diff --git a/docs/IE_PLUGIN_DG/QuantizedNetworks.md b/docs/IE_PLUGIN_DG/QuantizedNetworks.md index c327c3775fb..bbb5a508550 100644 --- a/docs/IE_PLUGIN_DG/QuantizedNetworks.md +++ b/docs/IE_PLUGIN_DG/QuantizedNetworks.md @@ -35,7 +35,7 @@ Thus we can define: **Note**: During the quantization process the values `input_low`, `input_high`, `output_low`, `output_high` are selected so that to map a floating-point zero exactly to an integer value (zero-point) and vice versa. ## Quantization specifics and restrictions -In general, OpenVINO can represent and execute quantized models from different sources. However, the Post-training Optimization Toolkit (POT) +In general, OpenVINO can represent and execute quantized models from different sources. However, the Post-training Optimization Tool (POT) is considered the default way to get optimized models. Since the POT supports HW-aware quantization it means that specific rules can be implemented in it for the particular HW. However, it is reasonable to have compatibility with general-purpose HW such as CPU and GPU and support their quantization schemes. Below we define these rules as follows: diff --git a/docs/get_started/get_started_dl_workbench.md b/docs/get_started/get_started_dl_workbench.md index 701f23f66d6..edd1ab21cd6 100644 --- a/docs/get_started/get_started_dl_workbench.md +++ b/docs/get_started/get_started_dl_workbench.md @@ -13,7 +13,7 @@ OpenVINO™ toolkit components: with pre-trained models for a range of different tasks * [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) to transform models into the Intermediate Representation (IR) format -* [Post-Training Optimization toolkit](@ref pot_README) to calibrate a model and then execute it in the +* [Post-training Optimization Tool](@ref pot_README) to calibrate a model and then execute it in the INT8 precision * [Accuracy Checker](@ref omz_tools_accuracy_checker) to determine the accuracy of a model * [Benchmark Tool](@ref openvino_inference_engine_samples_benchmark_app_README) to estimate inference performance on supported devices @@ -136,4 +136,4 @@ For detailed instructions to create a new project, visit the links below: * [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) * [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) * [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md) -* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models) \ No newline at end of file +* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models) diff --git a/docs/index.md b/docs/index.md index 15d0c2a3a2b..ca6a1fa6b2e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -29,11 +29,14 @@ If your neural network model contains layers that are not in the list of known l Run the [Accuracy Checker utility](@ref omz_tools_accuracy_checker) either against source topologies or against the output representation to evaluate the accuracy of inference. The Accuracy Checker is also part of the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), an integrated web-based performance analysis studio. +Use the [Post-training Optimization Tool](@ref pot_README) to accelerate the inference of a deep learning model by quantizing it to INT8. + Useful documents for model optimization: * [Model Optimizer Developer Guide](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) * [Intermediate Representation and Opsets](MO_DG/IR_and_opsets.md) * [Custom Layers Guide](HOWTO/Custom_Layers_Guide.md) * [Accuracy Checker utility](@ref omz_tools_accuracy_checker) +* [Post-training Optimization Tool](@ref pot_README) * [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) * [Model Downloader](@ref omz_tools_downloader) utility * [Intel's Pretrained Models (Open Model Zoo)](@ref omz_models_group_intel) @@ -42,7 +45,7 @@ Useful documents for model optimization: ### Running and Tuning Inference The other core component of OpenVINO™ is the [Inference Engine](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md), which manages the loading and compiling of the optimized neural network model, runs inference operations on input data, and outputs the results. Inference Engine can execute synchronously or asynchronously, and its plugin architecture manages the appropriate compilations for execution on multiple Intel® devices, including both workhorse CPUs and specialized graphics and video processing platforms (see below, Packaging and Deployment). -You can use OpenVINO™ Tuning Utilities with the Inference Engine to trial and test inference on your model. The Benchmark utility uses an input model to run iterative tests for throughput or latency measures, and the [Cross Check Utility](../inference-engine/tools/cross_check_tool/README.md) compares performance of differently configured inferences. The [Post-Training Optimization Tool](@ref pot_README) integrates a suite of quantization- and calibration-based tools to further streamline performance. +You can use OpenVINO™ Tuning Utilities with the Inference Engine to trial and test inference on your model. The Benchmark utility uses an input model to run iterative tests for throughput or latency measures, and the [Cross Check Utility](../inference-engine/tools/cross_check_tool/README.md) compares performance of differently configured inferences. For a full browser-based studio integrating these other key tuning utilities, try the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction). ![](img/OV-diagram-step3.png) @@ -56,7 +59,7 @@ Useful documents for inference tuning: * [Inference Engine API References](./api_references.html) * [Inference Code Samples](IE_DG/Samples_Overview.md) * [Application Demos](@ref omz_demos) -* [Post-Training Optimization Tool Guide](@ref pot_README) +* [Low Precision Optimization Guide] (@ref pot_docs_LowPrecisionOptimizationGuide) * [Deep Learning Workbench Guide](@ref workbench_docs_Workbench_DG_Introduction) * [Intel Media SDK](https://github.com/Intel-Media-SDK/MediaSDK) * [DL Streamer Samples](@ref gst_samples_README) @@ -86,7 +89,7 @@ Intel® Distribution of OpenVINO™ toolkit includes the following components: - [Deep Learning Inference Engine](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md): A unified API to allow high performance inference on many hardware types including Intel® CPU, Intel® Integrated Graphics, Intel® Neural Compute Stick 2, Intel® Vision Accelerator Design with Intel® Movidius™ vision processing unit (VPU). - [Inference Engine Samples](IE_DG/Samples_Overview.md): A set of simple console applications demonstrating how to use the Inference Engine in your applications. - [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction): A web-based graphical environment that allows you to easily use various sophisticated OpenVINO™ toolkit components. -- [Post-Training Optimization tool](@ref pot_README): A tool to calibrate a model and then execute it in the INT8 precision. +- [Post-training Optimization Tool](@ref pot_README): A tool to calibrate a model and then execute it in the INT8 precision. - Additional Tools: A set of tools to work with your models including [Benchmark App](../inference-engine/tools/benchmark_tool/README.md), [Cross Check Tool](../inference-engine/tools/cross_check_tool/README.md), [Compile tool](../inference-engine/tools/compile_tool/README.md). - [Open Model Zoo](@ref omz_models_group_intel) - [Demos](@ref omz_demos): Console applications that provide robust application templates to help you implement specific deep learning scenarios. @@ -100,4 +103,4 @@ Intel® Distribution of OpenVINO™ toolkit includes the following components: - [OpenCV](https://docs.opencv.org/master/) : OpenCV* community version compiled for Intel® hardware - [Intel® Media SDK](https://software.intel.com/en-us/media-sdk) (in Intel® Distribution of OpenVINO™ toolkit for Linux only) -OpenVINO™ Toolkit opensource version is available on [GitHub](https://github.com/openvinotoolkit/openvino). For building the Inference Engine from the source code, see the build instructions. \ No newline at end of file +OpenVINO™ Toolkit opensource version is available on [GitHub](https://github.com/openvinotoolkit/openvino). For building the Inference Engine from the source code, see the build instructions.