Migrate doc changes to r0.18 (#2738)

* Migrate doc changes

* Add TensorFlow version change

README.md

@@ -14,7 +14,7 @@ workloads on CPU for inference, please refer to the links below.
 
 |  Framework (Version)       | Installation guide                     | Notes  
 |----------------------------|----------------------------------------|-----------------------------------
-| TensorFlow* 1.12           | [Pip install](https://github.com/NervanaSystems/ngraph-tf#option-1-use-a-pre-built-ngraph-tensorflow-bridge) or [Build from source](https://github.com/NervanaSystems/ngraph-tf#option-2-build-ngraph-bridge-from-source)   | 20 [Validated workloads]
+| TensorFlow* 1.13.1         | [Pip install](https://github.com/NervanaSystems/ngraph-tf#option-1-use-a-pre-built-ngraph-tensorflow-bridge) or [Build from source](https://github.com/NervanaSystems/ngraph-tf#option-2-build-ngraph-bridge-from-source)   | 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 1.4                   | [Pip install](https://github.com/NervanaSystems/ngraph-onnx#installation)                          | 17 [Validated workloads] 
 

doc/sphinx/source/buildlb.rst

@@ -23,7 +23,7 @@ packages and prerequisites:
    :widths: 25, 15, 25, 20, 25
    :escape: ~
 
-   CentOS 7.4 64-bit, GCC 4.8, CMake 3.4.3, supported, ``wget zlib-devel ncurses-libs ncurses-devel patch diffutils gcc-c++ make git perl-Data-Dumper`` 
+   CentOS 7.4 64-bit, GCC 4.8, CMake 3.5.0, supported, ``wget zlib-devel ncurses-libs ncurses-devel patch diffutils gcc-c++ make git perl-Data-Dumper`` 
    Ubuntu 16.04 or 18.04 (LTS) 64-bit, Clang 3.9, CMake 3.5.1 + GNU Make, supported, ``build-essential cmake clang-3.9 clang-format-3.9 git curl zlib1g zlib1g-dev libtinfo-dev unzip autoconf automake libtool``
    Clear Linux\* OS for Intel Architecture, Clang 5.0.1, CMake 3.10.2, experimental, bundles ``machine-learning-basic dev-utils python3-basic python-basic-dev``
 
@@ -185,13 +185,13 @@ The process documented here will work on CentOS 7.4.
 
    .. code-block:: console
     
-      $ wget https://cmake.org/files/v3.4/cmake-3.4.3.tar.gz      
-      $ tar -xzvf cmake-3.4.3.tar.gz
-      $ cd cmake-3.4.3
+      $ wget https://cmake.org/files/v3.4/cmake-3.5.0.tar.gz      
+      $ tar -xzvf cmake-3.5.0.tar.gz
+      $ cd cmake-3.5.0
       $ ./bootstrap --system-curl --prefix=~/cmake
       $ make && make install     
 
-#. Clone the `NervanaSystems` ``ngraph`` repo via HTTPS and use Cmake 3.4.3 to 
+#. Clone the `NervanaSystems` ``ngraph`` repo via HTTPS and use Cmake 3.5.0 to 
    build nGraph Libraries to ``~/ngraph_dist``. This command enables ONNX 
    support in the library (optional). 
 

doc/sphinx/source/conf.py

@@ -73,11 +73,11 @@ author = 'Intel Corporation'
 # built documents.
 #
 # The short X.Y version.
-version = '0.17'
+version = '0.18'
 
 # The Documentation full version, including alpha/beta/rc tags. Some features
 # available in the latest code will not necessarily be documented first
-release = '0.17.0'
+release = '0.18.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

doc/sphinx/source/frameworks/index.rst

@@ -18,7 +18,7 @@ cloned from one of our GitHub repos and built to connect to nGraph device
 backends while maintaining the framework's programmatic or user interface. Bridges 
 currently exist for the TensorFlow\* and MXNet\* frameworks. 
 
-ONNX is not a framework; however, it can be used with nGraph's :doc:../python_api/index` 
+ONNX is not a framework; however, it can be used with nGraph's :doc:`../python_api/index` 
 to import and execute ONNX models.   
 
 .. figure:: ../graphics/whole-stack.png
@@ -49,7 +49,7 @@ like TensorFlow and PyTorch.
    :width: 725px
    :alt: Translation flow to nGraph function graph
 
-
-
 .. _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
+
+

doc/sphinx/source/ops/quantize.rst

-.. quantize.rst: 
+.. ops/quantize.rst:
 
 ########
 Quantize