Note
Note that the YUM distribution:
- offers both C/C++ and Python APIs
- does not offer support for NPU inference
- is dedicated to Linux users only
- additionally includes code samples
.. tab-set::
.. tab-item:: System Requirements
:sync: system-requirements
| Full requirement listing is available in:
| :doc:`System Requirements Page <../../../about-openvino/release-notes-openvino/system-requirements>`
.. note::
OpenVINO RPM packages are compatible with and can be run on the following operating systems:
- RHEL 8.2 and higher
- Amazon Linux 2022 and 2023
- Rocky Linux 8.7, 8.8 and 9.2-9.3
- Alma Linux 8.7, 8.8 and 9.2-9.4
- Oracle Linux 8.7, 8.8 and 9.2-9.4
- Fedora 29 and higher up to 41
- OpenEuler 20.03, 22.03, 23.03 and 24.03
- Anolis OS 8.6 and 8.8
- CentOS Stream 8 and 9
.. tab-item:: Processor Notes
:sync: processor-notes
| To see if your processor includes the integrated graphics technology and supports iGPU inference, refer to:
| `Product Specifications <https://ark.intel.com/>`__
.. tab-item:: Software
:sync: software
* `CMake 3.13 or higher, 64-bit <https://cmake.org/download/>`_
* GCC 8.2.0
* `Python 3.8 - 3.11, 64-bit <https://www.python.org/downloads/>`_
Create a YUM repository file (
openvino-2024.repo) in the/tmpdirectory as a normal user:tee > /tmp/openvino-2024.repo << EOF [OpenVINO] name=Intel(R) Distribution of OpenVINO 2024 baseurl=https://yum.repos.intel.com/openvino/2024 enabled=1 gpgcheck=1 repo_gpgcheck=1 gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB EOF
Move the new
openvino-2024.repofile to the YUM configuration directory, i.e./etc/yum.repos.d:sudo mv /tmp/openvino-2024.repo /etc/yum.repos.d
Verify that the new repository is set up properly.
yum repolist | grep -i openvinoYou will see the available list of packages.
To list available OpenVINO packages, use the following command:
yum list 'openvino*'.. tab-set::
.. tab-item:: The Latest Version
:sync: latest-version
Run the following command:
.. code-block:: sh
sudo yum install openvino
.. tab-item:: A Specific Version
:sync: specific-version
Run the following command:
.. code-block:: sh
sudo yum install openvino-<VERSION>.<UPDATE>.<PATCH>
For example:
.. code-block:: sh
sudo yum install openvino-2024.3.0
Run the following command:
yum list installed 'openvino*'Note
You can additionally install Python API using one of the alternative methods (:doc:`conda <install-openvino-conda>` or :doc:`pip <install-openvino-pip>`).
Congratulations! You've just Installed OpenVINO! For some use cases you may still need to install additional components. Check the :doc:`list of additional configurations <../configurations>` to see if your case needs any of them.
With the YUM distribution, you can build OpenVINO sample files, as explained in the
:doc:`guide for OpenVINO sample applications <../../../learn-openvino/openvino-samples>`.
For C++ and C, just run the build_samples.sh script:
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. code-block:: sh
/usr/share/openvino/samples/cpp/build_samples.sh
.. tab-item:: C
:sync: c
.. code-block:: sh
/usr/share/openvino/samples/c/build_samples.sh
To uninstall OpenVINO Runtime via YUM, run the following command based on your needs:
.. tab-set::
.. tab-item:: The Latest Version
:sync: latest-version
.. code-block:: sh
sudo yum autoremove openvino
.. tab-item:: A Specific Version
:sync: specific-version
.. code-block:: sh
sudo yum autoremove openvino-<VERSION>.<UPDATE>.<PATCH>
For example:
.. code-block:: sh
sudo yum autoremove openvino-2024.3.0
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:
Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
You can also try the following things:
Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
Important
Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now published on Hugging Face.
Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
Take a glance at the OpenVINO product home page .