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docs/Documentation/openvino_ecosystem.md
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@ -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)

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docs/optimization_guide/nncf_introduction.md
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# 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