Architecture and feature docs5 (#2146)

* New branch for doc changes

* Slightly out of scope doc requests to make some people happier;
this does technically change the API

* Doc versioning even with Beta release

* Don't remove markdown version of contrib guidelines after all

* Update changes from earlier branch

* Make sure ngraph-tf links to readme

README.md

-# nGraph Compiler Stack
+# nGraph Compiler Stack (Beta)
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/NervanaSystems/ngraph/blob/master/LICENSE) [![Build Status][build-status-badge]][build-status] 
 
@@ -16,12 +16,12 @@ workloads on CPU for inference, please refer to the links below.
 
 |  Framework (Version)       | Installation guide                     | Notes  
 |----------------------------|----------------------------------------|-----------------------------------
-| TensorFlow* 1.12           | [Pip package] or [Build from source]   | 17 [Validated workloads]
-| MXNet* 1.4                 | [Enable the module] or [Source compile]| 17 [Validated workloads]   
-| ONNX 1.3                   | [Pip package]                          | 14 [Validated workloads] 
+| TensorFlow* 1.12           | [Pip install](https://github.com/NervanaSystems/ngraph-tf) or [Build from source](https://github.com/NervanaSystems/ngraph-tf)   | 20 [Validated workloads]
+| MXNet* 1.3                 | [Pip install](https://github.com/NervanaSystems/ngraph-mxnet#Installation) or [Build from source](https://github.com/NervanaSystems/ngraph-mxnet#building-with-ngraph-support)| 18 [Validated workloads]   
+| ONNX 1.3                   | [Pip install](https://github.com/NervanaSystems/ngraph-onnx#installation)                          | 14 [Validated workloads] 
 
 Frameworks using nGraph Compiler stack to execute workloads have shown 
-**up to 45X** performance boost when compared to native framework 
+[**up to 45X**](https://ai.intel.com/ngraph-compiler-stack-beta-release/) performance boost when compared to native framework 
 implementations. We've also seen performance boosts running workloads that 
 are not included on the list of [Validated workloads], thanks to our 
 powerful subgraph pattern matching.
@@ -100,9 +100,6 @@ to improve it:
 [develop-without-lockin]: doc/sphinx/source/graphics/develop-without-lockin.png "Develop on any part of the stack wtihout lockin"
 [Movidius™ Myriad™ 2]:https://www.movidius.com/solutions/vision-processing-unit
 [PlaidML]: https://github.com/plaidml/plaidml
-[Pip package]: https://github.com/NervanaSystems/ngraph-onnx#installing-ngraph-onnx
-[Build from source]: https://github.com/NervanaSystems/ngraph-tf
-[Enable the module]: https://github.com/NervanaSystems/ngraph/blob/mbrookhart/mxnet_tutorial/doc/sphinx/source/shared/mxnet_tutorial.rst
 [Source compile]: https://github.com/NervanaSystems/ngraph-mxnet/blob/master/README.md
 [nGraph-ONNX]: https://github.com/NervanaSystems/ngraph-onnx/blob/master/README.md
 [nGraph-ONNX adaptable]: https://ai.intel.com/adaptable-deep-learning-solutions-with-ngraph-compiler-and-onnx/

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

@@ -1634,7 +1634,7 @@ body {
   color: #38403f;
   min-height: 100%;
   overflow-x: hidden;
-  background: #edf0f2;
+  background: #fcfcfc;
 }
 
 .wy-text-left {
@@ -3193,7 +3193,7 @@ footer span.commit code, footer span.commit .rst-content tt, .rst-content footer
 }
 @media screen and (min-width: 1400px) {
   .wy-nav-content-wrap {
-    background: #0C7881;
+    background: #fcfcfc;
   }
 
   .wy-nav-content {

doc/sphinx/source/conf.py

@@ -73,9 +73,11 @@ author = 'Intel Corporation'
 # built documents.
 #
 # The short X.Y version.
-version = '0.9'
-# The full version, including alpha/beta/rc tags.
-release = '0.9.0'
+version = '0.10'
+# The Documentation full version, including alpha/beta/rc tags. Some features
+# available in the latest code will not necessarily be documented first
+release = '0.10.1'
+
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

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

@@ -50,4 +50,4 @@ nGraph-TensorFlow bridge.
 .. _MXNet: http://mxnet.incubator.apache.org
 .. _DSO: http://csweb.cs.wfu.edu/%7Etorgerse/Kokua/More_SGI/007-2360-010/sgi_html/ch03.html
 .. _being the fastest: https://github.com/soumith/convnet-benchmarks
-.. _ngraph tensorflow bridge README: https://github.com/NervanaSystems/ngraph-tf
+.. _ngraph tensorflow bridge README: https://github.com/NervanaSystems/ngraph-tf/blob/master/README.md

doc/sphinx/source/frameworks/validation-testing.rst

@@ -15,19 +15,22 @@ TensorFlow
    :widths: 27, 53
    :escape: ~
 
-   Resnet50 v1 and v2, Image recognition
-   Inception V3 and V4, Image recognition
+   Resnet50 v1, Image recognition
+   Resnet50 v2, Image recognition
+   Inception V3, Image recognition
+   Inception V4, Image recognition
    Inception-ResNetv2, Image recognition
    MobileNet v1, Image recognition
-   SqueezeNet v1.1, Image recognition
-   DenseNet-121, Image recognition
+   MobileNet v2, Image recognition
+   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
    NCF, Recommender system
-   WaveNet, Speech generation
    U-Net, Image segmentation
    DCGAN, Generative adversarial network
    DRAW, Image generation
@@ -41,7 +44,8 @@ MXNet
    :widths: 27, 53
    :escape: ~
 
-   Resnet50 v1 and v2, Image recognition
+   Resnet50 v1, Image recognition
+   Resnet50 v2, Image recognition
    DenseNet-121, Image recognition
    InceptionV3, Image recognition
    InceptionV4, Image recognition
@@ -70,10 +74,10 @@ Additionally, we validated the following workloads are functional through nGraph
    :widths: 27, 53
    :escape: ~
 
+   ResNet-50, Image recognition
    DenseNet-121, Image recognition
    Inception-v1, Image recognition
    Inception-v2, Image recognition
-   ResNet-50, Image recognition
    Shufflenet, Image recognition
    SqueezeNet, Image recognition
    VGG-19, Image recognition