Neural-compressor

Intel® Neural Compressor(formerly known as Intel® Low Precision Optimization Tool) v1.7 release is featured by:

Features
* Quantization
* Improved quantization accuracy in SSD-Reset34 and MobileNet v3 on TensorFlow
* Pruning
* Supported magnitude pruning on TensorFlow
* Knowledge distillation
* Supported knowledge distillation on PyTorch
* Multi-node support
* Supported multi-node pruning with distributed dataloader on PyTorch
* Supported multi-node inference for benchmark on PyTorch
* Acceleration library
* Added a domain-specific acceleration library for NLP models

Productivity
* Supported the configuration-free (pure Python) quantization
* Improved ease-of-use user interface for quantization with few clicks

Ecosystem
* Integrated into HuggingFace optimization library (Optimum)
* Upstreamed INC quantized models (RN50, VGG16) to ONNX Model Zoo

Documentation
* Add tutorial and examples for knowledge distillation
* Add tutorial and examples for multi-node training
* Add tutorial and examples for acceleration library

Validated Configurations
* Python 3.6 & 3.7 & 3.8 & 3.9
* Centos 8.3 & Ubuntu 18.04
* TensorFlow 2.6.0
* Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
* PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
* MxNet 1.6.0, 1.7.0, 1.8.0
* ONNX Runtime 1.6.0, 1.7.0, 1.8.0

Distribution:

  | Channel | Links | Install Command
-- | -- | -- | --
Source | Github | https://github.com/intel/neural-compressor.git | $ git clone https://github.com/intel/neural-compressor.git
Binary | Pip | https://pypi.org/project/neural-compressor | $ pip install neural-compressor
Binary | Conda | https://anaconda.org/intel/neural-compressor | $ conda install neural-compressor -c conda-forge -c intel

Contact:
Please feel free to contact lpot.maintainersintel.com, if you get any questions.

Intel® Low Precision Optimization Tool v1.6 release is featured by:

Pruning:
* Support pruning and post-training quantization pipeline on PyTorch
* Support pruning during quantization-aware training on PyTorch

Quantization:
* Support post-training quantization on TensorFlow 2.6.0, PyTorch 1.9.0, IPEX 1.8.0, and MXNet 1.8.0
* Support quantization-aware training on TensorFlow 2.x (Keras API)

User Experience:
* Improve quantization productivity with new UI
* Support quantized model recovery from tuning history

New Models:
* Support ResNet50 on ONNX model zoo

Documentation:
* Add pruned models
* Add quantized MLPerf models

Validated Configurations:

* Python 3.6 & 3.7 & 3.8 & 3.9
* Centos 8.3 & Ubuntu 18.04
* TensorFlow 2.6.0
* Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
* PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
* MxNet 1.6.0, 1.7.0, 1.8.0
* ONNX Runtime 1.6.0, 1.7.0, 1.8.0

Distribution:

  | Channel | Links | Install Command
-- | -- | -- | --
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel

Contact:
Please feel free to contact lpot.maintainersintel.com, if you get any questions.

Intel® Low Precision Optimization Tool v1.5.1 release is featured by:

* Gradient-sensitivity pruning for CNN model
* Static quantization support for ONNX NLP model
* Dynamic seq length support in NLP dataloader
* Enrich quantization statistics

Validated Configurations:
* Python 3.6 & 3.7 & 3.8 & 3.9
* Centos 8.3 & Ubuntu 18.04
* Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0 and 1.15.0 UP1 & UP2 & UP3
* PyTorch 1.5.0+cpu, 1.6.0+cpu, 1.8.0+cpu, ipex
* MxNet 1.6.0, 1.7.0
* ONNX Runtime 1.6.0, 1.7.0, 1.8.0

Distribution:

  | Channel | Links | Install Command
-- | -- | -- | --
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel

Contact:
Please feel free to contact lpot.maintainersintel.com, if you get any questions.

Intel® Low Precision Optimization Tool v1.5 release is featured by:

* Add pattern-lock sparsity algorithm for NLP fine-tuning tasks
- Up to 70% unstructured sparsity and 50% structured sparsity with <2% accuracy loss on 5 Bert finetuning tasks
* Add NLP head pruning algorithm for HuggingFace models
- Performance speedup up to 3.0X within 1.5% accuracy loss on HuggingFace BERT SST-2
* Support model optimization pipeline
* Integrate SigOPT with multi-metrics optimization
- Complementary as basic strategy to speed up the tuning
* Support TensorFlow 2.5, PyTorch 1.8, and ONNX Runtime 1.8

Validated Configurations:
* Python 3.6 & 3.7 & 3.8 & 3.9
* Centos 8.3 & Ubuntu 18.04
* Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0 and 1.15.0 UP1 & UP2 & UP3
* PyTorch 1.5.0+cpu, 1.6.0+cpu, 1.8.0+cpu, ipex
* MxNet 1.6.0, 1.7.0
* ONNX Runtime 1.6.0, 1.7.0, 1.8.0

Distribution:

  | Channel | Links | Install Command
-- | -- | -- | --
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel

Contact:
Please feel free to contact lpot.maintainersintel.com, if you get any questions.

Intel® Low Precision Optimization Tool v1.4.1 release is featured by:

Intel® Low Precision Optimization Tool v1.4 release is featured by:

Quantization
1. PyTorch FX-based quantization support
2. TensorFlow & ONNX RT quantization enhancement

Pruning
1. Pruning/sparsity API refinement
2. Magnitude-based pruning on PyTorch

Model Zoo
1. INT8 key models updated (BERT on TensorFlow, DLRM on PyTorch, etc.)
2. 20+ HuggingFace model quantization

User Experience
1. More comprehensive logging message
2. UI enhancement with FP32 optimization, auto-mixed precision (BF16/FP32), and graph visualization
3. Online document: https://intel.github.io/lpot

Extended Capabilities
1. Model conversion from QAT to Intel Optimized TensorFlow model

Validated Configurations:
* Python 3.6 & 3.7 & 3.8
* Centos 7 & Ubuntu 18.04
* Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
* PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
* MxNet 1.7.0
* ONNX Runtime 1.6.0, 1.7.0

Distribution:

  | Channel | Links | Install Command
-- | -- | -- | --
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel

Contact:
Please feel free to contact lpot.maintainersintel.com, if you get any questions.