
 =======
 

doc/sphinx/source/project/doc-contributor-README.rst

@@ -15,6 +15,8 @@
 .. limitations under the License.
 .. ---------------------------------------------------------------------------
 
+:orphan:
+
 Contributing to documentation
 =============================
 

doc/sphinx/source/project/index.rst

@@ -10,7 +10,5 @@ This section contains documentation about the project and how to contribute.
 .. toctree::
    :maxdepth: 2
 
-   about.rst
    contribution-guide.rst
    governance.rst
-   doc-contributor-README.rst