Intel® Neural Compressor

An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)

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Intel® Neural Compressor, formerly known as Intel® Low Precision Optimization Tool, is an open-source Python library that runs on Intel CPUs and GPUs, which delivers unified interfaces across multiple deep-learning frameworks for popular network compression technologies such as quantization, pruning, and knowledge distillation. This tool supports automatic accuracy-driven tuning strategies to help the user quickly find out the best quantized model. It also implements different weight-pruning algorithms to generate a pruned model with predefined sparsity goal. It also supports knowledge distillation to distill the knowledge from the teacher model to the student model. Intel® Neural Compressor is a critical AI software component in the Intel® oneAPI AI Analytics Toolkit.

Visit the Intel® Neural Compressor online document website at: https://intel.github.io/neural-compressor.

Installation

Prerequisites

Python version: 3.7, 3.8, 3.9, 3.10

Install on Linux

• Release binary install

  # install stable basic version from pip
  pip install neural-compressor
  # Or install stable full version from pip (including GUI)
  pip install neural-compressor-full

• Nightly binary install

  git clone https://github.com/intel/neural-compressor.git
  cd neural-compressor
  pip install -r requirements.txt
  # install nightly basic version from pip
  pip install -i https://test.pypi.org/simple/ neural-compressor
  # Or install nightly full version from pip (including GUI)
  pip install -i https://test.pypi.org/simple/ neural-compressor-full

More installation methods can be found at Installation Guide. Please check out our FAQ for more details.

Getting Started

Quantization with Python API

# A TensorFlow Example
pip install tensorflow
# Prepare fp32 model
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb

from neural_compressor.config import PostTrainingQuantConfig
from neural_compressor.data.dataloaders.dataloader import DataLoader
from neural_compressor.data import Datasets

dataset = Datasets('tensorflow')['dummy'](shape=(1, 224, 224, 3))
from neural_compressor.quantization import fit
config = PostTrainingQuantConfig()
fit(
  model="./mobilenet_v1_1.0_224_frozen.pb",
  conf=config,
  calib_dataloader=DataLoader(framework='tensorflow', dataset=dataset),
  eval_dataloader=DataLoader(framework='tensorflow', dataset=dataset))

Quantization with JupyterLab Extension

Search for jupyter-lab-neural-compressor in the Extension Manager in JupyterLab and install with one click:

Quantization with GUI

# An ONNX Example
pip install onnx==1.12.0 onnxruntime==1.12.1 onnxruntime-extensions
# Prepare fp32 model
wget https://github.com/onnx/models/raw/main/vision/classification/resnet/model/resnet50-v1-12.onnx
# Start GUI
inc_bench

System Requirements

Validated Hardware Environment

Intel® Neural Compressor supports CPUs based on Intel 64 architecture or compatible processors:

• Intel Xeon Scalable processor (formerly Skylake, Cascade Lake, Cooper Lake, and Icelake)
• Future Intel Xeon Scalable processor (code name Sapphire Rapids)

Intel® Neural Compressor supports GPUs built on Intel's Xe architecture:

• Intel® Data Center GPU Flex Series

Intel® Neural Compressor quantized ONNX models support multiple hardware vendors through ONNX Runtime:

• Intel CPU, AMD/ARM CPU, and NVidia GPU. Please refer to the validated model list.

Validated Software Environment

• OS version: CentOS 8.4, Ubuntu 20.04
• Python version: 3.7, 3.8, 3.9, 3.10

Framework	TensorFlow	Intel TensorFlow	PyTorch	Intel® Extension for PyTorch*	ONNX Runtime	MXNet
Version	2.10.0 2.9.1 2.8.2	2.10.0 2.9.1 2.8.0	1.12.1+cpu 1.11.0+cpu 1.10.0+cpu	1.12.0 1.11.0 1.10.0	1.12.1 1.11.0 1.10.0	1.8.0 1.7.0 1.6.0

Note: Set the environment variable TF_ENABLE_ONEDNN_OPTS=1 to enable oneDNN optimizations if you are using TensorFlow v2.6 to v2.8. oneDNN is the default for TensorFlow v2.9.

Validated Models

Intel® Neural Compressor validated 420+ examples for quantization with a performance speedup geomean of 2.2x and up to 4.2x on VNNI while minimizing accuracy loss. Over 30 pruning and knowledge distillation samples are also available. More details for validated models are available here.

Documentation

Overview
Architecture			Examples		GUI		APIs
Intel oneAPI AI Analytics Toolkit					AI and Analytics Samples
Basic API
Transform		Dataset		Metric		Objective
Deep Dive
Quantization		Pruning(Sparsity)	Knowledge Distillation		Mixed Precision		Orchestration
Benchmarking		Distributed Training			TensorBoard
Distillation for Quantization				Neural Coder
Advanced Topics
Adaptor						Strategy

Selected Publications/Events

• Intel together with Tencent deepens the cooperation to build a cloud foundation for digital and intelligent industry (Dec 2022)
• Running Fast Transformers on CPUs: Intel Approach Achieves Significant Speed Ups and SOTA Performance (Nov 2022)
• Intel Neural Compressor for TF Virtual Appliance packaged by Bitnami (Nov 2022)
• Neural Compressor: an open-source Python library for network compression (Nov 2022)
• Running Fast Transformers on CPUs: Intel Approach Achieves Significant Speed Ups and SOTA Performance (Nov 2022)
• Personalized Stable Diffusion with Few-Shot Fine-Tuning (Nov 2022)
• Fast DistilBERT on CPUs (Oct 2022)
• Meet the Innovation of Intel AI Software: Intel® Extension for TensorFlow* (Oct 2022)
• PyTorch* Inference Acceleration with Intel® Neural Compressor (Oct 2022)

View our full publication list.

Additional Content

• Release Information
• Contribution Guidelines
• Legal Information
• Security Policy
• Intel® Neural Compressor Website

Hiring

We are actively hiring. Send your resume to [email protected] if you are interested in model compression techniques.