Interactive Tutorials (Python)

This collection of Python tutorials are written for running on Jupyter notebooks. The tutorials provide an introduction to the OpenVINO™ toolkit and explain how to use the Python API and tools for optimized deep learning inference. You can run the code one section at a time to see how to integrate your application with OpenVINO libraries.

Notebooks with Binder button and Google Colab button buttons can be run without installing anything. Once you have found the tutorial of your interest, just click the button next to its name and the Jupyter notebook will start it in a new tab of a browser.

Note

Binder and Google Colab are free online services with limited resources. For the best performance and more control, you should run the notebooks locally. Follow the Installation Guide in order to get information on how to run and manage the notebooks on your machine.

More examples along with additional details regarding OpenVINO Notebooks are available in OpenVINO™ Notebooks Github Repository.

The Jupyter notebooks are categorized into following classes:

Below you will find a selection of recommended tutorials that demonstrate inference on a particular model. These tutorials are guaranteed to provide a great experience with inference in OpenVINO:

284-openvoice.png

Voice tone cloning with OpenVoice and OpenVINO.

284-openvoice

GithubBinderColab
283-photo-maker.gif

Text-to-image generation using PhotoMaker and OpenVINO.

283-photo-maker

Github
281-kosmos2-multimodal-large-language-model.png

Kosmos-2: Multimodal Large Language Model and OpenVINO.

281-kosmos2-multimodal-large-language-model

Github
280-depth-anything.gif

Depth estimation with DepthAnything and OpenVINO.

280-depth-anything

GithubBinderColab
notebook_eye.png

Mobile language assistant with MobileVLM and OpenVINO.

279-mobilevlm-language-assistant

Github
278-stable-diffusion-ip-adapter.png

Image Generation with Stable Diffusion and IP-Adapter.

278-stable-diffusion-ip-adapter

Github
notebook_eye.png

LLM Instruction-following pipeline with OpenVINO.

275-llm-question-answering

Github
274-efficient-sam.png

Object segmentations with EfficientSAM and OpenVINO.

274-efficient-sam

Github
notebook_eye.png

LLM-powered chatbot using Stable-Zephyr-3b and OpenVINO.

273-stable-zephyr-3b-chatbot

Github
272-paint-by-example.png

Paint by Example using Stable Diffusion and OpenVINO.

272-paint-by-example

Github

Note

If there are any issues while running the notebooks, refer to the Troubleshooting and FAQ sections in the Installation Guide or start a GitHub discussion.