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DOCS shift to rst - Installing OpenVINO (#16311)

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@ -26,7 +26,7 @@ Make sure that you also [install OpenCV](https://github.com/opencv/opencv/wiki/B
### Install OpenVINO Development Tools
To install OpenVINO Development Tools, follow the [instructions for C++ developers on the Install OpenVINO Development Tools page](../install_guides/installing-model-dev-tools.md#cpp-developers). This guide uses the `googlenet-v1` model from the Caffe framework, therefore, when you get to Step 4 of the installation, run the following command to install OpenVINO with the Caffe requirements:
To install OpenVINO Development Tools, follow the [instructions for C++ developers on the Install OpenVINO Development Tools page](../install_guides/installing-model-dev-tools.md#cpp_developers). This guide uses the `googlenet-v1` model from the Caffe framework, therefore, when you get to Step 4 of the installation, run the following command to install OpenVINO with the Caffe requirements:
``` sh
pip install openvino-dev[caffe]

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# Install OpenVINO™ Development Tools {#openvino_docs_install_guides_install_dev_tools}
@sphinxdirective
OpenVINO Development Tools is a set of utilities that make it easy to develop and optimize models and applications for OpenVINO. It provides the following tools:
* Model Optimizer
@ -12,42 +14,56 @@ The instructions on this page show how to install OpenVINO Development Tools. If
In both cases, Python 3.7 - 3.10 needs to be installed on your machine before starting.
> **NOTE**: From the 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI.
.. note::
From the 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI.
## <a name="python-developers"></a>For Python Developers
.. _python_developers:
If you are a Python developer, follow the steps in the <a href="#install-dev-tools">Installing OpenVINO Development Tools</a> section on this page to install it. Installing OpenVINO Development Tools will also install OpenVINO Runtime as a dependency, so you don’t need to install OpenVINO Runtime separately. This option is recommended for new users.
For Python Developers
#####################
If you are a Python developer, follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it. Installing OpenVINO Development Tools will also install OpenVINO Runtime as a dependency, so you don’t need to install OpenVINO Runtime separately. This option is recommended for new users.
## <a name="cpp-developers"></a>For C++ Developers
If you are a C++ developer, you must first install OpenVINO Runtime separately to set up the C++ libraries, sample code, and dependencies for building applications with OpenVINO. These files are not included with the PyPI distribution. See the [Install OpenVINO Runtime](./installing-openvino-runtime.md) page to install OpenVINO Runtime from an archive file for your operating system.
.. _cpp_developers:
Once OpenVINO Runtime is installed, you may install OpenVINO Development Tools for access to tools like Model Optimizer, Model Downloader, Benchmark Tool, and other utilities that will help you optimize your model and develop your application. Follow the steps in the <a href="#install-dev-tools">Installing OpenVINO Development Tools</a> section on this page to install it.
For C++ Developers
##################
If you are a C++ developer, you must first install OpenVINO Runtime separately to set up the C++ libraries, sample code, and dependencies for building applications with OpenVINO. These files are not included with the PyPI distribution. See the :doc:`Install OpenVINO Runtime <openvino_docs_install_guides_install_runtime>` page to install OpenVINO Runtime from an archive file for your operating system.
Once OpenVINO Runtime is installed, you may install OpenVINO Development Tools for access to tools like Model Optimizer, Model Downloader, Benchmark Tool, and other utilities that will help you optimize your model and develop your application. Follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it.
.. _install_dev_tools:
Installing OpenVINO™ Development Tools
######################################
## <a name="install-dev-tools"></a>Installing OpenVINO™ Development Tools
Follow these step-by-step instructions to install OpenVINO Development Tools on your computer.
There are two options to install OpenVINO Development Tools: installation into an existing environment with a deep learning framework that was used
for model training or creation; or installation into a new environment.
### Installation into an Existing Environment with the Source Deep Learning Framework
Installation into an Existing Environment with the Source Deep Learning Framework
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
To install OpenVINO Development Tools (see the [Install the Package](#install-the-package) section of this article) into an existing environment
To install OpenVINO Development Tools (see the :ref:`Install the Package <install_the_package>` section of this article) into an existing environment
with the deep learning framework used for the model training or creation, run the following command:
```sh
pip install openvino-dev
```
.. code-block:: sh
### Installation in a New Environment
pip install openvino-dev
Installation in a New Environment
+++++++++++++++++++++++++++++++++
If you do not have an environment with a deep learning framework for the input model or you encounter any compatibility issues between OpenVINO
and your version of deep learning framework, you may install OpenVINO Development Tools with validated versions of frameworks into a new environment.
#### Step 1. Set Up Python Virtual Environment
Step 1. Set Up Python Virtual Environment
-----------------------------------------
Create a virtual Python environment to avoid dependency conflicts. To create a virtual environment, use the following command:
@sphinxdirective
.. tab:: Linux and macOS
.. code-block:: sh
@ -61,15 +77,12 @@ Create a virtual Python environment to avoid dependency conflicts. To create a v
python -m venv openvino_env
@endsphinxdirective
#### Step 2. Activate Virtual Environment
Step 2. Activate Virtual Environment
------------------------------------
Activate the newly created Python virtual environment by issuing this command:
@sphinxdirective
.. tab:: Linux and macOS
.. code-block:: sh
@ -86,79 +99,103 @@ Activate the newly created Python virtual environment by issuing this command:
The above command must be re-run every time a new command terminal window is opened.
@endsphinxdirective
Step 3. Set Up and Update PIP to the Highest Version
----------------------------------------------------
#### Step 3. Set Up and Update PIP to the Highest Version
Make sure `pip` is installed in your environment and upgrade it to the latest version by issuing the following command:
```sh
python -m pip install --upgrade pip
```
.. code-block:: sh
#### Step 4. <a name="install-the-package"></a> Install the Package
python -m pip install --upgrade pip
.. _install_the_package:
Step 4. Install the Package
---------------------------
To install and configure the components of the development package together with validated versions of specific frameworks, use the commands below.
```sh
pip install openvino-dev[extras]
```
.. code-block:: sh
where the `extras` parameter specifies the source deep learning framework for the input model
and is one or more of the following values separated with "," : `caffe`, `kaldi`, `mxnet`, `onnx`, `pytorch`, `tensorflow`, `tensorflow2`.
pip install openvino-dev[extras]
where the ``extras`` parameter specifies the source deep learning framework for the input model
and is one or more of the following values separated with "," : ``caffe``, ``kaldi``, ``mxnet``, ``onnx``, ``pytorch``, ``tensorflow``, ``tensorflow2``.
For example, to install and configure dependencies required for working with TensorFlow 2.x and ONNX models, use the following command:
```sh
pip install openvino-dev[tensorflow2,onnx]
```
.. code-block:: sh
> **NOTE**: Model Optimizer support for TensorFlow 1.x environment has been deprecated. Use the `tensorflow2` parameter to install a TensorFlow 2.x environment that can convert both TensorFlow 1.x and 2.x models. If your model isn't compatible with the TensorFlow 2.x environment, use the `tensorflow` parameter to install the TensorFlow 1.x environment. The TF 1.x environment is provided only for legacy compatibility reasons.
pip install openvino-dev[tensorflow2,onnx]
For more details on the openvino-dev PyPI package, see https://pypi.org/project/openvino-dev/.
### Step 5. Test the Installation
.. note::
Model Optimizer support for TensorFlow 1.x environment has been deprecated. Use the ``tensorflow2`` parameter to install a TensorFlow 2.x environment that can convert both TensorFlow 1.x and 2.x models. If your model isn't compatible with the TensorFlow 2.x environment, use the `tensorflow` parameter to install the TensorFlow 1.x environment. The TF 1.x environment is provided only for legacy compatibility reasons.
For more details on the openvino-dev PyPI package, see `pypi.org <https://pypi.org/project/openvino-dev/>`__ .
Step 5. Test the Installation
+++++++++++++++++++++++++++++
To verify the package is properly installed, run the command below (this may take a few seconds):
```sh
mo -h
```
.. code-block:: sh
You will see the help message for Model Optimizer if installation finished successfully. If you get an error, refer to the [Troubleshooting Guide](./troubleshooting.md) for possible solutions.
mo -h
You will see the help message for Model Optimizer if installation finished successfully. If you get an error, refer to the :doc:`Troubleshooting Guide <openvino_docs_get_started_guide_troubleshooting>` for possible solutions.
Congratulations! You finished installing OpenVINO Development Tools with C++ capability. Now you can start exploring OpenVINO's functionality through example C++ applications. See the "What's Next?" section to learn more!
## What's Next?
What's Next?
############
Learn more about OpenVINO and use it in your own application by trying out some of these examples!
### Get started with Python
<img src="https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif" width=400>
Get started with Python
+++++++++++++++++++++++
Try the [Python Quick Start Example](https://docs.openvino.ai/nightly/notebooks/201-vision-monodepth-with-output.html) to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
.. image:: _static/images/get_started_with_python.gif
:width: 400
Visit the [Tutorials](../tutorials.md) page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* [OpenVINO Python API Tutorial](https://docs.openvino.ai/nightly/notebooks/002-openvino-api-with-output.html)
* [Basic image classification program with Hello Image Classification](https://docs.openvino.ai/nightly/notebooks/001-hello-world-with-output.html)
* [Convert a PyTorch model and use it for image background removal](https://docs.openvino.ai/nightly/notebooks/205-vision-background-removal-with-output.html)
Try the `Python Quick Start Example <https://docs.openvino.ai/nightly/notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
### Get started with C++
<img src="https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg" width=400>
Visit the :doc:`Tutorials <tutorials>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* `OpenVINO Python API Tutorial <https://docs.openvino.ai/nightly/notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <https://docs.openvino.ai/nightly/notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <https://docs.openvino.ai/nightly/notebooks/205-vision-background-removal-with-output.html>`__
Try the [C++ Quick Start Example](@ref openvino_docs_get_started_get_started_demos) for step-by-step instructions on building and running a basic image classification C++ application.
Get started with C++
++++++++++++++++++++
Visit the [Samples](../OV_Runtime_UG/Samples_Overview.md) page for other C++ example applications to get you started with OpenVINO, such as:
* [Basic object detection with the Hello Reshape SSD C++ sample](@ref openvino_inference_engine_samples_hello_reshape_ssd_README)
* [Automatic speech recognition C++ sample](@ref openvino_inference_engine_samples_speech_sample_README)
.. image:: _static/images/get_started_with_cpp.jpg
:width: 400
### Learn OpenVINO Development Tools
* Explore a variety of pre-trained deep learning models in the <a href="model_zoo.html">Open Model Zoo</a> and deploy them in demo applications to see how they work.
* Want to import a model from another framework and optimize its performance with OpenVINO? Visit the <a href="openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html">Model Optimizer Developer Guide</a>.
* Accelerate your model's speed even further with quantization and other compression techniques using <a href="pot_introduction.html">Post-Training Optimization Tool</a>.
* Benchmark your model's inference speed with one simple command using the <a href="openvino_inference_engine_tools_benchmark_tool_README.html">Benchmark Tool</a>.
Try the :doc:`C++ Quick Start Example <openvino_docs_get_started_get_started_demos>` for step-by-step instructions on building and running a basic image classification C++ application.
Visit the :doc:`Samples <openvino_docs_OV_UG_Samples_Overview>` page for other C++ example applications to get you started with OpenVINO, such as:
* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <openvino_inference_engine_samples_hello_reshape_ssd_README>`
* :doc:`Automatic speech recognition C++ sample <openvino_inference_engine_samples_speech_sample_README>`
Learn OpenVINO Development Tools
++++++++++++++++++++++++++++++++
* Explore a variety of pre-trained deep learning models in the :ref:`Open Model Zoo <model_zoo.html>` and deploy them in demo applications to see how they work.
* Want to import a model from another framework and optimize its performance with OpenVINO? Visit the :ref:`Model Optimizer Developer Guide <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html>`.
* Accelerate your model's speed even further with quantization and other compression techniques using :ref:`Post-Training Optimization Tool <pot_introduction.html>`.
* Benchmark your model's inference speed with one simple command using the :ref:`Benchmark Tool <openvino_inference_engine_tools_benchmark_tool_README.html`>.
## Additional Resources
- Intel® Distribution of OpenVINO™ toolkit home page: <https://software.intel.com/en-us/openvino-toolkit>
- For IoT Libraries & Code Samples, see [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
- [OpenVINO Installation Selector Tool](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html)
- `Intel® Distribution of OpenVINO™ toolkit home page <https://software.intel.com/en-us/openvino-toolkit>`__
- For IoT Libraries & Code Samples, see `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__ .
- `OpenVINO Installation Selector Tool <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html`>__
@endsphinxdirective

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@ -11,8 +11,6 @@
Build from Source <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md>
Creating a Yocto Image <openvino_docs_install_guides_installing_openvino_yocto>
@endsphinxdirective
Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for developing applications and solutions based on deep learning tasks, such as computer vision, automatic speech recognition, natural language processing, recommendation systems, and more. It provides high-performance and rich deployment options, from edge to cloud. Some of its advantages are:
* Enables CNN-based and transformer-based deep learning inference on the edge or cloud.
@ -21,9 +19,8 @@ Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for devel
* Compatible with models from a wide variety of frameworks, including TensorFlow, PyTorch, PaddlePaddle, ONNX, and more.
## Install OpenVINO
@sphinxdirective
Install OpenVINO
################
.. button-link:: https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html
:color: primary
@ -31,7 +28,6 @@ Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for devel
Check out the OpenVINO Download Page :fas:`fa-external-link-alt`
@endsphinxdirective
OpenVINO installation package is distributed in two parts: OpenVINO Runtime and OpenVINO Development Tools.
@ -43,40 +39,48 @@ OpenVINO installation package is distributed in two parts: OpenVINO Runtime and
- Accuracy Checker and Annotation Converter
- Model Downloader and other Open Model Zoo tools
### Option 1. Install OpenVINO Runtime and OpenVINO Development Tools (recommended)
Option 1. Install OpenVINO Runtime and OpenVINO Development Tools (recommended)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
The best way to get started with OpenVINO is to install OpenVINO Development Tools, which will also install the OpenVINO Runtime Python package as a dependency. Follow the instructions on the [Install OpenVINO Development Tools](installing-model-dev-tools.md) page to install it.
The best way to get started with OpenVINO is to install OpenVINO Development Tools, which will also install the OpenVINO Runtime Python package as a dependency. Follow the instructions on the :doc:`Install OpenVINO Development Tools <openvino_docs_install_guides_install_dev_tools>` page to install it.
**Python**
For developers working in Python, OpenVINO Development Tools can easily be installed using PyPI. See the <a href="openvino_docs_install_guides_install_dev_tools.html#python-developers">For Python Developers</a> section of the Install OpenVINO Development Tools page for instructions.
For developers working in Python, OpenVINO Development Tools can easily be installed using PyPI. See the :ref:`For Python Developers <openvino_docs_install_guides_install_dev_tools.html#python_developers>` section of the Install OpenVINO Development Tools page for instructions.
**C++**
For developers working in C++, the core OpenVINO Runtime libraries must be installed separately. Then, OpenVINO Development Tools can be installed using requirements files or PyPI. See the <a href="openvino_docs_install_guides_install_dev_tools.html#cpp-developers">For C++ Developers</a> section of the Install OpenVINO Development Tools page for instructions.
For developers working in C++, the core OpenVINO Runtime libraries must be installed separately. Then, OpenVINO Development Tools can be installed using requirements files or PyPI. See the :ref:`For C++ Developers <openvino_docs_install_guides_install_dev_tools.html#cpp_developers>` section of the Install OpenVINO Development Tools page for instructions.
### Option 2. Install OpenVINO Runtime only
Option 2. Install OpenVINO Runtime only
+++++++++++++++++++++++++++++++++++++++
OpenVINO Runtime may also be installed on its own without OpenVINO Development Tools. This is recommended for users who already have an optimized model and want to deploy it in an application that uses OpenVINO for inference on their device. To install OpenVINO Runtime only, follow the instructions on the [Install OpenVINO Runtime](installing-openvino-runtime.md) page.
OpenVINO Runtime may also be installed on its own without OpenVINO Development Tools. This is recommended for users who already have an optimized model and want to deploy it in an application that uses OpenVINO for inference on their device. To install OpenVINO Runtime only, follow the instructions on the :doc:`Install OpenVINO Runtime <openvino_docs_install_guides_install_runtime>` page.
The following methods are available to install OpenVINO Runtime:
* Linux: You can install OpenVINO Runtime using APT, YUM, archive files or Docker. See [Install OpenVINO on Linux](installing-openvino-linux-header.md).
* Windows: You can install OpenVINO Runtime using archive files or Docker. See [Install OpenVINO on Windows](installing-openvino-windows-header.md).
* macOS: You can install OpenVINO Runtime using archive files or Docker. See [Install OpenVINO on macOS](installing-openvino-macos-header.md).
* [Raspbian OS](installing-openvino-raspbian.md)
* Linux: You can install OpenVINO Runtime using APT, YUM, archive files or Docker. See :doc:`Install OpenVINO on Linux <openvino_docs_install_guides_installing_openvino_linux_header>`.
* Windows: You can install OpenVINO Runtime using archive files or Docker. See :doc:`Install OpenVINO on Windows <openvino_docs_install_guides_installing_openvino_windows_header>`.
* macOS: You can install OpenVINO Runtime using archive files or Docker. See :doc:`Install OpenVINO on macOS <openvino_docs_install_guides_installing_openvino_macos_header>`.
* :doc:`Raspbian OS <openvino_docs_install_guides_installing_openvino_raspbian>`
### Option 3. Build OpenVINO from source
Option 3. Build OpenVINO from source
++++++++++++++++++++++++++++++++++++
Source files are also available in the OpenVINO Toolkit GitHub repository. If you want to build OpenVINO from source for your platform, follow the [OpenVINO Build Instructions](https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md).
Source files are also available in the OpenVINO Toolkit GitHub repository. If you want to build OpenVINO from source for your platform, follow the `OpenVINO Build Instructions <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md>`__ .
## Next Steps
Still unsure if you want to install OpenVINO toolkit? Check out the [OpenVINO tutorials](../tutorials.md) to run example applications directly in your web browser without installing it locally. Here are some exciting demos you can explore:
- [Monodepth Estimation with OpenVINO](https://docs.openvino.ai/latest/notebooks/201-vision-monodepth-with-output.html)
- [Style Transfer on ONNX Models with OpenVINO](https://docs.openvino.ai/latest/notebooks/212-onnx-style-transfer-with-output.html)
- [OpenVINO API Tutorial](https://docs.openvino.ai/latest/notebooks/002-openvino-api-with-output.html)
Next Steps
##########
Still unsure if you want to install OpenVINO toolkit? Check out the :doc:`OpenVINO tutorials <tutorials>` to run example applications directly in your web browser without installing it locally. Here are some exciting demos you can explore:
- `Monodepth Estimation with OpenVINO <https://docs.openvino.ai/latest/notebooks/201-vision-monodepth-with-output.html>`__
- `Style Transfer on ONNX Models with OpenVINO <https://docs.openvino.ai/latest/notebooks/212-onnx-style-transfer-with-output.html>`__
- `OpenVINO API Tutorial <https://docs.openvino.ai/latest/notebooks/002-openvino-api-with-output.html>`__
Follow these links to install OpenVINO:
- [Install OpenVINO Development Tools](installing-model-dev-tools.md)
- [Install OpenVINO Runtime](installing-openvino-runtime.md)
- [Build from Source](https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md)
- :doc:`Install OpenVINO Development Tools <openvino_docs_install_guides_install_dev_tools>`
- :doc:`Install OpenVINO Runtime <openvino_docs_install_guides_install_runtime>`
- `Build from Source <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md>`__
@endsphinxdirective

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@ -1,103 +1,129 @@
# Create a Yocto Image with Intel® Distribution of OpenVINO™ toolkit {#openvino_docs_install_guides_installing_openvino_yocto}
@sphinxdirective
This document provides instructions for creating a Yocto image with Intel® Distribution of OpenVINO™ toolkit.
## System Requirements
System Requirements
###################
Follow the [Yocto Project official documentation](https://docs.yoctoproject.org/brief-yoctoprojectqs/index.html#compatible-linux-distribution) to set up and configure your host machine to be compatible with BitBake.
Follow the `Yocto Project official documentation <https://docs.yoctoproject.org/brief-yoctoprojectqs/index.html#compatible-linux-distribution>`__ to set up and configure your host machine to be compatible with BitBake.
## Step 1: Set Up Environment
Step 1: Set Up Environment
##########################
1. Clone the repositories.
```sh
git clone https://git.yoctoproject.org/git/poky
git clone https://git.yoctoproject.org/meta-intel
git clone https://git.openembedded.org/meta-openembedded
git clone https://github.com/kraj/meta-clang.git
```
.. code-block:: sh
git clone https://git.yoctoproject.org/git/poky
git clone https://git.yoctoproject.org/meta-intel
git clone https://git.openembedded.org/meta-openembedded
git clone https://github.com/kraj/meta-clang.git
2. Set up the OpenEmbedded build environment.
```sh
source poky/oe-init-build-env
```
.. code-block:: sh
source poky/oe-init-build-env
3. Add BitBake layers.
```sh
bitbake-layers add-layer ../meta-intel
bitbake-layers add-layer ../meta-openembedded/meta-oe
bitbake-layers add-layer ../meta-openembedded/meta-python
bitbake-layers add-layer ../meta-clang
```
.. code-block:: sh
bitbake-layers add-layer ../meta-intel
bitbake-layers add-layer ../meta-openembedded/meta-oe
bitbake-layers add-layer ../meta-openembedded/meta-python
bitbake-layers add-layer ../meta-clang
4. Verify if layers were added (optional step).
```sh
bitbake-layers show-layers
```
.. code-block:: sh
bitbake-layers show-layers
5. Set up BitBake configurations.
Include extra configuration in the `conf/local.conf` file in your build directory as required.
```sh
# Build with SSE4.2, AVX2 etc. extensions
MACHINE = "intel-skylake-64"
# Enable clDNN GPU plugin when needed.
# This requires meta-clang and meta-oe layers to be included in bblayers.conf
# and is not enabled by default.
PACKAGECONFIG:append:pn-openvino-inference-engine = " opencl"
.. code-block:: sh
# Build with SSE4.2, AVX2 etc. extensions
MACHINE = "intel-skylake-64"
# Enable clDNN GPU plugin when needed.
# This requires meta-clang and meta-oe layers to be included in bblayers.conf
# and is not enabled by default.
PACKAGECONFIG:append:pn-openvino-inference-engine = " opencl"
# Enable building OpenVINO Python API.
# This requires meta-python layer to be included in bblayers.conf.
PACKAGECONFIG:append:pn-openvino-inference-engine = " python3"
# This adds OpenVINO related libraries in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine"
# This adds OpenVINO samples in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine-samples"
# Include OpenVINO Python API package in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine-python3"
# Include Model Optimizer in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-model-optimizer"
# Enable building OpenVINO Python API.
# This requires meta-python layer to be included in bblayers.conf.
PACKAGECONFIG:append:pn-openvino-inference-engine = " python3"
# This adds OpenVINO related libraries in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine"
# This adds OpenVINO samples in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine-samples"
# Include OpenVINO Python API package in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine-python3"
# Include Model Optimizer in the target image.
CORE_IMAGE_EXTRA_INSTALL:append = " openvino-model-optimizer"
```
## Step 2: Build a Yocto Image with OpenVINO Packages
Step 2: Build a Yocto Image with OpenVINO Packages
##################################################
Run BitBake to build your image with OpenVINO packages. For example, to build the minimal image, run the following command:
```sh
bitbake core-image-minimal
```
> **NOTE**: For validation/testing/reviewing purposes, you may consider using the `nohup` command and ensure that your vpn/ssh connection remains uninterrupted.
.. code-block:: sh
bitbake core-image-minimal
## Step 3: Verify the Yocto Image
.. note::
For validation/testing/reviewing purposes, you may consider using the ``nohup`` command and ensure that your vpn/ssh connection remains uninterrupted.
Step 3: Verify the Yocto Image
##############################
Verify that OpenVINO packages were built successfully. Run the following command:
```sh
oe-pkgdata-util list-pkgs | grep openvino
```
.. code-block:: sh
oe-pkgdata-util list-pkgs | grep openvino
If the image build is successful, it will return the list of packages as below:
```sh
openvino-inference-engine
openvino-inference-engine-dbg
openvino-inference-engine-dev
openvino-inference-engine-python3
openvino-inference-engine-samples
openvino-inference-engine-src
openvino-model-optimizer
openvino-model-optimizer-dbg
openvino-model-optimizer-dev
```
## Additional Resources
.. code-block:: sh
openvino-inference-engine
openvino-inference-engine-dbg
openvino-inference-engine-dev
openvino-inference-engine-python3
openvino-inference-engine-samples
openvino-inference-engine-src
openvino-model-optimizer
openvino-model-optimizer-dbg
openvino-model-optimizer-dev
Additional Resources
####################
- :ref:`Troubleshooting Guide <yocto-install-issues>`
- `Yocto Project <https://docs.yoctoproject.org/>`__ - official documentation webpage
- `BitBake Tool <https://docs.yoctoproject.org/bitbake/>`__
- `Poky <https://git.yoctoproject.org/poky>`__
- `Meta-intel <https://git.yoctoproject.org/meta-intel/tree/README>`__
- `Meta-openembedded <http://cgit.openembedded.org/meta-openembedded/tree/README>`__
- `Meta-clang <https://github.com/kraj/meta-clang/tree/master/#readme>`__
- `OpenVINO Installation Selector Tool <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html>`__
@endsphinxdirective
- [Troubleshooting Guide](@ref yocto-install-issues)
- [Yocto Project](https://docs.yoctoproject.org/) - official documentation webpage
- [BitBake Tool](https://docs.yoctoproject.org/bitbake/)
- [Poky](https://git.yoctoproject.org/poky)
- [Meta-intel](https://git.yoctoproject.org/meta-intel/tree/README)
- [Meta-openembedded](http://cgit.openembedded.org/meta-openembedded/tree/README)
- [Meta-clang](https://github.com/kraj/meta-clang/tree/master/#readme)
- [OpenVINO Installation Selector Tool](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html)