neural-compressor-full 2.1.1
Repository of Intel® Neural Compressor
License: Apache 2.0
Requires Python: >=3.6.0
Keywords: quantization,auto-tuning,post-training static quantization,post-training dynamic quantization,quantization-aware training,tuning strategy

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Intel® Neural Compressor
===========================
<h3> An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)</h3>

[![python](https://img.shields.io/badge/python-3.7%2B-blue)](https://github.com/intel/neural-compressor)
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[Architecture](./docs/source/design.md#architecture)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Workflow](./docs/source/design.md#workflow)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Results](./docs/source/validated_model_list.md)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Examples](./examples/README.md)&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[Documentations](https://intel.github.io/neural-compressor)
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Intel® Neural Compressor aims to provide popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [ONNX Runtime](https://onnxruntime.ai/), and [MXNet](https://mxnet.apache.org/),
as well as Intel extensions such as [Intel Extension for TensorFlow](https://github.com/intel/intel-extension-for-tensorflow) and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
In particular, the tool provides the key features, typical examples, and open collaborations as below:

* Support a wide range of Intel hardware such as [Intel Xeon Scalable processor](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing

* Validate more than 10,000 models such as [Bloom-176B](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), [OPT-6.7B](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), [Stable Diffusion](/examples/pytorch/nlp/huggingface_models/text-to-image/quantization), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx), [BERT-Large](/examples/pytorch/nlp/huggingface_models/text-classification/quantization/ptq_static/fx), and [ResNet50](/examples/pytorch/image_recognition/torchvision_models/quantization/ptq/cpu/fx) from popular model hubs such as [Hugging Face](https://huggingface.co/), [Torch Vision](https://pytorch.org/vision/stable/index.html), and [ONNX Model Zoo](https://github.com/onnx/models#models), by leveraging zero-code optimization solution [Neural Coder](/neural_coder#what-do-we-offer) and automatic [accuracy-driven](/docs/source/design.md#workflow) quantization strategies

* Collaborate with cloud marketplace such as [Google Cloud Platform](https://console.cloud.google.com/marketplace/product/bitnami-launchpad/inc-tensorflow-intel?project=verdant-sensor-286207), [Amazon Web Services](https://aws.amazon.com/marketplace/pp/prodview-yjyh2xmggbmga#pdp-support), and [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/bitnami.inc-tensorflow-intel), software platforms such as [Alibaba Cloud](https://www.intel.com/content/www/us/en/developer/articles/technical/quantize-ai-by-oneapi-analytics-on-alibaba-cloud.html) and [Tencent TACO](https://new.qq.com/rain/a/20221202A00B9S00), and open AI ecosystem such as [Hugging Face](https://huggingface.co/blog/intel), [PyTorch](https://pytorch.org/tutorials/recipes/intel_neural_compressor_for_pytorch.html), [ONNX](https://github.com/onnx/models#models), and [Lightning AI](https://github.com/Lightning-AI/lightning/blob/master/docs/source-pytorch/advanced/post_training_quantization.rst)

## Installation

### Install from pypi
```Shell
pip install neural-compressor
```
> More installation methods can be found at [Installation Guide](./docs/source/installation_guide.md). Please check out our [FAQ](./docs/source/faq.md) for more details.

## Getting Started
### Quantization with Python API    

```shell
# Install Intel Neural Compressor and TensorFlow
pip install neural-compressor 
pip install tensorflow
# Prepare fp32 model
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb
```
```python
from neural_compressor.config import PostTrainingQuantConfig
from neural_compressor.data import DataLoader
from neural_compressor.data import Datasets

dataset = Datasets('tensorflow')['dummy'](shape=(1, 224, 224, 3))
dataloader = DataLoader(framework='tensorflow', dataset=dataset)

from neural_compressor.quantization import fit
q_model = fit(
    model="./mobilenet_v1_1.0_224_frozen.pb",
    conf=PostTrainingQuantConfig(),
    calib_dataloader=dataloader,
    eval_dataloader=dataloader)
```
> More quick samples can be found in [Get Started Page](./docs/source/get_started.md).

## Documentation

<table class="docutils">
  <thead>
  <tr>
    <th colspan="8">Overview</th>
  </tr>
  </thead>
  <tbody>
    <tr>
      <td colspan="2" align="center"><a href="./docs/source/design.md#architecture">Architecture</a></td>
      <td colspan="2" align="center"><a href="./docs/source/design.md#workflow">Workflow</a></td>
      <td colspan="2" align="center"><a href="https://intel.github.io/neural-compressor/latest/docs/source/api-doc/apis.html">APIs</a></td>
      <td colspan="2" align="center"><a href="./docs/source/bench.md">GUI</a></td>
    </tr>
    <tr>
      <td colspan="2" align="center"><a href="examples/README.md#notebook-examples">Notebook</a></td>
      <td colspan="2" align="center"><a href="examples/README.md">Examples</a></td>
      <td colspan="4" align="center"><a href="https://software.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top.html">Intel oneAPI AI Analytics Toolkit</a></td>
    </tr>
  </tbody>
  <thead>
    <tr>
      <th colspan="8">Python-based APIs</th>
    </tr>
  </thead>
  <tbody>
    <tr>
        <td colspan="2" align="center"><a href="./docs/source/quantization.md">Quantization</a></td>
        <td colspan="2" align="center"><a href="./docs/source/mixed_precision.md">Advanced Mixed Precision</a></td>
        <td colspan="2" align="center"><a href="./docs/source/pruning.md">Pruning (Sparsity)</a></td> 
        <td colspan="2" align="center"><a href="./docs/source/distillation.md">Distillation</a></td>
    </tr>
    <tr>
        <td colspan="2" align="center"><a href="./docs/source/orchestration.md">Orchestration</a></td>        
        <td colspan="2" align="center"><a href="./docs/source/benchmark.md">Benchmarking</a></td>
        <td colspan="2" align="center"><a href="./docs/source/distributed.md">Distributed Compression</a></td>
        <td colspan="2" align="center"><a href="./docs/source/export.md">Model Export</a></td>
    </tr>
  </tbody>
  <thead>
    <tr>
      <th colspan="8">Neural Coder (Zero-code Optimization)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
        <td colspan="2" align="center"><a href="./neural_coder/docs/PythonLauncher.md">Launcher</a></td>
        <td colspan="2" align="center"><a href="./neural_coder/extensions/neural_compressor_ext_lab/README.md">JupyterLab Extension</a></td>
        <td colspan="2" align="center"><a href="./neural_coder/extensions/neural_compressor_ext_vscode/README.md">Visual Studio Code Extension</a></td>
        <td colspan="2" align="center"><a href="./neural_coder/docs/SupportMatrix.md">Supported Matrix</a></td>
    </tr>    
  </tbody>
  <thead>
      <tr>
        <th colspan="8">Advanced Topics</th>
      </tr>
  </thead>
  <tbody>
      <tr>
          <td colspan="2" align="center"><a href="./docs/source/adaptor.md">Adaptor</a></td>
          <td colspan="2" align="center"><a href="./docs/source/tuning_strategies.md">Strategy</a></td>
          <td colspan="2" align="center"><a href="./docs/source/distillation_quantization.md">Distillation for Quantization</a></td>
          <td colspan="2" align="center"><a href="./docs/source/smooth_quant.md">SmoothQuant</td>
      </tr>
  </tbody>
</table>

## Selected Publications/Events
* Blog on Medium: [Effective Post-training Quantization for Large Language Models with Enhanced SmoothQuant Approach](https://medium.com/@NeuralCompressor/effective-post-training-quantization-for-large-language-models-with-enhanced-smoothquant-approach-93e9d104fb98) (Apr 2023)
* Blog by Intel: [Intel® Xeon® Processors Are Still the Only CPU With MLPerf Results, Raising the Bar By 5x](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Intel-Xeon-Processors-Are-Still-the-Only-CPU-With-MLPerf-Results/post/1472750) (Apr 2023)
* Post on Social Media: [Adopt with Tencent TACO: Heterogeneous optimization is also key to improving AI computing power](https://mp.weixin.qq.com/s/I-FQqOuW7HTnwXegLGNAtw) (Mar 2023)
* Post on Social Media: [Training and Inference for Stable Diffusion | Intel Business](https://www.youtube.com/watch?v=emCgSTlJaAg) (Jan 2023)
* NeurIPS'2022: [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) (Oct 2022)
* NeurIPS'2022: [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114) (Oct 2022)

> View our [Full Publication List](./docs/source/publication_list.md).

## Additional Content

* [Release Information](./docs/source/releases_info.md)
* [Contribution Guidelines](./docs/source/CONTRIBUTING.md)
* [Legal Information](./docs/source/legal_information.md)
* [Security Policy](SECURITY.md)

## Research Collaborations

Welcome to raise any interesting research ideas on model compression techniques and feel free to reach us (inc.maintainers@intel.com). Look forward to our collaborations on Intel Neural Compressor!