OpenVINO(TM) Development Tools
OpenVINO™ Development Tools
NOTE: OpenVINO™ Development Tools package has been deprecated and will be discontinued with 2024.0 release. To learn more, refer to the OpenVINO Legacy Features and Components page.
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.
Before you start the installation, check the supported operating systems and required Python* versions. The complete list of supported hardware is available in the System Requirements.
C++ libraries are also required for the installation on Windows*. To install that, you can download the Visual Studio Redistributable file (.exe).
NOTE: This package can be installed on other versions of macOS, Linux and Windows, but only the specific versions above are fully validated.
Install the OpenVINO™ Development Tools Package
There are two options to install OpenVINO Development Tools: installation into an existing environment with a deep learning framework used for model training or creation; or installation in a new environment.
Installation into an Existing Environment with the Source Deep Learning Framework
To install OpenVINO Development Tools (see the What's in the Package section of this article) into an existing environment with the source deep learning framework used for model training or creation, run the following command:
pip install openvino-dev
Installation in a New Environment
If you do not have an environment with the source deep learning framework for the input model or you encounter any compatibility issues between OpenVINO and your version of deep learning framework, you may install OpenVINO Development Tools with validated versions of frameworks into a new environment.
Step 1. Set Up Python Virtual Environment
Use a virtual environment to avoid dependency conflicts.
To create a virtual environment, use the following commands:
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:
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
Use the following command:
pip install openvino-dev[extras]
extras is the source deep learning framework for the input model and is one or more of the following values separated with "," :
For example, to install and configure the components for working with TensorFlow 2.x and ONNX models, use the following command:
pip install openvino-dev[tensorflow2,onnx]
NOTE: Model conversion API support for TensorFlow 1.x environment has been deprecated. Use TensorFlow 2.x environment to convert both TensorFlow 1.x and 2.x models.
NOTE: On macOS, you may need to enclose the package name in quotes:
pip install "openvino-dev[extras]".
How to 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):
You will see the help message for
moif 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; print(Core().available_devices)"
If installation was successful, you will see a list of available devices.
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:
|Legacy Model conversion API
|Model conversion API 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*.
|Accuracy Checker and
|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
|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
|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 conversion API. 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 conversion API.
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.
For general troubleshooting steps and issues, see Troubleshooting Guide for OpenVINO Installation. The following sections also provide explanations to several error messages.
Errors with Installing via PIP for Users in China
Users in China might encounter errors while downloading sources via PIP during OpenVINO™ installation. To resolve the issues, try the following solution:
Add the download source using the
-iparameter with the Python
pipcommand. For example:
pip install openvino-dev -i https://mirrors.aliyun.com/pypi/simple/
--trusted-hostparameter if the URL above is
https. You can also run the following command to install openvino-dev with specific frameworks. For example:
pip install openvino-dev[tensorflow2] -i https://mirrors.aliyun.com/pypi/simple/
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
alias pip='noglob pip'
ERROR:root:Could not find the Inference Engine or nGraph Python API.
ImportError: libpython3.8.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 libpython3.8
- Intel® Distribution of OpenVINO™ toolkit
- OpenVINO™ Documentation
- OpenVINO™ Notebooks
- OpenVINO Installation Selector Tool
Copyright © 2018-2023 Intel Corporation
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