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📚 OpenVINO™ Notebooks

Apache License Version 2.0 CI CI

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.

📖 What's Inside

Notebooks with a binder logo button can be run without installing anything. Binder is a free online service with limited resources. For the best performance, please follow the Installation Guide and run the notebooks locally.

Getting Started

Brief tutorials that demonstrate how to use OpenVINO's Python API for inference.

Notebook Description Preview
001-hello-world
Binder		 Classify an image with OpenVINO
002-openvino-api
Binder		 Learn the OpenVINO Python API
003-hello-segmentation
Binder		 Semantic segmentation with OpenVINO

Convert & Optimize

Tutorials that explain how to optimize and quantize models with OpenVINO tools.

Notebook Description Preview
101-tensorflow-to-openvino
Binder		 Convert TensorFlow models to OpenVINO IR
102-pytorch-onnx-to-openvino
Binder		 Convert PyTorch models to OpenVINO IR
103-paddle-onnx-to-openvino
Binder		 Convert PaddlePaddle models to OpenVINO IR
104-model-tools
Binder		 Download, convert and benchmark models from Open Model Zoo
105-language-quantize-bert Optimize and quantize a pre-trained BERT model

Model Demos

Demos that demonstrate inference on a particular model.

Notebook Description Preview
201-vision-monodepth
Binder		 Monocular depth estimation with images and video
202-vision-superresolution-image
Binder		 Upscale raw images with a super resolution model →
202-vision-superresolution-video
Binder		 Turn 360p into 1080p video using a super resolution model →
205-vision-background-removal
Binder		 Remove and replace the background in an image using salient object detection
206-vision-paddlegan-anime
Binder		 Turn an image into anime using a GAN →

Model Training

Tutorials that include code to train neural networks.

Notebook Description Preview
301-tensorflow-training-openvino Train a flower classification model from TensorFlow, then convert to OpenVINO IR
301-tensorflow-training-openvino-pot Use Post-training Optimization Tool (POT) to quantize the flowers model

⚙️ 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. Note: Python 3.9 is not supported yet, but it will be soon.

Supported Operating System Python Version (64-bit)
Ubuntu* 18.04 LTS, 64-bit 3.6, 3.7, 3.8
Ubuntu* 20.04 LTS, 64-bit 3.6, 3.7, 3.8
Red Hat* Enterprise Linux* 8, 64-bit 3.6, 3.8
CentOS* 7, 64-bit 3.6, 3.7, 3.8
macOS* 10.15.x versions 3.6, 3.7, 3.8
Windows 10*, 64-bit Pro, Enterprise or Education editions 3.6, 3.7, 3.8
Windows Server* 2016 or higher 3.6, 3.7, 3.8

📝 Installation Guide

OpenVINO Notebooks require Python and Git. For Python 3.8, C++ is also required. Select a guide for your operating system or environment:

Windows 10 Ubuntu macOS Red Hat CentOS Azure ML Docker

Or, if you have already installed Python, Git and C++, please follow the steps below.

Step 1: Create and Activate openvino_env Environment

Linux and macOS Commands:

python3 -m venv openvino_env
source openvino_env/bin/activate

Windows Commands:

python -m venv openvino_env
openvino_env\Scripts\activate

Step 2: Clone the Repository

git clone https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks

Step 3: Install and Launch the Notebooks

Upgrade pip to the latest version. Use pip's legacy dependency resolver to avoid dependency conflicts

python -m pip install --upgrade pip
pip install -r requirements.txt
python -m ipykernel install --user --name openvino_env

💻 Run the Notebooks

To Launch a Single Notebook

If you wish to launch only one notebook, like the Monodepth notebook, run the command below.

jupyter notebook notebooks/201-vision-monodepth/201-vision-monodepth.ipynb

To Launch all Notebooks

jupyter lab notebooks

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, doublecheck that you installed the Jupyter kernel. If necessary, choose the openvinoenv 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, we recommend using Command Prompt (cmd.exe), not PowerShell.

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