Add PyPi publishing file (#2825)

Co-authored-by: Nikita Malinin <[email protected]>

MANIFEST.in

@@ -2,3 +2,4 @@ graft nncf/torch/extensions
 graft nncf/common/hardware/configs
 include LICENSE
 include licensing/third-party-programs.txt
+include docs/PyPiPublishing.md

docs/PyPiPublishing.md

@@ -0,0 +1,142 @@
+[![GitHub Release](https://img.shields.io/github/v/release/openvinotoolkit/nncf?color=green)](https://github.com/openvinotoolkit/nncf/releases)
+[![Website](https://img.shields.io/website?up_color=blue&up_message=docs&url=https%3A%2F%2Fdocs.openvino.ai%2Flatest%2Fopenvino_docs_model_optimization_guide.html)](https://docs.openvino.ai/nncf)
+[![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](https://github.com/openvinotoolkit/nncf?tab=Apache-2.0-1-ov-file#readme)
+[![PyPI Downloads](https://static.pepy.tech/badge/nncf)](https://pypi.org/project/nncf/)
+
+# Neural Network Compression Framework (NNCF)
+
+Neural Network Compression Framework (NNCF) provides a suite of post-training
+and training-time algorithms for optimizing inference of neural networks in
+[OpenVINO™](https://docs.openvino.ai) with a minimal accuracy drop.
+
+NNCF is designed to work with models from [PyTorch](https://pytorch.org/),
+[TensorFlow](https://www.tensorflow.org/), [ONNX](https://onnx.ai/) and
+[OpenVINO™](https://docs.openvino.ai).
+
+The framework is organized as a Python package that can be built and used
+as a standalone tool. Its architecture is unified to make adding different
+compression algorithms easy for both PyTorch and TensorFlow.
+
+NNCF provides samples that demonstrate the usage of compression algorithms
+for different use cases and models. See compression results achievable
+with the NNCF-powered samples on the
+[NNCF Model Zoo page](https://github.com/openvinotoolkit/nncf/blob/develop/docs/ModelZoo.md).
+
+For more information about NNCF, see:
+
+- [NNCF repository](https://github.com/openvinotoolkit/nncf)
+- [User documentation](https://docs.openvino.ai/nncf)
+- [NNCF API documentation](https://openvinotoolkit.github.io/nncf/autoapi/nncf/)
+- [Usage examples](https://github.com/openvinotoolkit/nncf/tree/develop/docs/usage)
+- [Notebook tutorials](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/README.md#model-training)
+
+## Table of contents
+
+- [Key Features](#key-features)
+- [Installation](#installation-guide)
+- [Third-party integration](#third-party-repository-integration)
+- [NNCF Compressed Model Zoo](#nncf-compressed-model-zoo)
+
+## Key Features<a id="key-features"></a>
+
+### Post-Training Compression Algorithms
+
+| Compression algorithm                                                                                                                                       | OpenVINO  | PyTorch   | TensorFlow    | ONNX          |
+| :---------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: |
+| [Post-Training Quantization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Supported     | Supported     |
+| [Weight Compression](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/post_training_compression/weights_compression/Usage.md)                | Supported | Supported | Not supported | Not supported |
+| [Activation Sparsity](https://github.com/openvinotoolkit/nncf/blob/develop/nncf/experimental/torch/sparsify_activations/ActivationSparsity.md)              | Not supported | Experimental |Not supported| Not supported |
+
+### Training-Time Compression Algorithms
+
+| Compression algorithm                                                                                                                                                                         | PyTorch      | TensorFlow    |
+| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------: | :-----------: |
+| [Quantization Aware Training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/quantization_aware_training/Usage.md)                                 | Supported    | Supported     |
+| [Mixed-Precision Quantization](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#mixed-precision-quantization) | Supported    | Not supported |
+| [Sparsity](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Sparsity.md)                                                            | Supported    | Supported     |
+| [Filter pruning](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Pruning.md)                                                       | Supported    | Supported     |
+| [Movement pruning](https://github.com/openvinotoolkit/nncf/blob/develop/nncf/experimental/torch/sparsity/movement/MovementSparsity.md)                                                        | Experimental | Not supported |
+
+- Automatic, configurable model graph transformation to obtain the compressed
+  model.
+  > **NOTE**: Limited support for TensorFlow models. Only models created using
+    Sequential or Keras Functional API are supported.
+- Common interface for compression methods.
+- GPU-accelerated layers for faster compressed model fine-tuning.
+- Distributed training support.
+- Git patch for prominent third-party repository
+  ([huggingface-transformers](https://github.com/huggingface/transformers))
+  demonstrating the process of integrating NNCF into custom training pipelines.
+- Seamless combination of pruning, sparsity, and quantization algorithms. Refer
+  to [optimum-intel](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino)
+  for examples of joint (movement) pruning, quantization, and distillation
+  (JPQD), end-to-end from NNCF optimization to compressed OpenVINO IR.
+- Exporting PyTorch compressed models to ONNX\* checkpoints and TensorFlow
+  compressed models to SavedModel or Frozen Graph format, ready to use with
+  [OpenVINO&trade; toolkit](https://docs.openvino.ai).
+- Support for [Accuracy-Aware model training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Usage.md#accuracy-aware-model-training)
+  pipelines via the [Adaptive Compression Level Training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/accuracy_aware_model_training/AdaptiveCompressionLevelTraining.md)
+  and [Early Exit Training](https://github.com/openvinotoolkit/nncf/blob/develop/docs/accuracy_aware_model_training/EarlyExitTraining.md).
+
+## Installation Guide<a id="installation-guide"></a>
+
+NNCF can be installed as a regular PyPI package:
+
+```bash
+pip install nncf
+```
+
+For detailed installation instructions, refer to the
+[Installation](https://github.com/openvinotoolkit/nncf/blob/develop/docs/Installation.md) guide.
+
+### System Requirements
+
+- Ubuntu 18.04 or later (64-bit)
+- Python 3.8 or later
+- Supported frameworks:
+  - PyTorch >=2.2, <2.4
+  - TensorFlow >=2.8.4, <=2.15.1
+  - ONNX ==1.16.0
+  - OpenVINO >=2022.3.0
+
+## Third-party Repository Integration<a id="third-party-repository-integration"></a>
+
+NNCF may be easily integrated into training/evaluation pipelines of third-party
+repositories.
+
+- [OpenVINO Training Extensions](https://github.com/openvinotoolkit/training_extensions)
+
+  NNCF is integrated into OpenVINO Training Extensions as a model optimization
+  backend. You can train, optimize, and export new models based on available
+  model templates as well as run the exported models with OpenVINO.
+
+- [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum/intel/optimization_ov)
+
+  NNCF is used as a compression backend within the renowned `transformers`
+  repository in HuggingFace Optimum Intel.
+
+## NNCF Compressed Model Zoo<a id="nncf-compressed-model-zoo"></a>
+
+A list of models and compression results for them can be found at our
+[NNCF Model Zoo page](https://github.com/openvinotoolkit/nncf/blob/develop/docs/ModelZoo.md).
+
+## Citing
+
+```bi
+@article{kozlov2020neural,
+    title =   {Neural network compression framework for fast model inference},
+    author =  {Kozlov, Alexander and Lazarevich, Ivan and Shamporov, Vasily and Lyalyushkin, Nikolay and Gorbachev, Yury},
+    journal = {arXiv preprint arXiv:2002.08679},
+    year =    {2020}
+}
+```
+
+## Telemetry
+
+NNCF as part of the OpenVINO™ toolkit collects anonymous usage data for the
+purpose of improving OpenVINO™ tools. You can opt-out at any time by running
+the following command in the Python environment where you have NNCF installed:
+
+`opt_in_out --opt_out`
+
+More information available on [OpenVINO telemetry](https://docs.openvino.ai/nightly/about-openvino/additional-resources/telemetry.html).

setup.py

@@ -129,7 +129,7 @@ def find_version(*file_paths):
     ],
 }
 
-with open("{}/README.md".format(here), "r", encoding="utf8") as fh:
+with open("{}/docs/PyPiPublishing.md".format(here), "r", encoding="utf8") as fh:
     long_description = fh.read()
 
 setup(