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NART

Introduction

What is NART?

NART = NART is not A RunTime

NART is a deep learning inference framework.

NART supports multiple types of deep learning models and multiple back-ends.

License

NART is licensed under the Apache-2.0 license.

Requirements

  • pybind11
  • numpy
  • onnx>=1.4.0
  • sphinx (for doc)
  • sphinxcontrib-contentui (for doc)
  • protobuf>=3.5.1 (python package and compiler)

Model conversion

The below are versions supported by the model conversion module:

  • PyTorch: 0.3, 0.4, 1.0, 1.1, 1.2, 1.3, 1.5, 1.8

Note: for Caffe target, some layers may be invalid for official Caffe, which will be fixed in later releases.

Build

# update git submodules
git submodule update --init --recursive

# install python requirements
pip install -r python/requirements.txt

cmake -B build \
    -DNART_CASE_MODULES='quant;cuda;tensorrt' \ # enable nart case modules in art/modules
    -DENABLE_NART_TOOLS=ON                      # enable nart tools (e.g. promark)

cmake --build build -j16

Install

# set install prefix, change it to virtual env or some other desired path
export _NART_INSTALL_PREFIX=`pwd`/install

# ensure libart.so can be found by nart tools
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$_NART_INSTALL_PREFIX/lib

# install built modules
cmake --install build --prefix $_NART_INSTALL_PREFIX

# install python modules
cd python
python setup.py install --prefix $_NART_INSTALL_PREFIX

Usage

Model inference deployment

Please refer to this example.

NART case runtime

Please refer to this example and nart_promark.