Install OpenVINO™ Runtime on Linux From ZYPPER Repository#

Note

Note that the ZYPPER distribution:

offers both C/C++ APIs
does not offer support for NPU inference
is dedicated to Linux users only
additionally includes code samples

Before installing OpenVINO, see the System Requirements page.

Install OpenVINO Runtime#

Step 1: Update the repository#

Update the official factory repository to obtain the latest release:
```
sudo zypper refresh
```
To list available OpenVINO packages, use the following command:
```
zypper se openvino
```

Step 2: Install OpenVINO Runtime Using the ZYPPER Package Manager#

Install OpenVINO Runtime#

Run the following command:

sudo zypper install openvino-devel openvino-sample

Check for Installed Packages and Version#

Run the following command:

zypper se -i openvino

Note

You can additionally install Python API using one of the alternative methods (conda or pip).

Congratulations! You’ve just Installed OpenVINO! For some use cases you may still need to install additional components. Check the list of additional configurations to see if your case needs any of them.

With the ZYPPER distribution, you can build OpenVINO sample files, as explained in the guide for OpenVINO sample applications. For C++ and C, just run the build_samples.sh script:

C++

/usr/share/openvino/samples/cpp/build_samples.sh

/usr/share/openvino/samples/c/build_samples.sh

Uninstalling OpenVINO Runtime#

To uninstall OpenVINO Runtime via ZYPPER, run the following command based on your needs:

The Latest Version

sudo zypper remove *openvino*

A Specific Version

sudo zypper remove *openvino-<VERSION>.<UPDATE>.<PATCH>*

For example:

sudo zypper remove *openvino-2024.4.0*

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:

Try the C++ Quick Start Example for step-by-step instructions on building and running a basic image classification C++ application.
Visit the 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
- Object classification sample

You can also try the following things:

Learn more about OpenVINO Workflow.
To prepare your models for working with OpenVINO, see Model Preparation.
See pre-trained deep learning models in our Open 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 Inference with OpenVINO Runtime.
See sample applications in OpenVINO toolkit Samples Overview.
Take a glance at the OpenVINO product home page .