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.
Getting Started
Brief tutorials that demonstrate how to use OpenVINO's Python API for inference.
Notebook | Description | Preview |
---|---|---|
001-hello-world |
Classify an image with OpenVINO | |
002-openvino-api |
Learn the OpenVINO Python API | |
003-hello-segmentation |
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 |
Convert TensorFlow models to OpenVINO IR | |
102-pytorch-onnx-to-openvino |
Convert PyTorch models to OpenVINO IR | |
103-paddle-onnx-to-openvino |
Convert PaddlePaddle models to OpenVINO IR | |
104-model-tools |
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 |
Monocular depth estimation with images and video | |
202-vision-superresolution-image |
Upscale raw images with a super resolution model | → |
202-vision-superresolution-video |
Turn 360p into 1080p video using a super resolution model | → |
205-vision-background-removal |
Remove and replace the background in an image using salient object detection | |
206-vision-paddlegan-anime |
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