DOCS: Port to master - fix for archive installation docs (#13298)
* Fix * Update installing-openvino-from-archive-linux.md * fix * Python 3.10 fix
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Maciej Smyk
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@ -15,7 +15,7 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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* Ubuntu 20.04 long-term support (LTS), 64-bit
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.. note::
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Since the OpenVINO™ 2022.1 release, CentOS 7.6, 64-bit is not longer supported.
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Since the OpenVINO™ 2022.1 release, CentOS 7.6, 64-bit is no longer supported.
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.. tab:: Hardware
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@ -42,7 +42,7 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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* `CMake 3.13 or higher, 64-bit <https://cmake.org/download/>`_
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* GCC 7.5.0 (for Ubuntu 18.04) or GCC 9.3.0 (for Ubuntu 20.04)
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* `Python 3.6 - 3.10, 64-bit <https://www.python.org/downloads/windows/>`_
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* Note that OpenVINO is gradually stopping the support for Python 3.6. Python 3.7 - 3.10 are recommended.
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* Note that OpenVINO is gradually stopping the support for Python 3.6. You are encouraged to use Python 3.7 - 3.10.
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@endsphinxdirective
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@ -51,7 +51,7 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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@sphinxdirective
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.. important::
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Before you start your journey with installation of the Intel® Distribution of OpenVINO™ toolkit, we encourage you to check the :ref:`code samples <code samples>` in C, C++, Python and :ref:`notebook tutorials <notebook tutorials>`, so you could see all the amazing things that you can achieve with our tool.
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Before you start your journey with the installation of the Intel® Distribution of OpenVINO™ toolkit, we encourage you to check the :ref:`code samples <code samples>` in C, C++, Python and :ref:`notebook tutorials <notebook tutorials>`, so you could see all the amazing things that you can achieve with our tool.
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@endsphinxdirective
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@ -84,11 +84,11 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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If you forgot to set the directory in Step 5, you can then use `sudo mv <extracted_folder> /opt/intel` (for root users or administrators), or `mv <extracted_folder> /home/<USER>/intel/` (for regular users) to set that.
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For simplicity, it is useful to create a symbolink link:
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For simplicity, it is useful to create a symbolic link/symlink:
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```sh
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ln -s /home/<USER>/intel/<extracted_folder> /home/<USER>/intel/openvino_2022
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```
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If such link already exists, remove the previous link with `rm /home/<USER>/intel/openvino_2022`.
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If such a link already exists, remove the previous link with `rm /home/<USER>/intel/openvino_2022`.
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The `/opt/intel/openvino_<version>/` or `/home/<USER>/intel/openvino_<version>/` will be referred as the standard OpenVINO `<INSTALL_DIR>` in this document.
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@ -104,7 +104,7 @@ source <INSTALL_DIR>/setupvars.sh
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If you have more than one OpenVINO™ version on your machine, you can easily switch its version by sourcing `setupvars.sh` of your choice.
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> **NOTE**: You can also run this script every time when you start new terminal session. Open `~/.bashrc` in your favorite editor, and add `source <INSTALL_DIR>/setupvars.sh`. Next time when you open a terminal, you will see `[setupvars.sh] OpenVINO™ environment initialized`. Changing `.bashrc` is not recommended when you have many OpenVINO™ versions on your machine and want to switch among them, as each may require different setup.
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> **NOTE**: You can also run this script every time you start a new terminal session. Open `~/.bashrc` in your favorite editor, and add `source <INSTALL_DIR>/setupvars.sh`. Next time when you open a terminal, you will see `[setupvars.sh] OpenVINO™ environment initialized`. Changing `.bashrc` is not recommended when you have many OpenVINO™ versions on your machine and want to switch among them, as each may require a different setup.
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The environment variables are set. Next, you can download some additional tools.
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@ -112,7 +112,7 @@ The environment variables are set. Next, you can download some additional tools.
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Since the OpenVINO™ 2022.1 release, the following development tools: Model Optimizer, Post-Training Optimization Tool, Model Downloader and other Open Model Zoo tools, Accuracy Checker, and Annotation Converter can only be installed via PyPI. See [Install OpenVINO™ Development Tools](installing-model-dev-tools.md) for detailed steps.
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OpenCV is necessary to run demos from Open Model Zoo (OMZ). Some OpenVINO samples can also extend their capabilities when compiled with OpenCV as a dependency. To install OpenCV for OpenVINO, see the [instructions on Github](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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OpenCV is necessary to run demos from Open Model Zoo (OMZ). Some OpenVINO samples can also extend their capabilities when compiled with OpenCV as a dependency. To install OpenCV for OpenVINO, see the [instructions on GitHub](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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### <a name="optional-steps"></a>Step 4 (Optional): Configure Inference on Non-CPU Devices
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@ -167,7 +167,7 @@ To uninstall the toolkit, follow the steps on the [Uninstalling page](uninstalli
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* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
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* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
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* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
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* IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit`_
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* IoT libraries and code samples in the GitHub repository: `Intel® IoT Developer Kit`_
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.. _Intel® IoT Developer Kit: https://github.com/intel-iot-devkit
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@ -30,8 +30,8 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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.. tab:: Software Requirements
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* `CMake 3.13 or higher <https://cmake.org/download/>`_ (choose "macOS 10.13 or later"). Add `/Applications/CMake.app/Contents/bin` to path (for default install).
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* `Python 3.6 - 3.9 <https://www.python.org/downloads/mac-osx/>`_ (choose 3.6 - 3.10). Install and add to path.
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* Note that OpenVINO is gradually stopping the support for Python 3.6. Python 3.7 - 3.10 are recommended.
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* `Python 3.6 - 3.10 <https://www.python.org/downloads/mac-osx/>`_ (choose 3.6 - 3.10). Install and add to path.
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* Note that OpenVINO is gradually stopping the support for Python 3.6. You are encouraged to use Python 3.7 - 3.10.
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* Apple Xcode Command Line Tools. In the terminal, run `xcode-select --install` from any directory
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* (Optional) Apple Xcode IDE (not required for OpenVINO™, but useful for development)
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@ -65,7 +65,7 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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For simplicity, you can create a symbolic link to the latest installation: `/opt/intel/openvino_2022/`.
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The core components are now installed. Continue to the next section to configure environment.
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The core components are now installed. Continue to the next section to configure the environment.
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### <a name="set-the-environment-variables"></a>Step 2: Configure the Environment
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@ -77,7 +77,7 @@ source <INSTALL_DIR>/setupvars.sh
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If you have more than one OpenVINO™ version on your machine, you can easily switch its version by sourcing `setupvars.sh` of your choice.
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> **NOTE**: You can also run this script every time when you start new terminal session. Open `~/.bashrc` in your favorite editor, and add `source <INSTALL_DIR>/setupvars.sh`. Next time when you open a terminal, you will see `[setupvars.sh] OpenVINO™ environment initialized`. Changing `.bashrc` is not recommended when you have many OpenVINO™ versions on your machine and want to switch among them, as each may require different setup.
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> **NOTE**: You can also run this script whenever you start a new terminal session. Open `~/.bashrc` in your favorite editor, and add `source <INSTALL_DIR>/setupvars.sh`. Next time when you open a terminal, you will see `[setupvars.sh] OpenVINO™ environment initialized`. Changing `.bashrc` is not recommended when you have many OpenVINO™ versions on your machine and want to switch among them, as each may require a different setup.
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The environment variables are set. Continue to the next section if you want to download any additional components.
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@ -85,7 +85,7 @@ The environment variables are set. Continue to the next section if you want to d
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Since the OpenVINO™ 2022.1 release, the following development tools: Model Optimizer, Post-Training Optimization Tool, Model Downloader and other Open Model Zoo tools, Accuracy Checker, and Annotation Converter are not part of the installer. The OpenVINO™ Development Tools can only be installed via PyPI now. See [Install OpenVINO™ Development Tools](installing-model-dev-tools.md) for detailed steps.
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OpenCV is necessary to run demos from Open Model Zoo (OMZ). Some OpenVINO samples can also extend their capabilities when compiled with OpenCV as a dependency. To install OpenCV for OpenVINO, see the [instructions on Github](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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OpenCV is necessary to run demos from Open Model Zoo (OMZ). Some OpenVINO samples can also extend their capabilities when compiled with OpenCV as a dependency. To install OpenCV for OpenVINO, see the [instructions on GitHub](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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### <a name="configure-ncs2"></a>Step 4 (Optional): Configure the Intel® Neural Compute Stick 2
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@ -113,31 +113,17 @@ To start with C++ samples, see <a href="openvino_docs_OV_UG_Samples_Overview.htm
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To uninstall the toolkit, follow the steps on the [Uninstalling page](uninstalling-openvino.md).
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@sphinxdirective
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.. raw:: html
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</div>
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@endsphinxdirective
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## Additional Resources
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@sphinxdirective
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.. dropdown:: Additional Resources
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* Converting models for use with OpenVINO™: :ref:`Model Optimizer Developer Guide <deep learning model optimizer>`
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* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
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* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
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* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
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* IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit`_
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* :ref:`Troubleshooting Guide for OpenVINO Installation & Configuration <troubleshooting guide for install>`
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* Converting models for use with OpenVINO™: :ref:`Model Optimizer User Guide <deep learning model optimizer>`
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* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
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* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
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* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
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* IoT libraries and code samples in the GitHub repository: `Intel® IoT Developer Kit`_
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<!---
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To learn more about converting models from specific frameworks, go to:
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* :ref:`Convert Your Caffe Model <convert model caffe>`
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* :ref:`Convert Your TensorFlow Model <convert model tf>`
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* :ref:`Convert Your Apache MXNet Model <convert model mxnet>`
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* :ref:`Convert Your Kaldi Model <convert model kaldi>`
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* :ref:`Convert Your ONNX Model <convert model onnx>`
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--->
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.. _Intel® IoT Developer Kit: https://github.com/intel-iot-devkit
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.. _Intel® IoT Developer Kit: https://github.com/intel-iot-devkit
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@endsphinxdirective
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@ -39,10 +39,10 @@ You can also check the [Release Notes](https://software.intel.com/en-us/articles
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* `Microsoft Visual Studio 2019 with MSBuild <http://visualstudio.microsoft.com/downloads/>`_
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* `CMake 3.14 or higher, 64-bit <https://cmake.org/download/>`_ (optional, only required for building sample applications)
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* For Python developers: `Python 3.6 - 3.10, 64-bit <https://www.python.org/downloads/windows/>`_
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* Note that OpenVINO is gradually stopping the support for Python 3.6. Python 3.7 - 3.10 are recommended.
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* Note that OpenVINO is gradually stopping the support for Python 3.6. You are encouraged to use Python 3.7 - 3.10.
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.. note::
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You can choose to download Community version. Use `Microsoft Visual Studio installation guide <https://docs.microsoft.com/en-us/visualstudio/install/install-visual-studio?view=vs-2019>`_ to walk you through the installation. During installation in the **Workloads** tab, choose **Desktop development with C++**.
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You can choose to download the Community version. Use `Microsoft Visual Studio installation guide <https://docs.microsoft.com/en-us/visualstudio/install/install-visual-studio?view=vs-2019>`_ to walk you through the installation. During installation in the **Workloads** tab, choose **Desktop development with C++**.
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.. note::
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You can either use `cmake<version>.msi` which is the installation wizard or `cmake<version>.zip` where you have to go into the `bin` folder and then manually add the path to environmental variables.
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@ -104,7 +104,7 @@ The environment variables are set. Next, you can download some additional tools.
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Since the OpenVINO™ 2022.1 release, the following development tools: Model Optimizer, Post-Training Optimization Tool, Model Downloader and other Open Model Zoo tools, Accuracy Checker, and Annotation Converter are not part of the installer. The OpenVINO™ Development Tools can only be installed via PyPI now. See [Install OpenVINO™ Development Tools](installing-model-dev-tools.md) for detailed steps.
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OpenCV is necessary to run demos from Open Model Zoo (OMZ). Some OpenVINO samples can also extend their capabilities when compiled with OpenCV as a dependency. To install OpenCV for OpenVINO, see the [instructions on Github](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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OpenCV is necessary to run demos from Open Model Zoo (OMZ). Some OpenVINO samples can also extend their capabilities when compiled with OpenCV as a dependency. To install OpenCV for OpenVINO, see the [instructions on GitHub](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
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### <a name="optional-steps"></a>Step 4 (Optional): Configure Inference on non-CPU Devices
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@ -145,24 +145,17 @@ To start with C++ samples, see <a href="openvino_docs_OV_UG_Samples_Overview.htm
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To uninstall the toolkit, follow the steps on the [Uninstalling page](uninstalling-openvino.md).
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@sphinxdirective
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## Additional Resources
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.. dropdown:: Additional Resources
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@sphinxdirective
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* Converting models for use with OpenVINO™: :ref:`Model Optimizer Developer Guide <deep learning model optimizer>`
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* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
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* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
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* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
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* IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit`_
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<!---
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To learn more about converting models from specific frameworks, go to:
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* :ref:`Convert Your Caffe Model <convert model caffe>`
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* :ref:`Convert Your TensorFlow Model <convert model tf>`
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* :ref:`Convert Your Apache MXNet Model <convert model mxnet>`
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* :ref:`Convert Your Kaldi Model <convert model kaldi>`
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* :ref:`Convert Your ONNX Model <convert model onnx>`
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--->
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.. _Intel® IoT Developer Kit: https://github.com/intel-iot-devkit
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* :ref:`Troubleshooting Guide for OpenVINO Installation & Configuration <troubleshooting guide for install>`
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* Converting models for use with OpenVINO™: :ref:`Model Optimizer User Guide <deep learning model optimizer>`
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* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
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* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
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* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
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* IoT libraries and code samples in the GitHub repository: `Intel® IoT Developer Kit`_
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.. _Intel® IoT Developer Kit: https://github.com/intel-iot-devkit
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@endsphinxdirective
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