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Install OpenVINO™ Runtime on macOS from an Archive File

@sphinxdirective

.. meta:: :description: Learn how to install OpenVINO™ Runtime on macOS operating system, using an archive file.

.. note::

Note that the Archive distribution:

  • offers both C/C++ and Python APIs
  • additionally includes code samples
  • is dedicated to users of all major OSs: Windows, Linux, macOS

.. tab-set::

.. tab-item:: System Requirements :sync: system-requirements

  | Full requirement listing is available in:
  | `System Requirements Page <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html>`__

.. tab-item:: Software Requirements :sync: software-requirements

  * `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).
  * `Python 3.8 - 3.11 <https://www.python.org/downloads/mac-osx/>`__ (choose 3.8 - 3.11). Install and add to path.
  * Apple Xcode Command Line Tools. In the terminal, run ``xcode-select --install`` from any directory
  * (Optional) Apple Xcode IDE (not required for OpenVINO™, but useful for development)

Installing OpenVINO Runtime ###########################

Step 1: Install OpenVINO Core Components ++++++++++++++++++++++++++++++++++++++++

  1. Open a command prompt terminal window.

  2. Create the /opt/intel folder for OpenVINO by using the following command. If the folder already exists, skip this command.

    .. code-block:: sh

    sudo mkdir /opt/intel

    .. note::

    The /opt/intel path is the recommended folder path for installing OpenVINO. You may use a different path if desired.

  3. Browse to the current user's Downloads folder:

    .. code-block:: sh

    cd <user_home>/Downloads

  4. Download the OpenVINO Runtime archive file for macOS <https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/macos/>__, extract the files, rename the extracted folder and move it to the desired path:

    .. tab-set::

    .. tab-item:: x86, 64-bit :sync: x86-64

      .. code-block:: sh

     curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/macos/m_openvino_toolkit_macos_10_15_2023.1.0.12185.47b736f63ed_x86_64.tgz --output openvino_2023.1.0.tgz
     tar -xf openvino_2023.1.0.tgz
     sudo mv m_openvino_toolkit_macos_10_15_2023.1.0.12185.47b736f63ed_x86_64 /opt/intel/openvino_2023.1.0

    .. tab-item:: ARM, 64-bit :sync: arm-64

      .. code-block:: sh

     curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/macos/m_openvino_toolkit_macos_11_0_2023.1.0.12185.47b736f63ed_arm64.tgz --output openvino_2023.1.0.tgz
     tar -xf openvino_2023.1.0.tgz
     sudo mv m_openvino_toolkit_macos_11_0_2023.1.0.12185.47b736f63ed_arm64 /opt/intel/openvino_2023.1.0

  5. (Optional) Install numpy Python Library:

    .. note::

    This step is required only when you decide to use Python API.

    You can use the requirements.txt file from the /opt/intel/openvino_2023.1.0/python folder:

    .. code-block:: sh

    cd /opt/intel/openvino_2023.1.0 python3 -m pip install -r ./python/requirements.txt

  6. For simplicity, it is useful to create a symbolic link as below:

    .. code-block:: sh

    sudo ln -s /opt/intel/openvino_2023.1.0 /opt/intel/openvino_2023

    .. note::

    If you have already installed a previous release of OpenVINO 2023, a symbolic link to the openvino_2023 folder may already exist. Unlink the previous link with sudo unlink openvino_2023, and then re-run the command above.

Congratulations, you have finished the installation! For some use cases you may still need to install additional components. Check the description below, as well as the :doc:list of additional configurations <openvino_docs_install_guides_configurations_header> to see if your case needs any of them.

The /opt/intel/openvino_2023 folder now contains the core components for OpenVINO. If you used a different path in Step 2, for example, /home/<USER>/intel/, OpenVINO is now in /home/<USER>/intel/openvino_2023. The path to the openvino_2023 directory is also referred as <INSTALL_DIR> throughout the OpenVINO documentation.

Step 2: Configure the Environment +++++++++++++++++++++++++++++++++

You must update several environment variables before you can compile and run OpenVINO applications. Open a terminal window and run the setupvars.sh script as shown below to temporarily set your environment variables. If your <INSTALL_DIR> (the folder you used to install OpenVINO) is not the default /opt/intel/openvino_2023, use the correct one instead.

.. code-block:: sh

cd /opt/intel/openvino_2023 source /opt/intel/openvino_2023/setupvars.sh

If you have more than one OpenVINO™ version on your machine, you can easily switch its version by sourcing the setupvars.sh of your choice.

.. note::

The above command must be re-run every time you start a new terminal session. To set up macOS to automatically run the command every time a new terminal is opened, open ~/.zshrc in your favorite editor and add source /opt/intel/openvino_2023/setupvars.sh after the last line. Next time when you open a terminal, you will see [setupvars.sh] OpenVINO™ environment initialized. Changing ~/.zshrc is not recommended when you have multiple OpenVINO versions on your machine and want to switch among them.

What's Next? ####################

Now that you've installed OpenVINO Runtime, you're ready to run your own machine learning applications! Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials.

.. tab-set::

.. tab-item:: Get started with Python :sync: get-started-py

  Try the `Python Quick Start Example <notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.

  .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
     :width: 400

  Visit the :ref:`Tutorials <notebook tutorials>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:

  * `OpenVINO Python API Tutorial <notebooks/002-openvino-api-with-output.html>`__
  * `Basic image classification program with Hello Image Classification <notebooks/001-hello-world-with-output.html>`__
  * `Convert a PyTorch model and use it for image background removal <notebooks/205-vision-background-removal-with-output.html>`__

.. tab-item:: Get started with C++ :sync: get-started-cpp

  Try the `C++ Quick Start Example <openvino_docs_get_started_get_started_demos.html>`_ for step-by-step instructions on building and running a basic image classification C++ application.

  .. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
     :width: 400

  Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:

  * `Basic object detection with the Hello Reshape SSD C++ sample <openvino_inference_engine_samples_hello_reshape_ssd_README.html>`_
  * `Automatic speech recognition C++ sample <openvino_inference_engine_samples_speech_sample_README.html>`_

Uninstalling Intel® Distribution of OpenVINO™ Toolkit #####################################################

If you have installed OpenVINO Runtime from archive files, you can uninstall it by deleting the archive files and the extracted folders. Uninstallation removes all Intel® Distribution of OpenVINO™ Toolkit component files but does not affect user files in the installation directory.

If you have created the symbolic link, remove the link first:

.. code-block:: sh

sudo rm /opt/intel/openvino_2023

To delete the files:

.. code-block:: sh

rm -r <extracted_folder> && rm <path_to_archive>

Additional Resources ####################

  • :ref:Troubleshooting Guide for OpenVINO Installation & Configuration <troubleshooting guide for install>
  • Converting models for use with OpenVINO™: :ref:Model Optimizer User Guide <deep learning model optimizer>
  • Writing your own OpenVINO™ applications: :ref:OpenVINO™ Runtime User Guide <deep learning openvino runtime>
  • Sample applications: :ref:OpenVINO™ Toolkit Samples Overview <code samples>
  • Pre-trained deep learning models: :ref:Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>
  • IoT libraries and code samples in the GitHUB repository: Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>__

@endsphinxdirective