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📚 OpenVINO™ Notebooks

A collection of ready-to-run Jupyter notebooks for learning and experimenting with the OpenVINO™ Toolkit. The notebooks provide an introduction to OpenVINO basics and teach developers how to leverage our API for optimized deep learning inference.

🚀 Checkout interactive GitHub pages application for navigation between OpenVINO™ Notebooks content: OpenVINO™ Notebooks at GitHub Pages

List of all notebooks is available in index file.

📝 Installation Guide

OpenVINO Notebooks require Python and Git. To get started, select the guide for your operating system or environment:

Windows	Ubuntu	macOS	Red Hat	CentOS	Azure ML	Docker	Amazon SageMaker

🚀 Getting Started

Explore Jupyter notebooks using this page, select one related to your needs or give them all a try. Good Luck!

NOTE: The main branch of this repository was updated to support the new OpenVINO 2025.3 release. To upgrade to the new release version, please run pip install --upgrade -r requirements.txt in your openvino_env virtual environment. If you need to install for the first time, see the Installation Guide section below. If you wish to use the previous release version of OpenVINO, please checkout the 2025.2 branch. If you wish to use the previous Long Term Support (LTS) version of OpenVINO check out the 2023.3 branch.

If you need help, please start a GitHub Discussion.

If you run into issues, please check the troubleshooting section, FAQs or start a GitHub discussion.

Notebooks with and buttons can be run without installing anything. Binder and Google Colab are free online services with limited resources. For the best performance, please follow the Installation Guide and run the notebooks locally.

⚙️ System Requirements

The notebooks run almost anywhere — your laptop, a cloud VM, or even a Docker container. The table below lists the supported operating systems and Python versions.

Supported Operating System	Python Version (64-bit)
Ubuntu 20.04 LTS, 64-bit	3.9 - 3.12
Ubuntu 22.04 LTS, 64-bit	3.9 - 3.12
Red Hat Enterprise Linux 8, 64-bit	3.9 - 3.12
CentOS 7, 64-bit	3.9 - 3.12
macOS 10.15.x versions or higher	3.9 - 3.12
Windows 10, 64-bit Pro, Enterprise or Education editions	3.9 - 3.12
Windows Server 2016 or higher	3.9 - 3.12

💻 Run the Notebooks

To Launch a Single Notebook

If you wish to launch only one notebook, like the Monodepth notebook, run the command below (from the repository root directory):

jupyter lab notebooks/vision-monodepth/vision-monodepth.ipynb

To Launch all Notebooks

Launch Jupyter Lab with index README.md file opened for easier navigation between notebooks directories and files. Run the following command from the repository root directory:

jupyter lab notebooks/README.md

Alternatively, in your browser select a notebook from the file browser in Jupyter Lab using the left sidebar. Each tutorial is located in a subdirectory within the notebooks directory.

🧹 Cleaning Up

Shut Down Jupyter Kernel

To end your Jupyter session, press Ctrl-c. This will prompt you to Shutdown this Jupyter server (y/[n])? enter y and hit Enter.

Deactivate Virtual Environment

To deactivate your virtualenv, simply run deactivate from the terminal window where you activated openvino_env. This will deactivate your environment.

To reactivate your environment, run source openvino_env/bin/activate on Linux or openvino_env\Scripts\activate on Windows, then type jupyter lab or jupyter notebook to launch the notebooks again.

Delete Virtual Environment (Optional)

To remove your virtual environment, simply delete the openvino_env directory:

On Linux and macOS:
```
rm -rf openvino_env
```

On Windows:
```
rmdir /s openvino_env
```

Remove openvino_env Kernel from Jupyter
```
jupyter kernelspec remove openvino_env
```

⚠️ Troubleshooting

If these tips do not solve your problem, please open a discussion topic or create an issue!

To check some common installation problems, run python check_install.py. This script is located in the openvino_notebooks directory. Please run it after activating the openvino_env virtual environment.
If you get an ImportError, double-check that you installed the Jupyter kernel. If necessary, choose the openvino_env kernel from the Kernel->Change Kernel menu in Jupyter Lab or Jupyter Notebook.
If OpenVINO is installed globally, do not run installation commands in a terminal where setupvars.bat or setupvars.sh are sourced.
For Windows installation, it is recommended to use Command Prompt (cmd.exe), not PowerShell.
If you get ImportError: cannot import name 'collect_telemetry' from 'notebook_utils', make sure that you have the latest version of notebook_utils.py file downloaded in the notebook directory. Try removing outdated notebook_utils.py file and re-run the notebook - new utils file will be downloaded.

📊 Telemetry

When you execute a notebook cell that contains collect_telemetry() function, telemetry data is collected to help us improve your experience. This data only indicates that the cell was executed and does not include any personally identifiable information (PII).

By default, anonymous telemetry data is collected, limited solely to the execution of the notebook. This telemetry does not extend to any Intel software, hardware, websites, or products.

If you prefer to disable telemetry, you can do so at any time by commenting out the specific line responsible for data collection in the notebook:

# collect_telemetry(...)

Also you can disable telemetry collection by setting SCARF_NO_ANALYTICS or DO_NOT_TRACK environment variable to 1:

export SCARF_NO_ANALYTICS=1
# or
export DO_NOT_TRACK=1

Scarf is used for telemetry purposes. Refer to Scarf documentation to understand how the data is collected and processed.

📚 Additional Resources

OpenVINO Blog - a collection of technical articles with OpenVINO best practices, interesting use cases and tutorials.
Awesome OpenVINO - a curated list of OpenVINO based AI projects.
OpenVINO GenAI Samples - collection of OpenVINO GenAI API samples.
Edge AI Reference Kit - pre-built components and code samples designed to accelerate the development and deployment of production-grade AI applications across various industries, such as retail, healthcare, and manufacturing.
Open Model Zoo demos - console applications that provide templates to help implement specific deep learning inference scenarios. These applications show how to preprocess and postprocess data for model inference and organize processing pipelines.
oneAPI-samples repository demonstrates the performance and productivity offered by oneAPI and its toolkits such as oneDNN in a multiarchitecture environment. OpenVINO™ toolkit takes advantage of the discrete GPUs using oneAPI, an open programming model for multi-architecture programming.

🧑‍💻 Contributors

Made with contrib.rocks.

❓ FAQ

Which devices does OpenVINO support?
What is the first CPU generation you support with OpenVINO?
Are there any success stories about deploying real-world solutions with OpenVINO?

* Other names and brands may be claimed as the property of others.

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