Installation of OpenVINO™ Notebooks — OpenVINO™ documentation (original) (raw)

The notebooks can be run in various environments. This guide will show you how to run and manage them on your local system.

Contents:

• Installation Guide
• Run the Notebooks
• Manage the notebooks
• Troubleshooting

Installation Guide#

The table below lists the supported operating systems and Python versions.

OpenVINO Notebooks also require Git. Follow the guide below for your operating system or environment.

Installing prerequisites#

Windows

1. Install Python
   Download 64 bit version of Python software (3.9 - 3.12) from python.org
   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. Also, it is recommended to use the installer option to disable the PATH length limit.
   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
   Run the installer by double clicking it. Follow the installation steps to set up the software.
3. Install FFMPEG (Optional)
   Download FFMPEG binary from here
   Set FFMPEG’s path (e.g., C:\ffmpeg\bin) to the PATH environmental variable on Windows.

Linux

1. Install Python and GIT
   Note
   Linux Systems may require installation of additional libraries.
   The following installation steps should work on a clean install of Ubuntu Desktop 20.04, and should also work on Ubuntu 22.04 and 20.10, and on Ubuntu Server.
   sudo apt-get update
   sudo apt-get upgrade
   sudo apt-get install python3-venv build-essential python3-dev git-all libgl1-mesa-dev ffmpeg
   For an Intel Integrated Graphics Card, you can install the Intel Graphics Compute Runtime to enable inference on this device. The command for Ubuntu 20.04 is:
   Note
   Only execute this command if you do not yet have OpenCL drivers installed.
   sudo apt-get install intel-opencl-icd
   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 section.
   sudo yum update
   sudo yum upgrade
   sudo yum install python36-devel mesa-libGL

macOS

Alternatively, you may skip steps 1-3 if you prefer to manually install Python 3 and Git.

1. Install Xcode Command Line Tools
2. Install Homebrew
   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
   After you install it, follow the instructions from the Homebrew installation to set it up.
3. Install Python and dependencies
   brew install python@3.9
   brew install protobuf

optional but recommended

brew install ffmpeg
Run each step below in a terminal.
Note
If OpenVINO is installed globally, do not run any of these commands in a terminal where setupvars.sh is sourced.

Azure ML

1. Adding a Compute Instance
   In Azure ML Studio, add a compute instance and pick any CPU-based instance. At least 4 CPU cores and 8GB of RAM are recommended.
   
2. Start the Terminal
   Once the compute instance has started, open the terminal window and then follow the installation steps below.
   

Docker

To run the notebooks inside a Linux-based Docker container, use the Dockerfile:

:caption: Source: https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/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-14.18.1 mesa-libGL dos2unix libsndfile &&
yum -y update-minimal --security --sec-severity=Important --sec-severity=Critical --sec-severity=Moderate

GPU drivers

RUN dnf install -y 'dnf-command(config-manager)' &&
dnf config-manager --add-repo https://repositories.intel.com/graphics/rhel/8.5/intel-graphics.repo

RUN rpm -ivh https://vault.centos.org/centos/8/AppStream/x86_64/os/Packages/mesa-filesystem-21.1.5-1.el8.x86_64.rpm &&
dnf install --refresh -y
intel-opencl-22.28.23726.1-i419.el8.x86_64 intel-media intel-mediasdk libmfxgen1 libvpl2
level-zero intel-level-zero-gpu
intel-metrics-library intel-igc-core intel-igc-cm
libva libva-utils intel-gmmlib &&
rpm -ivh http://mirror.centos.org/centos/8-stream/AppStream/x86_64/os/Packages/ocl-icd-2.2.12-1.el8.x86_64.rpm &&
rpm -ivh https://download-ib01.fedoraproject.org/pub/epel/8/Everything/x86_64/Packages/c/clinfo-3.0.21.02.21-4.el8.x86_64.rpm

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 COPY .ci/validate_notebooks.py /tmp/scripts COPY .ci/ignore_treon_docker.txt /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

Amazon SageMaker

1. Log into your Amazon SageMaker Studio Environment and Add user.
   
2. Choose desired user profile name
   
3. Choose Jupyter Lab version 3.0
   
4. Choose the remaining default setting and click “Submit” to add a user.
5. Launch the Amazon SageMaker Studio environment.
   Click “Open Studio” to start the environment:
   
   Note
   You are using an ml.t3.medium instance, which is for free for 250 hours per month for the first 2 months on Studio notebook.
6. Wait for a couple of minutes for your environment to load.
   You should be able to see the following screen:
   
7. Select a SageMaker image.
   Choose Data Science 3.0 in “Select a SageMaker image” drop-down under “Notebooks and compute resources”.
   Then, click + on “Image Terminal” to start a terminal session:
   

Installing notebooks#

Windows

1. Create a Virtual Environment
   If you already have installed openvino-dev, you may skip this step and proceed with the next one.
   python -m venv openvino_env
2. Activate the Environment
   openvino_env\Scripts\activate
3. Clone the Repository
   Using the –depth=1 option for git clone reduces download size.
   git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
   cd openvino_notebooks
4. Upgrade PIP
   python -m pip install --upgrade pip wheel setuptools
5. Install required packages
   pip install -r requirements.txt
   .. important::
   In case of problems with accessing HuggingFace in PRC, set-up the networking
   environment before you launch the notebooks:
   .. code-block::
   pip install -U huggingface_hub
   set HF_ENDPOINT = https://hf-mirror.com
   For more information, visit HF-Mirror HuggingFace <https://hf-mirror.com>__.

Linux

1. Create a Virtual Environment
   If you already have installed openvino-dev, you may skip this step and proceed with the next one.
   python3 -m venv openvino_env
2. Activate the Environment
   source openvino_env/bin/activate
3. Clone the Repository
   Using the –depth=1 option for git clone reduces download size.
   git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
   cd openvino_notebooks
4. Upgrade PIP
   python -m pip install --upgrade pip
   pip install wheel setuptools
5. Install required packages
   pip install -r requirements.txt
   .. important::
   In case of problems with accessing HuggingFace in PRC, set-up the networking
   environment before you launch the notebooks:
   .. code-block::
   pip install -U huggingface_hub
   set HF_ENDPOINT = https://hf-mirror.com
   For more information, visit HF-Mirror HuggingFace <https://hf-mirror.com>__.

macOS

1. Create a Virtual Environment
   If you already have installed openvino-dev, you may skip this step and proceed with the next one.
   python3 -m venv openvino_env
2. Activate the Environment
   source openvino_env/bin/activate
3. Clone the Repository
   Using the –depth=1 option for git clone reduces download size.
   git clone --depth=1 https://github.com/openvinotoolkit/openvino_notebooks.git
   cd openvino_notebooks
4. Upgrade PIP
   python -m pip install --upgrade pip wheel setuptools
5. Install required packages
   pip install -r requirements.txt

Azure ML

1. Create a Conda environment
   conda create --name openvino_env python=3.9 -y
2. Activate the environment
   conda activate openvino_env
3. Clone OpenVINO notebooks
   git clone https://github.com/openvinotoolkit/openvino_notebooks.git
4. Change directory to openvino_notebooks
5. Upgrade pip and install required dependencies.
   python -m pip install --upgrade pip
   pip install -r requirements.txt
6. Add openvino_env to PATH
   set PATH="/anaconda/envs/openvino_env/bin;%PATH%"
7. Run the notebooks.
   To run the notebooks, click on Notebooks and refresh your Files:
   
   Note
   Make sure you are using the openvino_env environment (not Python 3).
   

Docker

1. Clone the Repository
   git clone https://github.com/openvinotoolkit/openvino_notebooks.git
   cd openvino_notebooks
2. Build the Docker Image
   docker build -t openvino_notebooks .
3. Run the Docker Image
   docker run -it -p 8888:8888 openvino_notebooks
   Note
   For using model training notebooks, allocate additional memory:
   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.
   If it is a remote machine, replace 127.0.0.1 with the correct IP address.
   
   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-based image provided by Project Thoth.
   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.

Amazon SageMaker

Use the terminal and follow the steps below.

1. Install few system dependencies.
   apt update
   apt install build-essential -y
   apt install libpython3.9-dev -y
   apt install libgl1-mesa-glx -y
2. Setup OpenVINO conda environment.
   conda create --name openvino_env python=3.9
   conda activate openvino_env
   conda install ipykernel
   set PATH="/anaconda/envs/openvino_env/bin;%PATH%"
3. Setup OpenVINO Notebooks.
   git clone https://github.com/openvinotoolkit/openvino_notebooks.git
   cd openvino_notebooks

Install OpenVINO and OpenVINO notebook Requirements

python -m pip install --upgrade pip
pip install -r requirements.txt 4. Run the Notebooks

• To run the notebooks, click the top level “openvino_notebooks” folder and navigate to your example:
  
• Choose “Image” - Data Science 3.0, “Kernel” - Python [conda env:openvino_env],“Instance type”- your desired compute instance.
  
  
  
  Note
  Make sure you use the Python [conda env:openvino_env]environment (not Python 3).
• Next, run the cells of the notebook. You may try other notebooks to explore OpenVINO features and examples.

Run the Notebooks#

Launch a Single Notebook#

If you want to launch only one notebook, such as the Monodepth notebook, run the command below.

jupyter lab notebooks/vision-monodepth/vision-monodepth.ipynb

Launch All 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.

Manage the Notebooks#

Shut Down Jupyter Kernel#

To end your Jupyter session, press Ctrl-c. This will prompt you toShutdown 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:

This will deactivate your virtual environment.

Reactivate Virtual Environment#

To reactivate your environment, run:

Windows

source openvino_env\Scripts\activate

Linux

source openvino_env/bin/activate

macOS

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:

Remove openvino_env Kernel from Jupyter#

jupyter kernelspec remove openvino_env

If you run into issues, check the Troubleshooting, and FAQs sections or start a GitHubdiscussion.

Troubleshooting#

For solutions to common issues during installation, refer to the Troubleshooting andFAQ sections in openvino_notebooks repository.

If the above tips do not solve your problem, feel free to open adiscussion topicor create anissue on Github.

Additional Resources#

• OpenVINO™ Notebooks - Github Repository