fixed main product TOC, removed ref from the second-level items

openvinotoolkit · Mar 16, 2022 · 9bd2d25 · 9bd2d25
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diff --git a/docs/IE_PLUGIN_DG/QuantizedNetworks.md b/docs/IE_PLUGIN_DG/QuantizedNetworks.md
@@ -9,7 +9,7 @@ For more details about low-precision model representation please refer to this [
 During the model load each plugin can interpret quantization rules expressed in *FakeQuantize* operations:
 - Independently based on the definition of *FakeQuantize* operation.
 - Using a special library of low-precision transformations (LPT) which applies common rules for generic operations,
-such as Convolution, Fully-Connected, Eltwise, etc., and translates "fake-quantized" models into the models with low-precision operations. For more information about low-precision flow please refer to the following [document](@ref openvino_docs_IE_DG_Int8Inference). 
+such as Convolution, Fully-Connected, Eltwise, etc., and translates "fake-quantized" models into the models with low-precision operations. For more information about low-precision flow please refer to the following [document](../OV_Runtime_UG/Int8Inference.md). 
 
 Here we provide only a high-level overview of the interpretation rules of FakeQuantize. 
 At runtime each FakeQuantize can be split into two independent operations: **Quantize** and **Dequantize**. 

diff --git a/docs/OV_Runtime_UG/Bfloat16Inference.md b/docs/OV_Runtime_UG/Bfloat16Inference.md
@@ -1,4 +1,4 @@
-# Bfloat16 Inference {#openvino_docs_IE_DG_Bfloat16Inference}
+# Bfloat16 Inference
 
 ## Bfloat16 Inference Usage (C++)
 

diff --git a/docs/OV_Runtime_UG/Int8Inference.md b/docs/OV_Runtime_UG/Int8Inference.md
@@ -1,4 +1,4 @@
-# Low-Precision 8-bit Integer Inference {#openvino_docs_IE_DG_Int8Inference}
+# Low-Precision 8-bit Integer Inference
 
 ## Disclaimer
 

diff --git a/docs/OV_Runtime_UG/ov_dynamic_shapes.md b/docs/OV_Runtime_UG/ov_dynamic_shapes.md
@@ -1,15 +1,5 @@
 # Dynamic Shapes {#openvino_docs_OV_UG_DynamicShapes}
 
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   openvino_docs_OV_UG_NoDynamicShapes
-
-@endsphinxdirective
-
 As it was demonstrated in the [Changing Input Shapes](ShapeInference.md) article, there are models that support changing of input shapes before model compilation in `Core::compile_model`.
 Reshaping models provides an ability to customize the model input shape for exactly that size that is required in the end application.
 This article explains how the ability of model to reshape can further be leveraged in more dynamic scenarios.

diff --git a/docs/OV_Runtime_UG/ov_without_dynamic_shapes.md b/docs/OV_Runtime_UG/ov_without_dynamic_shapes.md
@@ -1,4 +1,4 @@
-# When Dynamic Shapes API is Not Applicable  {#openvino_docs_OV_UG_NoDynamicShapes}
+# When Dynamic Shapes API is Not Applicable
 
 Several approaches to emulate dynamic shapes are considered in this chapter
 Apply these methods only if [native dynamic shape API](ov_dynamic_shapes.md) doesn't work for you or doesn't give desired performance.

diff --git a/docs/OV_Runtime_UG/supported_plugins/CPU.md b/docs/OV_Runtime_UG/supported_plugins/CPU.md
@@ -1,12 +1,4 @@
 # CPU device {#openvino_docs_OV_UG_supported_plugins_CPU}
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-   openvino_docs_IE_DG_Bfloat16Inference
-
-@endsphinxdirective
 
 ## Introducing the CPU Plugin
 The CPU plugin was developed to achieve high performance of neural networks on CPU, using the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN).

diff --git a/docs/documentation.md b/docs/documentation.md
@@ -30,6 +30,11 @@
    openvino_docs_MO_DG_Getting_Performance_Numbers
    openvino_docs_model_optimization_guide
    openvino_docs_deployment_optimization_guide_dldt_optimization_guide
+   openvino_docs_deployment_optimization_guide_common
+   openvino_docs_deployment_optimization_guide_latency
+   openvino_docs_IE_DG_Model_caching_overview
+   openvino_docs_deployment_optimization_guide_tput
+   openvino_docs_deployment_optimization_guide_hints   
    openvino_docs_tuning_utilities
    openvino_docs_performance_benchmarks
 

diff --git a/docs/optimization_guide/dldt_deployment_optimization_common.md b/docs/optimization_guide/dldt_deployment_optimization_common.md
@@ -1,13 +1,5 @@
 # General Runtime/Deployment Optimizations {#openvino_docs_deployment_optimization_guide_common}
 
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-@endsphinxdirective
-
 ## Inputs Pre-processing with OpenVINO
 
 In many cases, a network expects a pre-processed image, so make sure you do not perform unnecessary steps in your code:

diff --git a/docs/optimization_guide/dldt_deployment_optimization_guide.md b/docs/optimization_guide/dldt_deployment_optimization_guide.md
@@ -1,15 +1,15 @@
-# Introduction to Inference Runtime Optimizations {#openvino_docs_deployment_optimization_guide_dldt_optimization_guide}
+# Runtime Inference Optimizations {#openvino_docs_deployment_optimization_guide_dldt_optimization_guide}
 
 @sphinxdirective
 
 .. toctree::
    :maxdepth: 1
    :hidden:
 
-    openvino_docs_deployment_optimization_guide_common
-    openvino_docs_deployment_optimization_guide_latency
-    openvino_docs_deployment_optimization_guide_tput
-    openvino_docs_deployment_optimization_guide_hints
+   openvino_docs_deployment_optimization_guide_common
+   openvino_docs_deployment_optimization_guide_latency
+   openvino_docs_deployment_optimization_guide_tput
+   openvino_docs_deployment_optimization_guide_hints
 
 @endsphinxdirective
 

diff --git a/docs/optimization_guide/dldt_deployment_optimization_hints.md b/docs/optimization_guide/dldt_deployment_optimization_hints.md
@@ -1,14 +1,5 @@
 # High-level Performance Hints (Presets) {#openvino_docs_deployment_optimization_guide_hints}
 
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-@endsphinxdirective
-
-
 Traditionally, each of the OpenVINO's [supported devices](../OV_Runtime_UG/supported_plugins/Supported_Devices.md) offers a bunch of low-level performance settings. 
 Tweaking this detailed configuration requires deep architecture understanding.
 Also, while the resulting performance may be optimal for the specific combination of the device and the model that is inferred, it is actually neither device/model nor future-proof:

diff --git a/docs/optimization_guide/dldt_deployment_optimization_latency.md b/docs/optimization_guide/dldt_deployment_optimization_latency.md
@@ -1,14 +1,5 @@
 ## Optimizing for the Latency {#openvino_docs_deployment_optimization_guide_latency}
 
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-   openvino_docs_IE_DG_Model_caching_overview
-
-@endsphinxdirective
-
 ## Latency Specifics
 A significant fraction of applications focused on the situations where typically a single model is loaded (and single input is used) at a time.
 This is a regular "consumer" use case and a default (also for the legacy reasons) performance setup for any OpenVINO device.

diff --git a/docs/optimization_guide/dldt_deployment_optimization_tput.md b/docs/optimization_guide/dldt_deployment_optimization_tput.md
@@ -1,13 +1,5 @@
 # General Throughput Considerations  Optimization Guide {#openvino_docs_deployment_optimization_guide_tput}
 
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-@endsphinxdirective
-
 ### General Throughput Considerations
 As described in the section on the [latency-specific considerations](./dldt_deployment_optimization_latency.md) one possible use-case is focused on delivering the every single request at the minimal delay.
 Throughput on the other hand, is about inference scenarios in which potentially large number of inference requests are served simultaneously.

diff --git a/docs/optimization_guide/model_optimization_guide.md b/docs/optimization_guide/model_optimization_guide.md
@@ -8,7 +8,6 @@
 
    pot_README
    docs_nncf_introduction
-   openvino_docs_IE_DG_Int8Inference
 
 @endsphinxdirective
 
@@ -32,4 +31,5 @@ POT is the easiest way to get optimized models, and usually takes several minute
 ![](../img/WHAT_TO_USE.svg)
 
 ## See also
-- [Deployment optimization](./dldt_deployment_optimization_guide.md)
+- [Deployment optimization](./dldt_deployment_optimization_guide.md)
+- [int8 runtime specifics](../OV_Runtime_UG/Int8Inference.md)