Install Intel® Distribution of OpenVINO™ Toolkit from PyPI Repository — OpenVINO™ documentation (original) (raw)

Note

Note that the PyPi distribution:

• offers the Python API only
• is dedicated to users of all major OSes: Windows, Linux, and macOS (all x86_64 / arm64 architectures)
• macOS offers support only for CPU inference

Before installing OpenVINO, see theSystem Requirements page.

Installing OpenVINO Runtime#

Step 1. Set Up Python Virtual Environment#

Use a virtual environment to avoid dependency conflicts. To create a virtual environment, use the following command:

Windows

python -m venv openvino_env

Linux and macOS

python3 -m venv openvino_env

Step 2. Activate Virtual Environment#

Windows

openvino_env\Scripts\activate

Linux and macOS

source openvino_env/bin/activate

Important

The above command must be re-run every time a new command terminal window is opened.

Step 3. Set Up and Update PIP to the Highest Version#

Use the following command:

python -m pip install --upgrade pip

Step 4. Install the Package#

Use the following command to install either the base or GenAI OpenVINO package:

python -m pip install openvino

Step 5. Verify that the Package Is Installed#

Run the command below:

python -c "from openvino import Core; print(Core().available_devices)"

If installation was successful, you will see the list of available devices.

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

Simplified Build and Integration

The package includes CMake configurations, precompiled static libraries, and headers, which can be easily accessed through the Python API. You can use the get_cmake_path() method to retrieve the paths to the CMake configurations and libraries:

from openvino.utils import get_cmake_path cmake_path = get_cmake_path()

For detailed instructions on how to use these configurations in your build setup, check out theCreate a library with extensions section.

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 Python Quick Start Exampleto estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.

Get started with Python#

Visit the Tutorials page for more Jupyter Notebooks to get you started with OpenVINO, such as:

• OpenVINO Python API Tutorial
• Basic image classification program with Hello Image Classification
• Convert a PyTorch model and use it for image background removal

Additional Resources#

• Intel® Distribution of OpenVINO™ toolkit home page