openvino-dev 2022.2.0.dev20220829

OpenVINO(TM) Development Tools

OpenVINO™ Development Tools

Intel® Distribution of OpenVINO™ toolkit is an open-source toolkit for optimizing and deploying AI inference. It can be used to develop applications and solutions based on deep learning tasks, such as: emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, etc. It provides high-performance and rich deployment options, from edge to cloud.

OpenVINO™ Development Tools enables you to download models from Open Model Zoo, convert your own models to OpenVINO IR, as well as optimize and tune pre-trained deep learning models. See What's in the Package for more information.

System Requirements

Before you start the installation, check the supported operating systems and required Python* versions. The complete list of supported hardware is available in the Release Notes.

Supported Operating System	Python* Version (64-bit)
Ubuntu* 18.04 long-term support (LTS), 64-bit	3.6, 3.7, 3.8
Ubuntu* 20.04 long-term support (LTS), 64-bit	3.6, 3.7, 3.8, 3.9
Red Hat* Enterprise Linux* 8, 64-bit	3.6, 3.8
macOS* 10.15.x	3.6, 3.7, 3.8, 3.9
Windows 10*, 64-bit	3.6, 3.7, 3.8, 3.9

NOTE: This package can be installed on other versions of macOS, Linux and Windows, but only the specific versions above are fully validated.

NOTE: The current version of the OpenVINO™ Runtime for macOS* supports inference on Intel® CPUs only.

Install the OpenVINO™ Development Tools Package

Step 1. Set Up Python Virtual Environment

Use a virtual environment to avoid dependency conflicts.

To create a virtual environment, use the following commands:

On Windows:

python -m venv openvino_env

On Linux and macOS:

python3 -m venv openvino_env

NOTE: On Linux and macOS, you may need to install pip. For example, on Ubuntu execute the following command to get pip installed: sudo apt install python3-venv python3-pip.

Step 2. Activate Virtual Environment

On Linux and macOS:

source openvino_env/bin/activate

On Windows:

openvino_env\Scripts\activate

Step 3. Set Up and Update PIP to the Highest Version

Run the command below:

python -m pip install --upgrade pip

Step 4. Install the Package

There are two options to install OpenVINO Development Tools:

Installing Default Components

To install the default components in the package (see the What's in the Package section of this article), use the following command:

pip install openvino-dev

Installing Components for Specific Frameworks

To install and configure the components of the package for working with specific frameworks, use the following command:

pip install openvino-dev[extras]

where extras has the following values:

Extras Value	DL Framework
caffe	Caffe*
kaldi	Kaldi*
mxnet	Apache MXNet*
onnx	ONNX*
pytorch	PyTorch*
tensorflow	TensorFlow* 1.x
tensorflow2	TensorFlow* 2.x

For example, to install and configure the components for working with TensorFlow 2.x, Apache MXNet and Caffe, use the following command:

pip install openvino-dev[tensorflow2,mxnet,caffe]

NOTE: Model Optimizer support for TensorFlow 1.x environment has been deprecated. Use TensorFlow 2.x environment to convert both TensorFlow 1.x and 2.x models.

Step 5. Verify that the Package Is Installed

• To verify that the developer package is properly installed, run the command below (this may take a few seconds):

  mo -h
  

  You will see the help message for Model Optimizer if installation finished successfully.

• To verify that OpenVINO Runtime from the runtime package is available, run the command below:

  python -c "from openvino.runtime import Core"
  

  If installation was successful, you will not see any error messages (no console output).

What's in the Package?

NOTE: The openvino-dev package installs OpenVINO™ Runtime as a dependency, which is the engine that runs the deep learning model and includes a set of libraries for an easy inference integration into your applications.

In addition, the openvino-dev package installs the following components by default:

Component	Console Script	Description
Model Optimizer	mo	Model Optimizer imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components.  Supported frameworks include Caffe*, TensorFlow*, MXNet*, PaddlePaddle*, and ONNX*.
Benchmark Tool	benchmark_app	Benchmark Application allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.
Accuracy Checker and Annotation Converter	accuracy_check convert_annotation	Accuracy Checker is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. Annotation Converter is a utility that prepares datasets for evaluation with Accuracy Checker.
Post-Training Optimization Tool	pot	Post-Training Optimization Tool allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.
Model Downloader and other Open Model Zoo tools	omz_downloader omz_converter omz_quantizer omz_info_dumper	Model Downloader is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models: Model Converter is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using Model Optimizer. Model Quantizer is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. Model Information Dumper is a helper utility for dumping information about the models to a stable, machine-readable format.

Troubleshooting

For general troubleshooting steps and issues, see Troubleshooting Guide for OpenVINO Installation. The following sections also provide explanations to several error messages.

zsh: no matches found : openvino-dev[...]

If you use zsh (Z shell) interpreter, that is the default shell for macOS starting with version 10.15 (Catalina), you may encounter the following error while installing openvino-dev package with extras:

pip install openvino-dev[tensorflow2,mxnet,caffe]
zsh: no matches found: openvino-dev[tensorflow2,mxnet,caffe]

By default zsh interprets square brackets as an expression for pattern matching. To resolve this issue, you need to escape the command with quotes:

pip install 'openvino-dev[tensorflow2,mxnet,caffe]'

To avoid such issues you can also disable globbing for PIP commands by defining an alias in ~/.zshrc file:

alias pip='noglob pip'

Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"

On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install Build Tools for Visual Studio* 2019 and repeat package installation.

ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory

To resolve missing external dependency on Ubuntu* 18.04, execute the following command:

sudo apt-get install libpython3.7

Additional Resources

• Intel® Distribution of OpenVINO™ toolkit
• OpenVINO™ Documentation
• OpenVINO™ Notebooks

Copyright © 2018-2022 Intel Corporation

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NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.