144 lines
8.0 KiB
Markdown
144 lines
8.0 KiB
Markdown
# Install OpenVINO™ Runtime on macOS from an Archive File {#openvino_docs_install_guides_installing_openvino_from_archive_macos}
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With the OpenVINO™ 2022.2 release, you can download and use archive files to install OpenVINO Runtime.
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You can also check the [Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes) for more information on updates in this release.
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> **NOTE**: 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 be installed via [pypi.org](https://pypi.org/project/openvino-dev/) only.
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> **NOTE**: The Intel® Distribution of OpenVINO™ toolkit is supported on macOS version 10.15 with Intel® processor-based machines.
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## System Requirements
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@sphinxdirective
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.. tab:: Operating Systems
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macOS 10.15
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.. tab:: Hardware
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Optimized for these processors:
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* 6th to 12th generation Intel® Core™ processors and Intel® Xeon® processors
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* 3rd generation Intel® Xeon® Scalable processor (formerly code named Cooper Lake)
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* Intel® Xeon® Scalable processor (formerly Skylake and Cascade Lake)
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* Intel® Neural Compute Stick 2
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.. note::
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The current version of the Intel® Distribution of OpenVINO™ toolkit for macOS supports inference on Intel CPUs and Intel® Neural Compute Stick 2 devices only.
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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 - 3.9). Install and add to path.
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* Note that OpenVINO is gradually stopping the support for Python 3.6. Python 3.7 - 3.9 are recommended.
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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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@endsphinxdirective
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## Installing OpenVINO Runtime
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### <a name="install-core"></a>Step 1: Install OpenVINO Core Components
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1. Select and download the OpenVINO™ archive files from [Intel® Distribution of OpenVINO™ toolkit for macOS download page](https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-macos). There are typically two files for you to download:
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```sh
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m_openvino_toolkit_<operating system>_<release version>_<package ID>_x86_64.tgz
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m_openvino_toolkit_<operating system>_<release version>_<package ID>_x86_64.tgz.sha256
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```
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where the `.sha256` file is used to verify the success of the download process.
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2. Locate the downloaded files in your system. This document assumes the files are in your `Downloads` directory.
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3. Open a command prompt terminal window, and verify the checksum of the `sha256` file by using the following command:
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```sh
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shasum -c -a 256 <archive name>.tgz.sha256
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```
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If any error message appears, check your network connections, re-download the correct files, and make sure the download process completes successfully.
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4. Extract OpenVINO files from the `.tgz` file:
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```sh
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tar xf <archive name>.tgz -C <destination_dir>
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```
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where the `<destination_dir>` is the directory that you extract OpenVINO files to. You're recommended to set it as `/opt/intel/`.
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The standard OpenVINO `INSTALL_DIR` referenced in this document is `/opt/intel/openvino_<version>`.
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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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### <a name="set-the-environment-variables"></a>Step 2: Configure the Environment
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You must update several environment variables before you can compile and run OpenVINO™ applications. Set environment variables as follows:
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```sh
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source <INSTALL_DIR>/setupvars.sh
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```
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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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The environment variables are set. Continue to the next section if you want to download any additional components.
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### <a name="model-optimizer"></a>Step 3 (Optional): Install Additional Components
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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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### <a name="configure-ncs2"></a>Step 4 (Optional): Configure the Intel® Neural Compute Stick 2
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@sphinxdirective
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If you want to run inference on Intel® Neural Compute Stick 2 use the following instructions to setup the device: :ref:`NCS2 Setup Guide <ncs guide macos>`.
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@endsphinxdirective
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## <a name="get-started"></a>What's Next?
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Now you are ready to try out the toolkit. You can use the following tutorials to write your applications using Python and C++.
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Start with some Python tutorials:
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* [Hello Image Classification](https://docs.openvino.ai/latest/notebooks/001-hello-world-with-output.html)
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* [Convert TensorFlow models with OpenVINO™](https://docs.openvino.ai/latest/notebooks/101-tensorflow-to-openvino-with-output.html)
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* [Convert a PyTorch model and remove the image background](https://docs.openvino.ai/latest/notebooks/205-vision-background-removal-with-output.html)
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To start with C++ samples, see [Build Sample Applications on macOS](../OV_Runtime_UG/Samples_Overview.md#build_samples_macos) first, and then you can try the following samples:
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* [Hello Classification C++ Sample](@ref openvino_inference_engine_samples_hello_classification_README)
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* [Hello Reshape SSD C++ Sample](@ref openvino_inference_engine_samples_hello_reshape_ssd_README)
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* [Image Classification Async C++ Sample](@ref openvino_inference_engine_samples_classification_sample_async_README)
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## <a name="uninstall"></a>Uninstalling the Intel® Distribution of OpenVINO™ Toolkit
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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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@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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<!---
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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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@endsphinxdirective
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