OpenVINO Notebooks: What the Tutorial Repository Actually Ships

What OpenVINO Notebooks Is For, and Who It Is Aimed At

OpenVINO Notebooks is a collection of ready-to-run Jupyter notebooks that introduce the OpenVINO toolkit and demonstrate its inference API. The README describes the goal plainly: the notebooks "provide an introduction to OpenVINO basics and teach developers how to leverage our API for optimized deep learning inference." That is a teaching scope, not a library scope. Nothing here is a package you import into an application.

The audience follows from that. If you have a trained model and want to see it converted, quantized or run on Intel hardware without reading the full toolkit documentation first, a notebook gives you a runnable path. If you already know the API, the repository offers little beyond reference examples you could copy once. And if you need a supported, versioned dependency to build against, this is the wrong artifact: the notebooks are the artifact.

How the Repository Is Organized and How a Notebook Runs

The layout is conventional for a tutorial collection. The notebooks/ directory holds the notebooks themselves, notebooks/README.md is the index, and utils/ holds shared helper code that notebooks import. The selector/ directory backs the GitHub Pages application the README links to for browsing the collection, and supplementary_materials/ carries assets the notebooks reference.

Execution is standard Jupyter. A notebook is a sequence of cells; each cell runs against a kernel, and the kernel is the Python environment you registered. The Makefile shows the intended shape: it creates a virtual environment, installs dependencies, and registers an ipykernel named openvino_env. That name matters, because when you open a notebook you must select the matching kernel or the imports fail. Model downloads and conversion happen inside cells at run time, which is why the first execution of a notebook is slower than later ones.

Installing OpenVINO Notebooks and Running a First Notebook

The README states the prerequisites directly: Python and Git. It does not give a single cross-platform command. Instead it points to per-system wiki guides for Windows, Ubuntu, macOS, Red Hat and CentOS, plus Azure ML, Docker and Amazon SageMaker. The supported Python range is 3.10 to 3.13 on 64-bit builds.

The repository's Makefile encodes the same sequence for a local Linux or macOS checkout. Running make venv creates a virtual environment at .venv, and the later targets activate it before installing anything:

make venv

Dependency installation is split across two targets. cache_openvino_packages pre-downloads the openvino and nncf wheels into a local pip cache, then uninstalls them so the cache is reused rather than the packages installed early:

make cache_openvino_packages

install_dependencies then installs .ci/dev-requirements.txt, registers the kernel under the name openvino_env, and prints a frozen dependency list. The kernel registration is the step that makes the environment selectable from Jupyter:

make install_dependencies

Finally, check_install runs check_install.py, a script at the repository root whose job is to confirm the environment is usable. Run it before opening a notebook, because it surfaces a broken install at the command line rather than in the middle of a cell:

make check_install

With the environment registered, start JupyterLab and pick a notebook from notebooks/README.md. Select the openvino_env kernel. The first cell that loads a model will fetch weights, so expect a wait on the first run.

The Release Branch Is the Constraint That Bites First

The README carries a note that the main branch was updated to support the OpenVINO 2026.3.1 release, and it tells existing users to run pip install --upgrade -r requirements.txt inside their openvino_env virtual environment to move to it. That single instruction is the clearest statement of how this repository is versioned: the notebooks track a toolkit release, and upgrading the toolkit means upgrading the notebook environment.

The consequence is that a notebook you ran successfully last quarter may not run unchanged after you upgrade, and a notebook you fix locally is not a fix the project carries unless it lands upstream. The README offers two escape hatches: the 2026.2 branch for the previous release and the 2023.3 branch for the previous Long Term Support version. If your deployment is pinned to an LTS OpenVINO build, checking out the matching branch is the sane move, because the main branch will keep moving with each release. The README does not document a rollback procedure beyond checking out an older branch, and it does not state a support window for any branch.

Where the Notebook Format Stops Being the Right Tool

Notebooks are a poor delivery format for anything you intend to run repeatedly and unattended. Cells carry hidden state: running them out of order, or re-running one after another has already mutated a variable, produces results that do not reproduce from a clean kernel. For a tutorial that is tolerable. For a pipeline it is a defect.

The second limitation is hardware. The toolkit targets Intel hardware, and the Dockerfile in the repository makes the scope visible: it installs intel-opencl, level-zero and intel-level-zero-gpu from Intel's RHEL repository, alongside libsndfile. Those packages exist to give the container access to Intel GPUs. On non-Intel accelerators the notebooks may still execute on CPU, but the acceleration story the toolkit is built around does not apply, and the performance you observe will not represent what the toolkit is for. Treat the notebooks as an Intel-hardware evaluation tool, not a portable benchmark.

A third constraint is resource ceilings. The README notes that notebooks carrying Binder and Colab badges can be run without installing anything, and immediately qualifies it: those services are free with limited resources, and it recommends the local installation for best performance. A notebook that downloads a large model may simply not complete in a free hosted session.

How This Differs From Working Directly With the Toolkit

The obvious alternative is the OpenVINO toolkit's own documentation and Python API, without the notebook layer. The difference is not capability, since the notebooks call the same API. It is the order in which you learn things.

Working from the API directly, you decide the model, the conversion path and the runtime configuration yourself, and you get an application you can test and version. Working from a notebook, someone else has already made those decisions and written them as executable steps, which is faster when your goal is to see whether a model converts and runs at all, and slower when your goal is to ship something. A second alternative is the container image: the Dockerfile builds a Jupyter image on a UBI9 Python 3.11 base with the OpenVINO release label, which suits environments where you cannot install system packages locally. It trades flexibility for a known-good environment, and it assumes you have a container runtime and, for GPU access, the matching host drivers.

Licence, Maintenance and the Cost of Keeping Up

The repository is Apache-2.0, stated in the README badge and the LICENSE file at the root. For the notebooks and the utility code in the repository that is a permissive licence, and it is the same licence family as the toolkit itself. The Dockerfile pulls in third-party components, including packages from Intel's repositories and an EPEL clinfo RPM, so the licence picture for a built image is broader than the repository licence alone. That is a packaging question for whoever distributes the image, not a legal opinion here.

The last push to the repository was on 2026-09-09, which is recent, and the repository is not archived. Neither fact tells you how long any given branch will be maintained. The upgrade cost is real and recurring: the README instructs users to run pip install --upgrade -r requirements.txt to move to the new release, and the Makefile's cache_openvino_packages target exists specifically to make reinstalling openvino and nncf faster across those upgrades. Budget for re-running notebooks after each toolkit release rather than assuming they are frozen.

Editorial conclusion

Adopt OpenVINO Notebooks if you are learning the toolkit or evaluating whether a model converts and runs acceptably on your hardware, and you want a working example rather than an API reference. Do not adopt it as a production codebase: it is a teaching repository whose main branch tracks a specific OpenVINO release, and its dependency set changes with that release. Before you invest time, check the notebooks/README.md index for a notebook close to your task, confirm your Python version sits in the supported 3.10 to 3.13 range, and decide whether the 2026.3.1 branch or the 2023.3 LTS branch matches the OpenVINO version you already run.

Frequently asked questions

Official sources

1. Issues
2. License: Apache-2.0
3. openvinotoolkit/openvino_notebooks on GitHub
4. Project website
5. README