Installation
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1.1. Prerequisites
1.2. Install from Binary
1.3. Install from Source
1.4. Install from AI Kit
Installation
Prerequisites
You can install Neural Compressor using one of three options: Install single component from binary or source, or get the Intel-optimized framework together with the library by installing the Intel® oneAPI AI Analytics Toolkit.
The following prerequisites and requirements must be satisfied for a successful installation:
Python version: 3.8 or 3.9 or 3.10 or 3.11
Notes:
If you get some build issues, please check frequently asked questions at first.
Install Framework
Install torch for CPU
pip install torch --index-url https://download.pytorch.org/whl/cpu
Use Docker Image with torch installed for HPU
https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#bare-metal-fresh-os-single-click
Install torch/intel_extension_for_pytorch for Intel GPU
https://intel.github.io/intel-extension-for-pytorch/index.html#installation
Install torch for other platform
https://pytorch.org/get-started/locally
Install tensorflow
pip install tensorflow
Install from Binary
Install from Pypi
# Install 2.X API + Framework extension API + PyTorch dependency
pip install neural-compressor[pt]
# Install 2.X API + Framework extension API + TensorFlow dependency
pip install neural-compressor[tf]
# Install 2.X API + Framework extension API
# With this install CMD, some dependencies for framework extension API not installed,
# you can install them separately by `pip install -r requirements_pt.txt` or `pip install -r requirements_tf.txt`.
pip install neural-compressor
# Framework extension API + PyTorch dependency
pip install neural-compressor-pt
# Framework extension API + TensorFlow dependency
pip install neural-compressor-tf
Install from Source
git clone https://github.com/intel/neural-compressor.git
cd neural-compressor
pip install -r requirements.txt
python setup.py install
[optional] pip install -r requirements_pt.txt # for PyTorch framework extension API
[optional] pip install -r requirements_tf.txt # for TensorFlow framework extension API
Install from AI Kit
The Intel® Neural Compressor library is released as part of the Intel® oneAPI AI Analytics Toolkit (AI Kit). The AI Kit provides a consolidated package of Intel’s latest deep learning and machine optimizations all in one place for ease of development. Along with Neural Compressor, the AI Kit includes Intel-optimized versions of deep learning frameworks (such as TensorFlow and PyTorch) and high-performing Python libraries to streamline end-to-end data science and AI workflows on Intel architectures.
The AI Kit is distributed through many common channels, including from Intel’s website, YUM, APT, Anaconda, and more. Select and download the AI Kit distribution package that’s best suited for you and follow the Get Started Guide for post-installation instructions.
Download | Guide |
---|---|
Download AI Kit | AI Kit Get Started Guide |
System Requirements
Validated Hardware Environment
Intel® Neural Compressor supports HPUs based on heterogeneous architecture with two compute engines (MME and TPC):
Intel Gaudi Al Accelerators (Gaudi2)
Intel® Neural Compressor supports CPUs based on Intel 64 architecture or compatible processors:
Intel Xeon Scalable processor (Skylake, Cascade Lake, Cooper Lake, Ice Lake, and Sapphire Rapids)
Intel Xeon CPU Max Series (Sapphire Rapids HBM)
Intel Core Ultra Processors (Meteor Lake)
Intel® Neural Compressor supports GPUs built on Intel’s Xe architecture:
Intel Data Center GPU Flex Series (Arctic Sound-M)
Intel Data Center GPU Max Series (Ponte Vecchio)
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 22.04, MacOS Ventura 13.5, Windows 11
Python version: 3.8, 3.9, 3.10, 3.11
Framework | TensorFlow | Intel® Extension for TensorFlow* |
PyTorch | Intel® Extension for PyTorch* |
ONNX Runtime |
---|---|---|---|---|---|
Version |
2.16.1 2.15.0 2.14.1 |
2.15.0.0 2.14.0.1 2.13.0.0 |
2.3.0 2.2.2 2.1.1 |
2.3.0 2.2.0 2.1.100 |
1.18.0 1.17.3 1.16.3 |