Support and compatibility with official openSUSE repository (#24208)
@ilya-lavrenov Thank you very much for your patience and support. It took a while to complete the official publication in the science repository of the openSUSE Tumbleweed distribution. The result is gratifying, as it was a lot of work to make Intel and openSUSE technical policies compatible (it took 3 months of work). Evidence: Upcoming tasks - Add 15.4. 15.5 and 15.6 (This will be faster) - Add to default repository in progress. https://build.opensuse.org/request/show/1168442 https://software.opensuse.org/package/openvino https://software.opensuse.org/download/package?package=openvino&project=science --------- Co-authored-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>
This commit is contained in:
Alessandro de Oliveira Faria (A.K.A.CABELO)
GitHub
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@ -48,13 +48,13 @@ Install OpenVINO™ 2024.0
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.. dropdown:: Distribution Comparison for OpenVINO 2024.0
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=============== ========== ====== ========= ======== ============ ========== ========== ==========
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Device Archives PyPI APT/YUM Conda Homebrew vcpkg Conan npm
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=============== ========== ====== ========= ======== ============ ========== ========== ==========
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CPU V V V V V V V V
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GPU V V V V V V V V
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NPU V\* V\* V\* n/a n/a n/a n/a V\*
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=============== ========== ====== ========= ======== ============ ========== ========== ==========
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=============== ========== ====== =============== ======== ============ ========== ========== ==========
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Device Archives PyPI APT/YUM/ZYPPER Conda Homebrew vcpkg Conan npm
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=============== ========== ====== =============== ======== ============ ========== ========== ==========
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CPU V V V V V V V V
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GPU V V V V V V V V
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NPU V\* V\* V\ * n/a n/a n/a n/a V\*
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=============== ========== ====== =============== ======== ============ ========== ========== ==========
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| \* **Of the Linux systems, only Ubuntu 22.04 includes drivers for NPU device.**
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| **For Windows, CPU inference on ARM64 is not supported.**
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@ -18,6 +18,7 @@ Install OpenVINO™ Runtime on Linux
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Use PyPI <install-openvino-pip>
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Use APT <install-openvino-apt>
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Use YUM <install-openvino-yum>
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Use ZYPPER <install-openvino-zypper>
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Use Conda Forge <install-openvino-conda>
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Use vcpkg <install-openvino-vcpkg>
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Use Homebrew <install-openvino-brew>
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@ -0,0 +1,187 @@
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.. {#openvino_docs_install_guides_installing_openvino_zypper}
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Install OpenVINO™ Runtime on Linux From ZYPPER Repository
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=========================================================
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.. meta::
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:description: Learn how to install OpenVINO™ Runtime on Linux operating
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system, using the ZYPPER repository.
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.. note::
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Note that the ZYPPER distribution:
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* offers both C/C++ APIs
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* does not offer support for NPU inference
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* is dedicated to Linux users only
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* additionally includes code samples
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.. tab-set::
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.. tab-item:: System Requirements
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:sync: system-requirements
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| Full requirement listing is available in:
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| :doc:`System Requirements Page <../../../about-openvino/release-notes-openvino/system-requirements>`
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.. note::
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OpenVINO RPM packages are compatible with and can be run on the following operating systems:
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- openSUSE Tumbleweed
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.. tab-item:: Processor Notes
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:sync: processor-notes
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| To see if your processor includes the integrated graphics technology and supports iGPU inference, refer to:
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| `Product Specifications <https://ark.intel.com/>`__
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.. tab-item:: Software
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:sync: software
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* `CMake 3.13 or higher, 64-bit <https://cmake.org/download/>`_
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* GCC 8.2.0
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* `Python 3.8 - 3.11, 64-bit <https://www.python.org/downloads/>`_
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Install OpenVINO Runtime
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########################
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Step 1: Set Up the Repository
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+++++++++++++++++++++++++++++
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1. Create a ZYPPER repository file with the command below:
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.. code-block:: sh
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sudo zypper addrepo https://download.opensuse.org/repositories/science/openSUSE_Tumbleweed/science.repo
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sudo zypper refresh
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2. Verify that the new repository is set up properly.
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.. code-block:: sh
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zypper lr |grep -i science
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You will see the available list of packages.
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To list available OpenVINO packages, use the following command:
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.. code-block:: sh
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zypper se openvino
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Step 2: Install OpenVINO Runtime Using the ZYPPER Package Manager
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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Install OpenVINO Runtime
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-------------------------
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Run the following command:
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.. code-block:: sh
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sudo zypper install openvino-devel openvino-sample
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Check for Installed Packages and Version
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-----------------------------------------
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Run the following command:
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.. code-block:: sh
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zypper se -i openvino
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.. note::
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You can additionally install Python API using one of the alternative methods (:doc:`conda <install-openvino-conda>` or :doc:`pip <install-openvino-pip>`).
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Congratulations! You've just Installed OpenVINO! For some use cases you may still
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need to install additional components. Check the
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:doc:`list of additional configurations <../configurations>`
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to see if your case needs any of them.
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With the ZYPPER distribution, you can build OpenVINO sample files, as explained in the
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:doc:`guide for OpenVINO sample applications <../../../learn-openvino/openvino-samples>`.
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For C++ and C, just run the ``build_samples.sh`` script:
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.. tab-set::
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.. tab-item:: C++
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:sync: cpp
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.. code-block:: sh
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/usr/share/openvino/samples/cpp/build_samples.sh
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.. tab-item:: C
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:sync: c
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.. code-block:: sh
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/usr/share/openvino/samples/c/build_samples.sh
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Uninstalling OpenVINO Runtime
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##############################
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To uninstall OpenVINO Runtime via ZYPPER, run the following command based on your needs:
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.. tab-set::
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.. tab-item:: The Latest Version
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:sync: latest-version
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.. code-block:: sh
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sudo zypper remove *openvino*
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.. tab-item:: A Specific Version
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:sync: specific-version
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.. code-block:: sh
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sudo zypper remove *openvino-<VERSION>.<UPDATE>.<PATCH>*
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For example:
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.. code-block:: sh
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sudo zypper remove *openvino-2024.0.0*
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What's Next?
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#############
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Now that you've installed OpenVINO Runtime, you're ready to run your own machine learning applications!
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Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials:
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* Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>`
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for step-by-step instructions on building and running a basic image classification C++ application.
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.. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
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:width: 400
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* Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
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You can also try the following things:
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* Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
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* To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
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* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
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* Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
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* See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
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* Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
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