From 563d4f16e6d916db32af31aa33933a234b78552f Mon Sep 17 00:00:00 2001 From: Karol Blaszczak Date: Wed, 29 Jun 2022 08:54:05 +0200 Subject: [PATCH] DOCS-nncf_rephrasing (#11997) --- docs/Documentation/openvino_ecosystem.md | 2 +- docs/optimization_guide/nncf_introduction.md | 29 +++++++++----------- 2 files changed, 14 insertions(+), 17 deletions(-) diff --git a/docs/Documentation/openvino_ecosystem.md b/docs/Documentation/openvino_ecosystem.md index dcafd7bf007..87ea00a71dc 100644 --- a/docs/Documentation/openvino_ecosystem.md +++ b/docs/Documentation/openvino_ecosystem.md @@ -44,7 +44,7 @@ More resources: * [installation Guide on GitHub](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Install-Guide) ### DL Workbench -A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyse, optimize, and build your pre-trained models. You can do all that by visiting [Intel® DevCloud for the Edge](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench on-line. +A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyze, optimize, and build your pre-trained models. You can do all that by visiting [Intel® DevCloud for the Edge](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench on-line. More resources: * [documentation](dl_workbench_overview.md) diff --git a/docs/optimization_guide/nncf_introduction.md b/docs/optimization_guide/nncf_introduction.md index 8cc7591f0f7..90fe32c02cc 100644 --- a/docs/optimization_guide/nncf_introduction.md +++ b/docs/optimization_guide/nncf_introduction.md @@ -1,13 +1,7 @@ # Neural Network Compression Framework {#docs_nncf_introduction} -This document describes the Neural Network Compression Framework (NNCF) which is distributed as a separate tool but is highly aligned with OpenVINO™ in terms of the supported optimization features and models. It is open-sourced and available on [GitHub](https://github.com/openvinotoolkit/nncf). -## Introduction - Neural Network Compression Framework (NNCF) is aimed at optimizing Deep Neural Network (DNN) by applying optimization methods, such as quantization, pruning, etc., to the original framework model. It provides in-training optimization capabilities which means that optimization methods require model fine-tuning or even re-training. The diagram below shows the model optimization workflow using NNCF. - ![](../img/nncf_workflow.png) - - ### Features - - Support optimization of PyTorch and TensorFlow 2.x models. - - Support of various optimization algorithms, applied during a model fine-tuning process to achieve a better performance-accuracy trade-off: +Neural Network Compression Framework (NNCF) is a set of advanced algorithms for optimizing Deep Neural Networks (DNN). +It provides in-training optimization capabilities, which means that fine-tuning or even re-training the original model is necessary, and supports several optimization algorithms: |Compression algorithm|PyTorch|TensorFlow 2.x| | :--- | :---: | :---: | @@ -16,18 +10,21 @@ This document describes the Neural Network Compression Framework (NNCF) which is |[Sparsity](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Sparsity.md) | Supported | Supported | |[Mixed-precision quantization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#mixed_precision_quantization) | Supported | Not supported | |[Binarization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Binarization.md) | Supported | Not supported | - - -- Stacking of optimization methods. For example: 8-bit quaNtization + Filter Pruning. +The model optimization workflow using NNCF: +![](../img/nncf_workflow.png) + +The main NNCF characteristics: +- Support for optimization of PyTorch and TensorFlow 2.x models. +- Stacking of optimization methods, for example: 8-bit quaNtization + Filter Pruning. - Support for [Accuracy-Aware model training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/Usage.md#accuracy-aware-model-training) pipelines via the [Adaptive Compression Level Training](https://github.com/openvinotoolkit/nncf/tree/develop/docs/accuracy_aware_model_training/AdaptiveCompressionLevelTraining.md) and [Early Exit Training](https://github.com/openvinotoolkit/nncf/tree/develop/docs/accuracy_aware_model_training/EarlyExitTrainig.md). -- Automatic, configurable model graph transformation to obtain the compressed model. - > **NOTE**: Limited support for TensorFlow models. Only the models created, using Sequential or Keras Functional API, are supported. -- GPU-accelerated layers for the faster compressed model fine-tuning. +- Automatic and configurable model graph transformation to obtain the compressed model (limited support for TensorFlow models, only the ones created using Sequential or Keras Functional API, are supported). +- GPU-accelerated layers for faster compressed model fine-tuning. - Distributed training support. - Configuration file examples for each supported compression algorithm. -- Exporting PyTorch compressed models to ONNX\* checkpoints and TensorFlow compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO™ toolkit](https://github.com/openvinotoolkit/). -- Git patches for prominent third-party repositories ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines +- Exporting PyTorch compressed models to ONNX checkpoints and TensorFlow compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO™ toolkit](https://github.com/openvinotoolkit/). +- Open source, available on [GitHub](https://github.com/openvinotoolkit/nncf). +- Git patches for prominent third-party repositories ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines. ## Get started ### Installation