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BuildingForLinux
To install software requirements, please follow instructions. This manual is for Ubuntu 20.04, for other OS it may be different.
DLI supports several inference frameworks:
- Intel® Distribution of OpenVINO™ Toolkit.
 - Intel® Optimization for Caffe.
 - Intel® Optimization for TensorFlow.
 - TensorFlow Lite.
 - ONNX Runtime (C++ and Python APIs).
 - MXNet (Python Gluon API).
 - OpenCV DNN (C++ and Python APIs).
 - PyTorch.
 
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Install Python tools (Python 3.8 is already installed by default in Ubuntu 20.04).
sudo apt install python3-pip python3-venv python3-tk
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Create and activate virtual environment.
cd ~/ python3 -m venv dl-benchmark-env source ~/dl-benchmark-env/bin/activate python3 -m pip install --upgrade pip
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Clone repository.
sudo apt install git git clone https://github.com/itlab-vision/dl-benchmark.git
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Install requirements.
pip install -r ~/dl-benchmark/requirements.txt 
If you would like to infer deep models using the Intel® Distribution of OpenVINO™ Toolkit (Python API), please, install openvino-dev package using pip.
pip install openvino-dev[caffe,mxnet,onnx,pytorch,tensorflow]==2022.1.0Note: there is no way to install tensorflow and tensorflow2 packages to the single virtual environment, so to convert tensorflow2 models, please, create another virtual environment and install openvino-dev package with the support of tensorflow2:
pip install openvino-dev[tensorflow2]==2022.1.0If you would like to infer deep models using the Intel® Distribution of OpenVINO™ Toolkit (C++ API), please, install the OpenVINO toolkit from sources or download pre-built package and follow Benchmark C++ tool build instructions to get OpenVINO C++ benchmark app built.
If you prefer Intel® Optimization for Caffe to infer deep neural networks, please, install Miniconda or Anaconda and corresponding package intel-caffe.
conda install -c intel-caffeIf you would like to infer deep models using the Intel® Optimization for TensorFlow, please, install package intel-tensorflow using pip.
pip install intel-tensorflow[TBD]
ONNX Runtime requires to be built from sources along with dedicated benchmark tool. Please refer to build instruction to build binaries.
To infer deep learning models using tensorflow-lite framework please install tensorflow python package.
pip install tensorflowIf you would like to infer deep models using MXNet please install mxnet python package.
pip install mxnet[TBD]
OpenCV DNN CPP requires to be built from sources along with dedicated benchmark tool. Please refer to build instruction to build binaries.
[TBD]