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# Installation of OpenVINO™ Notebooks {#notebooks-installation}
@sphinxdirective
.. _notebooks installation:
.. meta::
:description: An installation guide for Jupyter notebooks on which Python
tutorials run. The tutorials serve as introduction to the
OpenVINO™ toolkit.
:keywords: OpenVINO™ toolkit, Jupyter notebooks, Jupyter, Python, Python API,
installation guide, tutorials, install notebooks, local
installation, OpenVINO™ Notebooks, run notebooks
The notebooks run almost anywhere, from browsers and desktops to even a cloud VM or a Docker container.
Follow the guide below in order to run and manage the notebooks on your machine.
--------------------
Contents:
- `Installation Guide <#-installation-guide>`__
- `Run the Notebooks <#-run-the-notebooks>`__
- `Manage the notebooks <#-manage-the-notebooks>`__
- `Troubleshooting <#-troubleshooting>`__
- `FAQ <#-faq>`__
--------------------
.. raw:: html
<a name="-installation-guide">
`Installation Guide`_
=====================
The table below lists the supported operating systems and Python versions.
+-------------------------------------+--------------------------------+
| Supported Operating System (64-bit) | `Python Version |
| | (64-bit |
| | ) <https://www.python.org/>`__ |
+=====================================+================================+
| Ubuntu 18.04 LTS | 3.6, 3.7, 3.8, 3.9 |
+-------------------------------------+--------------------------------+
| Ubuntu 20.04 LTS | 3.6, 3.7, 3.8, 3.9 |
+-------------------------------------+--------------------------------+
| Red Hat Enterprise Linux 8 | 3.6, 3.8, 3.9 |
+-------------------------------------+--------------------------------+
| CentOS 7 | 3.6, 3.7, 3.8, 3.9 |
+-------------------------------------+--------------------------------+
| macOS 10.15.x versions | 3.6, 3.7, 3.8, 3.9 |
+-------------------------------------+--------------------------------+
| Windows 10 Pro, Enterprise | 3.6, 3.7, 3.8, 3.9 |
| or Education editions | |
+-------------------------------------+--------------------------------+
| Windows Server 2016 or higher | 3.6, 3.7, 3.8, 3.9 |
+-------------------------------------+--------------------------------+
OpenVINO Notebooks also require Git. Follow the guide below for your
operating system or environment.
`Installing prerequisites`_
----------------------------
.. tab:: WINDOWS
1. **Install Python**
Download 64 bit version of Python software (3.6, 3.7, 3.8, 3.9) from `python.org`_.
.. _python.org: https://www.python.org/downloads/windows/
Run the installer by double clicking it. Follow the installation steps to set up the software.
While installing, make sure you check the box to *add Python to system PATH*.
.. note::
Python software available in the Microsoft Store is not recommended. It may require additional packages.
2. **Install GIT**
Download 64 bit version of GIT from `git-scm.org`_
.. _git-scm.org: https://github.com/git-for-windows/git/releases/download/v2.36.0.windows.1/Git-2.36.0-64-bit.exe
Run the installer by double clicking it. Follow the installation steps to set up the software.
3. **Install C++ Redistributable (For Python 3.8 only)**
Download 64 bit version of C++ Redistributable from `here`_
.. _here: https://download.visualstudio.microsoft.com/download/pr/4100b84d-1b4d-487d-9f89-1354a7138c8f/5B0CBB977F2F5253B1EBE5C9D30EDBDA35DBD68FB70DE7AF5FAAC6423DB575B5/VC_redist.x64.exe
Run the installer by double clicking it. Follow the installation steps to set up the software.
.. tab:: Linux Systems
1. **Install Python and GIT**
.. note::
Linux Systems may require installation of additional libraries.
The following installation steps should work on Ubuntu Desktop 18.04, 20.04, 20.10, and on Ubuntu Server.
.. code-block::
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python3-venv build-essential python3-dev git-all
The following installation steps should work on a clean install of Red Hat, CentOS, Amazon Linux 2 or Fedora. If any issues occur, see the `Troubleshooting <#-troubleshooting>`__ section.
.. code-block::
sudo yum update
sudo yum upgrade
sudo yum install python36-devel mesa-libGL
.. tab:: macOS
1. **Install Python**
Download Python software (3.7, 3.8, 3.9) from `python.org`. For example, this `installer`_.
.. _installer: https://www.python.org/ftp/python/3.7.9/python-3.7.9-macosx10.9.pkg
Run the installer by double clicking it. Follow the installation steps to set up the software.
.. note::
Refer to the "Important Information" displayed during installation for information about SSL/TLS certificate validation and running the "Install Certificates.command". These certificates are required to run some of the notebooks.
.. tab:: Azure ML
.. note::
An Azure account and access to `Azure ML Studio <https://ml.azure.com/>`__ are required.
1. **Adding a Compute Instance**
In Azure ML Studio, `add a compute instance <https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-manage-compute-instance?tabs=python>`__ and pick any CPU-based instance. At least 4 CPU cores and 8GB of RAM are recommended.
|ml-studio-1|
2. **Start the Terminal**
Once the compute instance has started, open the terminal window and then follow the installation steps below.
|ml-studio-2|
.. tab:: Docker
To run the notebooks inside a Linux-based Docker container, use the Dockerfile:
.. code-block:: bash
:caption: Source: https://github.com/openvinotoolkit/openvino_notebooks/blob/main/Dockerfile
FROM quay.io/thoth-station/s2i-thoth-ubi8-py38:v0.29.0
LABEL name="OpenVINO(TM) Notebooks" \
maintainer="helena.kloosterman@intel.com" \
vendor="Intel Corporation" \
version="0.2.0" \
release="2021.4" \
summary="OpenVINO(TM) Developer Tools and Jupyter Notebooks" \
description="OpenVINO(TM) Notebooks Container"
ENV JUPYTER_ENABLE_LAB="true" \
ENABLE_MICROPIPENV="1" \
UPGRADE_PIP_TO_LATEST="1" \
WEB_CONCURRENCY="1" \
THOTH_ADVISE="0" \
THOTH_ERROR_FALLBACK="1" \
THOTH_DRY_RUN="1" \
THAMOS_DEBUG="0" \
THAMOS_VERBOSE="1" \
THOTH_PROVENANCE_CHECK="0"
USER root
# Upgrade NodeJS > 12.0
# Install dos2unix for line end conversion on Windows
RUN curl -sL https://rpm.nodesource.com/setup_14.x | bash - && \
yum remove -y nodejs && \
yum install -y nodejs mesa-libGL dos2unix libsndfile && \
yum -y update-minimal --security --sec-severity=Important --sec-severity=Critical --sec-severity=Moderate
# Copying in override assemble/run scripts
COPY .docker/.s2i/bin /tmp/scripts
# Copying in source code
COPY .docker /tmp/src
COPY .ci/patch_notebooks.py /tmp/scripts
# Git on Windows may convert line endings. Run dos2unix to enable
# building the image when the scripts have CRLF line endings.
RUN dos2unix /tmp/scripts/*
RUN dos2unix /tmp/src/builder/*
# Change file ownership to the assemble user. Builder image must support chown command.
RUN chown -R 1001:0 /tmp/scripts /tmp/src
USER 1001
RUN mkdir /opt/app-root/notebooks
COPY notebooks/ /opt/app-root/notebooks
RUN /tmp/scripts/assemble
RUN pip check
USER root
RUN dos2unix /opt/app-root/bin/*sh
RUN yum remove -y dos2unix
RUN chown -R 1001:0 .
RUN chown -R 1001:0 /opt/app-root/notebooks
USER 1001
# RUN jupyter lab build
CMD /tmp/scripts/run
`Installing notebooks`_
------------------------
.. tab:: WINDOWS
1. **Create a Virtual Environment**
If you already have installed *openvino-dev*, you may skip this step and proceed with the next one.
.. code-block::
python -m venv openvino_env
2. **Activate the Environment**
.. code-block::
openvino_env\Scripts\activate
3. **Clone the Repository**
Using the --depth=1 option for git clone reduces download size.
.. code-block::
git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks
4. **Upgrade PIP**
.. code-block::
python -m pip install --upgrade pip
5. **Install required packages**
.. code-block::
pip install -r requirements.txt
6. **Install the virtualenv Kernel in Jupyter**
.. code-block::
python -m ipykernel install --user --name openvino_env
.. tab:: Linux Systems
1. **Create a Virtual Environment**
If you already have installed *openvino-dev*, you may skip this step and proceed with the next one.
.. code-block::
python3 -m venv openvino_env
2. **Activate the Environment**
.. code-block::
source openvino_env/bin/activate
3. **Clone the Repository**
Using the --depth=1 option for git clone reduces download size.
.. code-block::
git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks
4. **Upgrade PIP**
.. code-block::
python -m pip install --upgrade pip
5. **Install required packages**
.. code-block::
pip install -r requirements.txt
6. **Install the virtualenv Kernel in Jupyter**
.. code-block::
python -m ipykernel install --user --name openvino_env
.. tab:: macOS
1. **Create a Virtual Environment**
If you already have installed *openvino-dev*, you may skip this step and proceed with the next one.
.. code-block::
python3 -m venv openvino_env
2. **Activate the Environment**
.. code-block::
source openvino_env/bin/activate
3. **Clone the Repository**
Using the --depth=1 option for git clone reduces download size.
.. code-block::
git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks
4. **Upgrade PIP**
.. code-block::
python -m pip install --upgrade pip
5. **Install required packages**
.. code-block::
pip install -r requirements.txt
6. **Install the virtualenv Kernel in Jupyter**
.. code-block::
python -m ipykernel install --user --name openvino_env
.. tab:: Azure ML
1. **Create a Virtual Environment**
If you already have installed *openvino-dev*, you may skip this step and proceed with the next one.
.. code-block::
python3 -m venv openvino_env
2. **Activate the Environment**
.. code-block::
source openvino_env/bin/activate
3. **Clone the Repository**
Using the --depth=1 option for git clone reduces download size.
.. code-block::
git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks
4. **Upgrade PIP**
.. code-block::
python -m pip install --upgrade pip
5. **Install required packages**
.. code-block::
pip install -r requirements.txt
6. **Install the virtualenv Kernel in Jupyter**
.. code-block::
python -m ipykernel install --user --name openvino_env
.. tab:: Docker
1. **Clone the Repository**
.. code-block::
git clone https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks
2. **Build the Docker Image**
.. code-block::
docker build -t openvino_notebooks .
3. **Run the Docker Image**
.. code-block::
docker run -it -p 8888:8888 openvino_notebooks
.. note::
For using model training notebooks, allocate additional memory:
.. code-block::
docker run -it -p 8888:8888 --shm-size 8G openvino_notebooks
4. **Start the browser**
Copy the URL printed in the terminal window and open in a browser. |br|
If it is a remote machine, replace 127.0.0.1 with the correct IP address.
|docker-terminal-1|
The Dockerfile can be used to run a local image on Windows, Linux or macOS.
It is also compatible with Open Data Hub and Red Hat OpenShift Data Science.
The base layer is a `UBI 8 <https://catalog.redhat.com/software/containers/ubi8/5c647760bed8bd28d0e38f9f?container-tabs=overview>`__-based image provided by `Project Thoth <https://thoth-station.ninja/>`__.
.. note::
While running the container on Windows and macOS, only CPU devices can be used. To access the iGPU, install the notebooks locally, following the instructions above.
--------------------
.. raw:: html
<a name="-run-the-notebooks"/>
`Run the Notebooks`_
====================
Launch a Single Notebook
------------------------------
If you want to launch only one notebook, such as the *Monodepth* notebook, run the command below.
.. code:: bash
jupyter 201-vision-monodepth.ipynb
Launch All Notebooks
--------------------------
.. code:: bash
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.
|launch-jupyter|
--------------------
.. raw:: html
<a name="-manage-the-notebooks"/>
`Manage the Notebooks`_
========================
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
------------------------------------
First, make sure you use the terminal window where you activated ``openvino_env``. To deactivate your ``virtualenv``, simply run:
.. code:: bash
deactivate
This will deactivate your virtual environment.
Reactivate Virtual Environment
------------------------------------
To reactivate your environment, run:
.. tab:: WINDOWS
.. code:: bash
source openvino_env\Scripts\activate
.. tab:: Linux Systems
.. code:: bash
source openvino_env/bin/activate
.. tab:: macOS
.. code:: bash
source openvino_env/bin/activate
Then type ``jupyter lab`` or ``jupyter notebook`` to launch the notebooks again.
Delete Virtual Environment
-------------------------------------
This operation is optional. However, if you want to remove your virtual environment, simply delete the ``openvino_env`` directory:
.. tab:: WINDOWS
.. code:: bash
rmdir /s openvino_env
.. tab:: Linux Systems
.. code:: bash
rm -rf openvino_env
.. tab:: macOS
.. code:: bash
rm -rf openvino_env
Remove openvino_env Kernel from Jupyter
-------------------------------------------
.. code:: bash
jupyter kernelspec remove openvino_env
If you run into issues, check the `Troubleshooting <#-troubleshooting>`__, and `FAQs <#-faq>`__ sections or start a GitHub
`discussion <https://github.com/openvinotoolkit/openvino_notebooks/discussions>`__.
-------------------
.. raw:: html
<a name="-troubleshooting"/>
`Troubleshooting`_
====================
- To check some common installation problems, run
``python check_install.py``. This script is located in the
openvino_notebooks directory. 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 ``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 the following tips do not solve your problem, feel free to open a `discussion
topic <https://github.com/openvinotoolkit/openvino_notebooks/discussions>`__
or create an
`issue <https://github.com/openvinotoolkit/openvino_notebooks/issues>`__! on Github.
.. raw:: html
<a name="-faq"/>
`FAQ`_
========
- `Which devices does OpenVINO
support? <https://docs.openvino.ai/2022.1/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html>`__
- `What is the first CPU generation that OpenVINO
supports? <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html>`__
- `Are there any success stories about deploying real-world solutions
with
OpenVINO? <https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html>`__
--------------
`Additional Resources`_
-------------------------
* `OpenVINO™ Notebooks - Github Repository <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md>`_
* `Install OpenVINO™ Development Tools <https://docs.openvino.ai/nightly/openvino_docs_install_guides_install_dev_tools.html>`_
.. |br| raw:: html
<br />
.. |launch-jupyter| image:: https://user-images.githubusercontent.com/15709723/120527271-006fd200-c38f-11eb-9935-2d36d50bab9f.gif
.. |Apache License Version 2.0| image:: https://img.shields.io/badge/license-Apache_2.0-green.svg
:target: https://github.com/openvinotoolkit/openvino_notebooks/blob/main/LICENSE
.. |nbval| image:: https://github.com/openvinotoolkit/openvino_notebooks/actions/workflows/nbval.yml/badge.svg
:target: https://github.com/openvinotoolkit/openvino_notebooks/actions/workflows/nbval.yml?query=branch%3Amain
.. |nbval-docker| image:: https://github.com/openvinotoolkit/openvino_notebooks/actions/workflows/docker.yml/badge.svg
:target: https://github.com/openvinotoolkit/openvino_notebooks/actions/workflows/nbval.yml?query=branch%3Amain
.. |binder logo| image:: https://mybinder.org/badge_logo.svg
:alt: Binder button
.. |ml-studio-1| image:: https://user-images.githubusercontent.com/15709723/117559437-17463180-b03a-11eb-9e8d-d4539d1502f2.png
.. |ml-studio-2| image:: https://user-images.githubusercontent.com/15709723/117582205-b6f4d580-b0b5-11eb-9b83-eb2004ad9b19.png
.. |docker-terminal-1| image:: https://user-images.githubusercontent.com/15709723/127793994-355e4d29-d131-432d-a12a-b08ca6131223.png
@endsphinxdirective

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@ -4,26 +4,465 @@
.. _notebook tutorials:
.. meta::
:description: A collection of Python tutorials run on Jupyter notebooks. The
tutorials explain how to use OpenVINO™ toolkit for optimized
deep learning inference.
:keywords: OpenVINO™ toolkit, Jupyter, Jupyter notebooks, tutorials, Python
API, Python, deep learning, inference, model inference, infer a
model, Binder, object detection, quantization, image
classification, speech recognition, OCR, OpenVINO IR, deep
learning model, AI, neural networks
.. toctree::
:maxdepth: 2
:caption: Notebooks
:hidden:
notebooks-installation
notebooks/notebooks
@endsphinxdirective
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.
This collection of Python tutorials are written for running on [Jupyter*](https://jupyter.org) 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 a |binder logo| button can be run without installing anything.
Once you have found the tutorial of your interest, just click the button next to
the name of it and `Binder <https://mybinder.org/>`__ will start it in a new tab of a browser.
Binder is a free online service with limited resources (for more information about it,
see the `Additional Resources <#-additional-resources>`__ section).
@sphinxdirective
.. note::
For the best performance, more control and resources, you should run the notebooks locally.
Follow the `Installation Guide <notebooks-installation.html>`__ in order to get information
on how to run and manage the notebooks on your machine.
|binder_link|
.. |binder_link| raw:: html
--------------------
**Contents:**
- `Getting Started <#-getting-started>`__
- `First steps with OpenVINO <#-first-steps>`__
- `Convert & Optimize <#-convert--optimize>`__
- `Model Demos <#-model-demos>`__
- `Model Training <#-model-training>`__
- `Live Demos <#-live-demos>`__
- `Recommended Tutorials <#-recommended-tutorials>`__
- `Additional Resources <#-additional-resources>`__
- `Contributors <#-contributors>`__
--------------------
.. raw:: html
<a name='-getting-started' id='-getting-started'/>
`Getting Started`_
==================
The Jupyter notebooks are categorized into four classes, select one
related to your needs or give them all a try. Good Luck!
.. raw:: html
<a name='-first-steps' id='-first-steps' />
`First steps with OpenVINO`_
-------------------------------
Brief tutorials that demonstrate how to use Python API for inference in OpenVINO.
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | Description | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `001-hello-world <notebooks/001-hello-world-with-output.html>`__ |br| |n001| | Classify an image with OpenVINO. | |n001-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `002-openvino-api <notebooks/002-openvino-api-with-output.html>`__ |br| |n002| | Learn the OpenVINO Python API. | |n002-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `003-hello-segmentation <notebooks/003-hello-segmentation-with-output.html>`__ |br| |n003| | Semantic segmentation with OpenVINO. | |n003-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `004-hello-detection <notebooks/004-hello-detection-with-output.html>`__ |br| |n004| | Text detection with OpenVINO. | |n004-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
.. raw:: html
<a name='-convert--optimize' id='-convert--optimize'/>
`Convert & Optimize`_
-----------------------
Tutorials that explain how to optimize and quantize models with OpenVINO tools.
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | Description | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `101-tensorflow-to-openvino <notebooks/101-tensorflow-to-openvino-with-output.html>`__ |br| |n101| | Convert TensorFlow models to OpenVINO IR. | |n101-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `102-pytorch-onnx-to-openvino <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__ | Convert PyTorch models to OpenVINO IR. | |n102-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `103-paddle-onnx-to-openvino <notebooks/103-paddle-onnx-to-openvino-classification-with-output.html>`__ |br| |n103| | Convert PaddlePaddle models to OpenVINO IR. | |n103-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `104-model-tools <notebooks/104-model-tools-with-output.html>`__ |br| |n104| | Download, convert and benchmark models from Open Model Zoo. | |n104-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
.. dropdown:: Explore more notebooks here.
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| Notebook | Description |
+==============================================================================================================================+==================================================================================================================================+
| `105-language-quantize-bert <notebooks/105-language-quantize-bert-with-output.html>`__ | Optimize and quantize a pre-trained BERT model |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `106-auto-device <notebooks/106-auto-device-with-output.html>`__ | Demonstrates how to use AUTO Device |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `107-speech-recognition-quantization <notebooks/107-speech-recognition-quantization-with-output.html>`__ | Optimize and quantize a pre-trained Wav2Vec2 speech model |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `110-ct-segmentation-quantize <notebooks/110-ct-segmentation-quantize-with-output.html>`__ | Quantize a kidney segmentation model and show live inference |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `111-detection-quantization <notebooks/111-detection-quantization-with-output.html>`__ |br| |n111| | Quantize an object detection model |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `112-pytorch-post-training-quantization-nncf <notebooks/112-pytorch-post-training-quantization-nncf-with-output.html>`__ | Use Neural Network Compression Framework (NNCF) to quantize PyTorch model in post-training mode (without model fine-tuning) |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `113-image-classification-quantization <notebooks/113-image-classification-quantization-with-output.html>`__ | Quantize mobilenet image classification |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `114-quantization-simplified-mode <notebooks/114-quantization-simplified-mode-with-output.html>`__ | Quantize Image Classification Models with POT in Simplified Mode |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
| `115-async-api <notebooks/115-async-api-with-output.html>`__ | Use Asynchronous Execution to Improve Data Pipelining |
+------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
.. raw:: html
<a name='-model-demos' id='-model-demos'/>
`Model Demos`_
----------------
Demos that demonstrate inference on a particular model.
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | Description | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `210-ct-scan-live-inference <notebooks/210-ct-scan-live-inference-with-output.html>`__ |br| |n210| | Show live inference on segmentation of CT-scan data. | |n210-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `211-speech-to-text <notebooks/211-speech-to-text-with-output.html>`__ |br| |n211| | Run inference on speech-to-text recognition model. | |n211-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `208-optical-character-recognition <notebooks/208-optical-character-recognition-with-output.html>`__ | Annotate text on images using text recognition resnet. | |n208-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `209-handwritten-ocr <notebooks/209-handwritten-ocr-with-output.html>`__ |br| |n209| | OCR for handwritten simplified Chinese and Japanese. | |n209-img1| |br| |chinese-text| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `218-vehicle-detection-and-recognition <notebooks/218-vehicle-detection-and-recognition-with-output.html>`__ | Use pre-trained models to detect and recognize vehicles and their attributes with OpenVINO. | |n218-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
.. dropdown:: Explore more notebooks below.
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | Description | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `201-vision-monodepth <notebooks/201-vision-monodepth-with-output.html>`__ |br| |n201| | Monocular depth estimation with images and video. | |n201-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `202-vision-superresolution-image <notebooks/202-vision-superresolution-image-with-output.html>`__ |br| |n202i| | Upscale raw images with a super resolution model. | |n202i-img1| → |n202i-img2| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `202-vision-superresolution-video <notebooks/202-vision-superresolution-video-with-output.html>`__ |br| |n202v| | Turn 360p into 1080p video using a super resolution model. | |n202v-img1| → |n202v-img2| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `203-meter-reader <notebooks/203-meter-reader-with-output.html>`__ |br| |n203| | PaddlePaddle pre-trained models to read industrial meter's value | |n203-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `204-named-entity-recognition <notebooks/204-named-entity-recognition-with-output.html>`__ |br| |n204| | Perform named entity recognition on simple text. | |n204-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `205-vision-background-removal <notebooks/205-vision-background-removal-with-output.html>`__ |br| |n205| | Remove and replace the background in an image using salient object detection. | |n205-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `206-vision-paddlegan-anime <notebooks/206-vision-paddlegan-anime-with-output.html>`__ |br| |n206| | Turn an image into anime using a GAN. | |n206-img1| → |n206-img2| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `207-vision-paddlegan-superresolution <notebooks/207-vision-paddlegan-superresolution-with-output.html>`__ |br| |n207| | Upscale small images with superresolution using a PaddleGAN model. | |n207-img1| → |n207-img2| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `212-onnx-style-transfer <notebooks/212-onnx-style-transfer-with-output.html>`__ |br| |n212| | Transform images to five different styles with neural style transfer. | |n212-img1| → |n212-img2| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `214-vision-paddle-classification <notebooks/214-vision-paddle-classification-with-output.html>`__ |br| |n214| | PaddlePaddle Image Classification with OpenVINO. | |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `215-image-inpainting <notebooks/215-image-inpainting-with-output.html>`__ | Fill missing pixels with image in-painting. | |n215-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `216-license-plate-recognition <notebooks/216-license-plate-recognition-with-output.html>`__ | Recognize Chinese license plates in traffic. | |n216-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `217-vision-deblur <notebooks/217-vision-deblur-with-output.html>`__ |br| |n217| | Deblur Images with DeblurGAN-v2. | |n217-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `219-knowledge-graphs-conve <notebooks/219-knowledge-graphs-conve-with-output.html>`__ | Optimize the knowledge graph embeddings model (ConvE) with OpenVINO | |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `220-yolov5-accuracy-check-and-quantization <notebooks/220-yolov5-accuracy-check-and-quantization-with-output.html>`__ | Quantize the Ultralytics YOLOv5 model and check accuracy using the OpenVINO POT API | |n220-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `221-machine-translation <notebooks/221-machine-translation-with-output.html>`__ | Real-time translation from English to German | |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `222-vision-image-colorization <notebooks/222-vision-image-colorization-with-output.html>`__ | Use pre-trained models to colorize black & white images using OpenVINO | |n222-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `223-gpt2-text-prediction <notebooks/223-gpt2-text-prediction-with-output.html>`__ | Use GPT-2 to perform text prediction on an input sequence | |n223-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
.. raw:: html
<a name='-model-training' id='-model-training' />
`Model Training`_
------------------
Tutorials that include code to train neural networks.
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | Description | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `301-tensorflow-training-openvino <notebooks/301-tensorflow-training-openvino-with-output.html>`__ | Train a flower classification model from TensorFlow, then convert to OpenVINO IR. | |n301-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `301-tensorflow-training-openvino-pot <notebooks/301-tensorflow-training-openvino-pot-with-output.html>`__ | Use Post-training Optimization Tool (POT) to quantize the flowers model. | |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `302-pytorch-quantization-aware-training <notebooks/302-pytorch-quantization-aware-training-with-output.html>`__ | Use Neural Network Compression Framework (NNCF) to quantize PyTorch model. | |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `305-tensorflow-quantization-aware-training <notebooks/305-tensorflow-quantization-aware-training-with-output.html>`__ | Use Neural Network Compression Framework (NNCF) to quantize TensorFlow model. | |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
.. raw:: html
<a name='-live-demos' id='-live-demos' />
`Live Demos`_
---------------
Live inference demos that run on a webcam or video files.
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | Description | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `401-object-detection-webcam <notebooks/401-object-detection-with-output.html>`__ |br| |n401| | Object detection with a webcam or video file. | |n401-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `402-pose-estimation-webcam <notebooks/402-pose-estimation-with-output.html>`__ |br| |n402| | Human pose estimation with a webcam or video file. | |n402-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `403-action-recognition-webcam <notebooks/403-action-recognition-webcam-with-output.html>`__ |br| |n403| | Human action recognition with a webcam or video file. | |n403-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `405-paddle-ocr-webcam <notebooks/405-paddle-ocr-webcam-with-output.html>`__ |br| |n405| | OCR with a webcam or video file | |n405-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
.. raw:: html
<a name='-recommended-tutorials' id='-recommended-tutorials'/>
`Recommended Tutorials`_
--------------------------
The following tutorials are guaranteed to provide a great experience with inference in OpenVINO:
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| Notebook | | Preview |
+===============================================================================================================================+============================================================================================================================================+===========================================+
| `Vision-monodepth <notebooks/201-vision-monodepth-with-output.html>`__ |br| |n201| | Monocular depth estimation with images and video. | |n201-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `CT-scan-live-inference <notebooks/210-ct-scan-live-inference-with-output.html>`__ |br| |n210| | Show live inference on segmentation of CT-scan data. | |n210-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `Object-detection-webcam <notebooks/401-object-detection-with-output.html>`__ |br| |n401| | Object detection with a webcam or video file. | |n401-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `Pose-estimation-webcam <notebooks/402-pose-estimation-with-output>`__ |br| |n402| | Human pose estimation with a webcam or video file. | |n402-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
| `Action-recognition-webcam <notebooks/403-action-recognition-webcam-with-output.html>`__ |br| |n403| | Human action recognition with a webcam or video file. | |n403-img1| |
+-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
-------------------
.. note::
If there are any issues while running the notebooks, refer to the **Troubleshooting** and **FAQ** sections in the `Installation Guide <notebooks-installation.html>`__ or start a GitHub
`discussion <https://github.com/openvinotoolkit/openvino_notebooks/discussions>`__.
.. raw:: html
<a name='-additional-resources' id='-additional-resources'/>
`Additional Resources`_
-------------------------
* `OpenVINO™ Notebooks - Github Repository <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md>`_
* `Binder documentation <https://mybinder.readthedocs.io/en/latest/>`_
.. raw:: html
<a name='-contributors' id='-contributors' />
`Contributors`_
--------------------------
|contributors|
Made with `contributors-img <https://contrib.rocks>`__.
.. |br| raw:: html
<br />
.. |chinese-text| raw:: html
<span style="font-size:10px">的人不一了是他有为在责新中任自之我们</span>
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:alt: Binder button
<a href="https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F001-hello-world%2F001-hello-world.ipynb" target="_blank"><img src="https://mybinder.org/badge_logo.svg" alt="Binder"></a>
@endsphinxdirective
Tutorials showing this logo may be run remotely using Binder with no setup, although running the notebooks on a local system is recommended for best performance. See the [OpenVINO™ Notebooks Installation Guide](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide) to install and run locally.