Updated PyPi documentation (#4423)
* Updated documentation * Added documentation for dev package * Added separate documents for PyPI Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
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Mikhail Ryzhov
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# Install Intel® Distribution of OpenVINO™ Toolkit from PyPI Repository {#openvino_docs_install_guides_installing_openvino_pip}
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This guide provides installation steps for the Intel® distribution of OpenVINO™ toolkit distributed through the PyPI repository.
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OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
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## System Requirements
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Intel® Distribution of OpenVINO™ Toolkit provides the following packages available for installation through the PyPI repository:
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* [Python* distribution](https://www.python.org/) 3.6 or 3.7
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* Operating Systems:
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- Ubuntu* 18.04 long-term support (LTS), 64-bit
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- macOS* 10.15.x versions
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- Windows 10*, 64-bit Pro, Enterprise or Education (1607 Anniversary Update, Build 14393 or higher) editions
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- Windows Server* 2016 or higher
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## Install the Runtime Package Using the PyPI Repository
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### Step 1. Set up and update pip to the highest version
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Run the command below:
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```sh
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python3 -m pip install --upgrade pip
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```
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### Step 2. Install the Intel® distribution of OpenVINO™ toolkit
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Run the command below:
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```sh
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pip install openvino
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```
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### Step 3. Add PATH to environment variables
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Run a command for your operating system:
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- Ubuntu 18.04 and macOS:
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```sh
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export LD_LIBRARY_PATH=<library_dir>:${LD_LIBRARY_PATH}
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```
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- Windows* 10:
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```sh
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set PATH=<library_dir>;%PATH%
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```
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To find `library_dir`:
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**Ubuntu, macOS**:
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- Standard user:
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```sh
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echo $(python3 -m site --user-base)/lib
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```
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- Root or sudo user:
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```sh
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/usr/local/lib
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```
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- Virtual environments or custom Python installations (from sources or tarball):
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```sh
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echo $(which python3)/../../lib
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```
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**Windows**:
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- Standard Python:
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```sh
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python -c "import os, sys; print((os.path.dirname(sys.executable))+'\Library\\bin')"
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```
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- Virtual environments or custom Python installations (from sources or tarball):
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```sh
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python -c "import os, sys; print((os.path.dirname(sys.executable))+'\..\Library\\bin')"
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```
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### Step 4. Verify that the package is installed
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Run the command below:
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```sh
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python3 -c "import openvino"
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```
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Now you are ready to develop and run your application.
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* Runtime package with the Inference Engine inside: [https://pypi.org/project/openvino/](https://pypi.org/project/openvino/).
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* Developers package that includes the runtime package as a dependency, Model Optimizer, Accuracy Checker and Post-Training Optimization Tool: [https://pypi.org/project/openvino-dev](https://pypi.org/project/openvino-dev).
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## Additional Resources
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- [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit).
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- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org).
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- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
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- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
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- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
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- [Intel® Distribution of OpenVINO™ toolkit PIP home page](https://pypi.org/project/openvino/)
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- [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
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88
docs/install_guides/pypi-openvino-dev.md
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docs/install_guides/pypi-openvino-dev.md
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# Intel® Distribution of OpenVINO™ Toolkit Developer Package
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> **LEGAL NOTICE**: Your use of this software and any required dependent software (the
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“Software Package”) is subject to the terms and conditions of the [software license agreements](https://software.intel.com/en-us/license/eula-for-intel-software-development-products) for the Software Package, which may also include notices, disclaimers, or
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license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
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## Introduction
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OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
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**The Developer Package Includes the Following Components Installed by Default:**
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| Component | Description |
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|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*. |
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| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker_README.html), [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html) |
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**The Runtime Package Includes the Following Components Installed by Dependency:**
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| Component | Description |
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|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Inference Engine](https://pypi.org/project/openvino) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
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## System Requirements
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* [Python* distribution](https://www.python.org/) 3.6, 3.7, 3.8
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* Supported Operating Systems:
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- Ubuntu* 18.04 long-term support (LTS), 64-bit (python 3.6 or 3.7)
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- Ubuntu* 20.04 long-term support (LTS), 64-bit (python 3.6 or 3.7)
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- macOS* 10.15.x versions
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- Windows 10*, 64-bit Pro, Enterprise or Education (1607 Anniversary Update, Build 14393 or higher) editions
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- Windows Server* 2016 or higher
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> NOTE: This package can be installed on other versions of Linux and Windows OSes, but only the specific versions above are fully validated.
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## Install the Developer Package
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### Step 1. Set Up Python Virtual Environment
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To avoid dependency conflicts, use a virtual environment. Skip this
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step only if you do want to install all dependencies globally.
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Create virtual environment:
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```sh
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python -m pip install --user virtualenv
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python -m venv openvino_env --system-site-packages
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```
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Activate virtual environment:<br>
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On Linux and macOS:
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```sh
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source openvino_env/bin/activate
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```
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On Windows:
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```sh
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openvino_env\Scripts\activate
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```
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### Step 2. Set Up and Update pip to the Highest Version
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Run the command below:
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```sh
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python -m pip install --upgrade pip
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```
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### Step 3. Install the Package
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Run the command below: <br>
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```sh
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pip install openvino-dev
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```
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### Step 4. Verify that the Package is Installed
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Run the command below:
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```sh
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python -c "pot -h"
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```
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You will see the help message for Post-Training Optimization Tool if installation finished successfully.
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## Additional Resources
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- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
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- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
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84
docs/install_guides/pypi-openvino-rt.md
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84
docs/install_guides/pypi-openvino-rt.md
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# Intel® Distribution of OpenVINO™ Toolkit Runtime Package
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> **LEGAL NOTICE**: Your use of this software and any required dependent software (the
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“Software Package”) is subject to the terms and conditions of the [software license agreements](https://software.intel.com/en-us/license/eula-for-intel-software-development-products) for the Software Package, which may also include notices, disclaimers, or
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license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
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## Introduction
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OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
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The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
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- Enables CNN-based deep learning inference on the edge
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- Supports heterogeneous execution across Intel® CPU, Intel® Integrated Graphics, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
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- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
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**The Runtime Package Includes the Following Components Installed by Default:**
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| Component | Description |
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|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Inference Engine](https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_inference_engine_intro.html) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
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## System Requirements
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* [Python* distribution](https://www.python.org/) 3.6, 3.7, 3.8
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* Supported Operating Systems:
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- Ubuntu* 18.04 long-term support (LTS), 64-bit (python 3.6 or 3.7)
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- Ubuntu* 20.04 long-term support (LTS), 64-bit (python 3.6 or 3.7)
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- macOS* 10.15.x version
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- Windows 10*, 64-bit Pro, Enterprise or Education (1607 Anniversary Update, Build 14393 or higher) editions
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- Windows Server* 2016 or higher
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> NOTE: This package can be installed on other versions of Linux and Windows OSes, but only the specific versions above are fully validated
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## Install the Runtime Package
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### Step 1. Set Up Python Virtual Environment
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To avoid dependency conflicts, use a virtual environment. Skip this
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step only if you do want to install all dependencies globally.
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Create virtual environment:
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```sh
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python -m pip install --user virtualenv
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python -m venv openvino_env --system-site-packages
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```
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Activate virtual environment:<br>
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On Linux and macOS:
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```sh
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source openvino_env/bin/activate
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```
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On Windows:
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```sh
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openvino_env\Scripts\activate
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```
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### Step 2. Set Up and Update pip to the Highest Version
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Run the command below:
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```sh
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python -m pip install --upgrade pip
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```
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### Step 3. Install the Package
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Run the command below: <br>
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```sh
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pip install openvino
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```
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### Step 4. Verify that the Package is Installed
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Run the command below:
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```sh
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python -c "from openvino.inference_engine import IECore"
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```
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You will not see any error messages if installation finished successfully.
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## Additional Resources
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- [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit).
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- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org).
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