Install Intel® Distribution of OpenVINO™ Toolkit for Linux Using APT Repository

Note

Note that the APT distribution:

• offers both C/C++ and Python APIs

• is dedicated to Linux users only

• additionally includes code samples

System Requirements

Full requirement listing is available in:

System Requirements Page

Processor Notes

To see if your processor includes the integrated graphics technology and supports iGPU inference, refer to:

Product Specifications

Software Requirements

• CMake 3.13 or higher, 64-bit

• GCC 7.5.0 (for Ubuntu 18.04), GCC 9.3.0 (for Ubuntu 20.04) or GCC 11.3.0 (for Ubuntu 22.04)

• Python 3.8 - 3.12, 64-bit

Installing OpenVINO Runtime

Step 1: Set Up the OpenVINO Toolkit APT Repository

1. Install the GPG key for the repository

   1. Download the GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB

      You can also use the following command:

      wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
      

   2. Add this key to the system keyring:

      sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
      

      Note

      You might need to install GnuPG:

      sudo apt-get install gnupg
      

2. Add the repository via the following command:

   Ubuntu 24

   echo "deb https://apt.repos.intel.com/openvino/2024 ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2024.list
   

   Ubuntu 22

   echo "deb https://apt.repos.intel.com/openvino/2024 ubuntu22 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2024.list
   

   Ubuntu 20

   echo "deb https://apt.repos.intel.com/openvino/2024 ubuntu20 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2024.list
   

   Ubuntu 18

   echo "deb https://apt.repos.intel.com/openvino/2024 ubuntu18 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2024.list
   

3. Update the list of packages via the update command:

   sudo apt update
   

4. Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components:

   apt-cache search openvino
   

Step 2: Install OpenVINO Runtime Using the APT Package Manager

1. Install OpenVINO Runtime

The Latest Version

Run the following command:

sudo apt install openvino

A Specific Version

1. Get a list of OpenVINO packages available for installation:

   sudo apt-cache search openvino
   

2. Install a specific version of an OpenVINO package:

   sudo apt install openvino-<VERSION>.<UPDATE>.<PATCH>
   

   For example:

   sudo apt install openvino-2024.4.0
   

Note

You can use --no-install-recommends option to install only required packages. Keep in mind that the build tools must be installed separately if you want to compile the samples.

2. Check for Installed Packages and Versions

Run the following command:

apt list --installed | grep openvino

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 APT 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

C

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

Python samples can run as following:

python3 /usr/share/openvino/samples/python/hello_query_device/hello_query_device.py

Uninstalling OpenVINO Runtime

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

The Latest Version

sudo apt autoremove openvino

A Specific Version

sudo apt autoremove openvino-<VERSION>.<UPDATE>.<PATCH>

For example:

sudo apt autoremove 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:

• Learn more about OpenVINO Workflow.

• To prepare your models for working with OpenVINO, see Model Preparation.

• See pre-trained deep learning models 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 .