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updating apt, yum, conda installation for 22/1 (#10219) 

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# Install Intel® Distribution of OpenVINO™ toolkit for Linux* Using APT Repository {#openvino_docs_install_guides_installing_openvino_apt}
# Install Intel® Distribution of OpenVINO™ Toolkit for Linux Using APT Repository {#openvino_docs_install_guides_installing_openvino_apt}
This guide provides installation steps for Intel® Distribution of OpenVINO™ toolkit for Linux* distributed through the APT repository.
This guide provides installation steps for Intel® Distribution of OpenVINO™ toolkit for Linux distributed through the APT repository.
> **IMPORTANT**: By downloading and using this container and the included software, you agree to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf). Please, review the content inside the `<openvino_install_root>/licensing` folder for more details.
From 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI. If you want to develop or optimize your models with OpenVINO, see [Install OpenVINO Development Tools](installing-model-dev-tools.md) for detailed steps.
> **NOTE**: Intel® Graphics Compute Runtime for OpenCL™ is not a part of OpenVINO™ APT distribution. You can install it from the [Intel® Graphics Compute Runtime for OpenCL™ GitHub repo](https://github.com/intel/compute-runtime).
> **IMPORTANT**: By downloading and using this container and the included software, you agree to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf). Please review the content inside the `<INSTALL_DIR>/licensing` folder for more details.
## System Requirements
The complete list of supported hardware is available in the [Release Notes](https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html#inpage-nav-8).
The complete list of supported hardware is available in the [Release Notes](https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html).
**Operating Systems**
- Ubuntu 18.04.x long-term support (LTS), 64-bit
- Ubuntu 20.04.0 long-term support (LTS), 64-bit
- Ubuntu 18.04 long-term support (LTS), 64-bit
- Ubuntu 20.04 long-term support (LTS), 64-bit
## Included with Runtime Package
## Install OpenVINO Runtime
The following components are installed with the OpenVINO runtime package:
### Step 1: Set Up the OpenVINO Toolkit APT Repository
| Component | Description|
|-----------|------------|
| [OpenVINO™ Runtime](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md)| The engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
| [OpenCV*](https://docs.opencv.org/master/) | OpenCV* community version compiled for Intel® hardware. |
| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
1. Install the GPG key for the repository
## Included with Developer Package
a. Download the [GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB](https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB). You can also use the following command:
```sh
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
```
b. Add this key to the system keyring:
```sh
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
```
> **NOTE**: You might need to install GnuPG: `sudo apt-get install gnupg`
2. Add the repository via the following command:
The following components are installed with the OpenVINO developer package:
* On Ubuntu 18
```sh
echo "deb https://apt.repos.intel.com/openvino/2022/bionic all main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2022.list
```
* On Ubuntu 20
```sh
echo "deb https://apt.repos.intel.com/openvino/2022/focal all main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2022.list
```
| Component | Description|
|-----------|------------|
| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*. |
| [OpenVINO™ Runtime](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) | The engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications.|
| [OpenCV*](https://docs.opencv.org/master/) | OpenCV\* community version compiled for Intel® hardware |
| [Sample Applications](../OV_Runtime_UG/Samples_Overview.md) | A set of simple console applications demonstrating how to use the Inference Engine in your applications. |
| [Demo Applications](@ref omz_demos) | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use cases. |
| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other |
| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo). |
| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer\*, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
3. Update the list of packages via the update command:
```sh
sudo apt update
```
4. Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components:
```sh
apt-cache search openvino
```
### Step 2: Install OpenVINO Runtime Using the APT Package Manager
## Install Packages
OpenVINO will be installed in: `/opt/intel/openvino_<VERSION>.<UPDATE>.<PATCH>`
A symlink will be created: `/opt/intel/openvino_<VERSION>`
### Set up the OpenVINO™ Toolkit APT Repository
#### Install the GPG key for the Repository
1. Download the public key from [https://apt.repos.intel.com/openvino/2021/GPG-PUB-KEY-INTEL-OPENVINO-2021](https://apt.repos.intel.com/openvino/2021/GPG-PUB-KEY-INTEL-OPENVINO-2021) and save it to a file.
2. Add this key to the system keyring:
```sh
sudo apt-key add <PATH_TO_DOWNLOADED_GPG_KEY>
```
> **NOTE**: You might need to install GnuPG: `sudo apt-get install gnupg`
3. Check the list of APT keys running the following command:
```sh
sudo apt-key list
```
#### Add the Repository
#### To Install the Latest Version
Run the following command:
```sh
echo "deb https://apt.repos.intel.com/openvino/2021 all main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2021.list
sudo apt install openvino
```
#### Update the List of Packages
Run the `update` command:
```sh
sudo apt update
```
#### Verify that the APT Repository is Properly Set Up
Run the `apt-cache` command to see a list of all available OpenVINO packages and components:
```sh
apt-cache search openvino
```
See the example commands below:
* **Runtime Packages**
On Ubuntu 18.04:
```sh
sudo apt-cache search intel-openvino-runtime-ubuntu18
```
On Ubuntu 20.04:
```sh
sudo apt-cache search intel-openvino-runtime-ubuntu20
```
* **Developer Packages**
On Ubuntu 18.04:
```sh
sudo apt-cache search intel-openvino-dev-ubuntu18
```
On Ubuntu 20.04:
```sh
sudo apt-cache search intel-openvino-dev-ubuntu20
```
### Install Runtime or Developer Packages using the APT Package Manager
Intel® OpenVINO™ Toolkit will be installed in: `/opt/intel/openvino_<VERSION>.<UPDATE>.<BUILD_NUM>`
A symlink will be created: `/opt/intel/openvino_<VERSION>`
#### To Install a Specific Version
1. Get a list of OpenVINO packages available for installation:
```sh
sudo apt-cache search intel-openvino-runtime-ubuntu18
```
2. Install a specific version of an OpenVINO package:
```sh
sudo apt install intel-openvino-<PACKAGE_TYPE>-ubuntu<OS_VERSION>-<VERSION>.<UPDATE>.<BUILD_NUM>
```
See the example commands below:
* **Runtime Package**<br>
On Ubuntu 18.04:
```sh
sudo apt install intel-openvino-runtime-ubuntu18-2021.1.105
```
On Ubuntu 20.04:
```sh
sudo apt install intel-openvino-runtime-ubuntu20-2021.1.105
```
* **Developer Package**<br>
On Ubuntu 18.04:
```sh
sudo apt install intel-openvino-dev-ubuntu18-2021.1.105
```
On Ubuntu 20.04:
```sh
sudo apt install intel-openvino-dev-ubuntu20-2021.1.105
```
#### To check for Installed Packages and Versions
1. Get a list of OpenVINO packages available for installation:
```sh
sudo apt-cache search openvino
```
2. Install a specific version of an OpenVINO package:
```sh
sudo apt install openvino-<VERSION>.<UPDATE>.<PATCH>
```
For example:
```sh
sudo apt install openvino-2022.1.0
```
To get a list of installed OpenVINO packages:
#### To Check for Installed Packages and Versions
Run the following command:
```sh
apt list --installed | grep openvino
```
#### To Uninstall a Specific Version
#### To Uninstall the Latest Version
To uninstall a specific package:
Run the following command:
```sh
sudo apt autoremove intel-openvino-<PACKAGE_TYPE>-ubuntu<OS_VERSION>-<VERSION>.<UPDATE>.<BUILD_NUM>
sudo apt autoremove openvino
```
#### To Uninstall a Specific Version
**Additional Resources**
Run the following command:
```sh
sudo apt autoremove openvino-<VERSION>.<UPDATE>.<PATCH>
```
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
- OpenVINO™ toolkit online documentation: [https://docs.openvino.ai](https://docs.openvino.ai).
### Step 3 (Optional): Install OpenCV from APT
OpenCV is necessary to run C++ demos from Open Model Zoo. Some C++ samples and demos also use OpenCV as a dependency. OpenVINO provides a package to install OpenCV from APT:
#### To Install the Latest Version of OpenCV
Run the following command:
```sh
sudo apt install openvino-opencv
```
#### To Install a Specific Version of OpenCV
Run the following command:
```sh
sudo apt install openvino-opencv-<VERSION>.<UPDATE>.<PATCH>
```
### Step 4 (Optional): Install Software Dependencies
After you have installed OpenVINO Runtime, if you decided to [install OpenVINO Development Tools](installing-model-dev-tools.md), make sure that you install external software dependencies first.
Refer to <a href="installing-openvino-linux.md#install-external-dependencies">Install External Software Dependencies</a> for detailed steps.
## Configurations for Non-CPU Devices
If you are using Intel® Processor Graphics, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs or Intel® Neural Compute Stick 2, please follow the configuration steps in [Configurations for GPU](configurations-for-intel-gpu.md), [Configurations for VPU](installing-openvino-config-ivad-vpu.md) or [Configurations for NCS2](configurations-for-ncs2.md) accordingly.
## Additional Resources
- Intel® Distribution of OpenVINO™ toolkit home page: <https://software.intel.com/en-us/openvino-toolkit>.
- OpenVINO™ toolkit online documentation: <https://docs.openvino.ai/>.
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [OpenVINO™ Runtime User Guide](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md).
- [OpenVINO Runtime User Guide](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../OV_Runtime_UG/Samples_Overview.md).
- For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

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# Install Intel® Distribution of OpenVINO™ toolkit from Anaconda* Cloud {#openvino_docs_install_guides_installing_openvino_conda}
# Install Intel® Distribution of OpenVINO™ toolkit from Anaconda Cloud {#openvino_docs_install_guides_installing_openvino_conda}
This guide provides installation steps for Intel® Distribution of OpenVINO™ toolkit distributed through the Anaconda* Cloud.
This guide provides installation steps for Intel® Distribution of OpenVINO™ toolkit for Linux distributed through the Anaconda Cloud.
> **NOTE**: Only runtime packages are available from Anaconda* Cloud.
## Introduction
OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
The Intel® Distribution of OpenVINO™ toolkit\*:
- Enables CNN-based deep learning inference on the edge
- Supports heterogeneous execution across Intel® CPU, Intel® Integrated Graphics, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
The **runtime package** includes the following components installed by default:
| Component | Description |
|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [OpenVINO™ Runtime](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
From 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI. If you want to develop or optimize your models with OpenVINO, see [Install OpenVINO Development Tools](installing-model-dev-tools.md) for detailed steps.
## System Requirements
**Software**
- [Anaconda* distribution](https://www.anaconda.com/products/individual/)
- [Anaconda distribution](https://www.anaconda.com/products/individual/)
**Operating Systems**
| Supported Operating System | [Python* Version (64-bit)](https://www.python.org/) |
| Supported Operating System | [Python Version (64-bit)](https://www.python.org/) |
| :------------------------------------------------------------| :---------------------------------------------------|
| Ubuntu* 18.04 long-term support (LTS), 64-bit | 3.6, 3.7 |
| Ubuntu* 20.04 long-term support (LTS), 64-bit | 3.6, 3.7 |
| CentOS* 7.6, 64-bit | 3.6, 3.7 |
| macOS* 10.15.x | 3.6, 3.7 |
| Windows 10*, 64-bit | 3.6, 3.7 |
| Ubuntu 18.04 long-term support (LTS), 64-bit | 3.6, 3.7, 3.8, 3.9 |
| Ubuntu 20.04 long-term support (LTS), 64-bit | 3.6, 3.7, 3.8, 3.9 |
| Red Hat Enterprise Linux 8, 64-bit | 3.6, 3.7, 3.8, 3.9 |
| macOS 10.15 | 3.6, 3.7, 3.8, 3.9 |
| Windows 10, 64-bit | 3.6, 3.7, 3.8, 3.9 |
## Install the Runtime Package using the Anaconda* Package Manager
## Install OpenVINO Runtime Using the Anaconda Package Manager
1. Set up the Anaconda* environment: 
1. Set up the Anaconda environment (taking Python 3.7 for example): 
```sh
conda create --name py37 python=3.7
```
```sh
conda activate py37
```
2. Update Anaconda environment to the latest version:
```sh
conda update --all
```
3. Install pre-requisites:
```sh
conda install numpy
```
4. Install the Intel® Distribution of OpenVINO™ Toolkit:
3. Install the Intel® Distribution of OpenVINO™ toolkit:
- Ubuntu* 20.04
```sh
conda install openvino-ie4py-ubuntu20 -c intel
@ -60,28 +40,28 @@ The **runtime package** includes the following components installed by default:
```sh
conda install openvino-ie4py-ubuntu18 -c intel
```
- CentOS* 7.6
- Red Hat Enterprise Linux 8, 64-bit
```sh
conda install openvino-ie4py-centos7 -c intel
conda install openvino-ie4py-rhel8 -c intel
```
- Windows* 10 and macOS*
- Windows 10 and macOS
```sh
conda install openvino-ie4py -c intel
```
5. Verify the package is installed:
4. Verify the package is installed:
```sh
python -c "from openvino.inference_engine import IECore"
python -c "from openvino.runtime import Core"
```
If installation was successful, you will not see any error messages (no console output).
Now you can start developing your application.
## Additional Resources
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
- OpenVINO™ toolkit online documentation: [https://docs.openvino.ai](https://docs.openvino.ai).
- Intel® Distribution of OpenVINO™ toolkit home page: <https://software.intel.com/en-us/openvino-toolkit>.
- OpenVINO™ toolkit online documentation: <https://docs.openvinotoolkit.ai>.
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [OpenVINO™ Runtime User Guide](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md).
- [OpenVINO Runtime User Guide](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../OV_Runtime_UG/Samples_Overview.md).
- Intel® Distribution of OpenVINO™ toolkit Anaconda* home page: [https://anaconda.org/intel/openvino-ie4py](https://anaconda.org/intel/openvino-ie4py)
- Intel® Distribution of OpenVINO™ toolkit Anaconda home page: [https://anaconda.org/intel/openvino-ie4py](https://anaconda.org/intel/openvino-ie4py)

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# Install and Configure Intel® Distribution of OpenVINO™ toolkit for Linux {#openvino_docs_install_guides_installing_openvino_linux}
# Install and Configure Intel® Distribution of OpenVINO™ Toolkit for Linux {#openvino_docs_install_guides_installing_openvino_linux}
> **NOTE**: Since the OpenVINO™ 2022.1 release, the following development tools: Model Optimizer, Post-Training Optimization Tool, Model Downloader and other Open Model Zoo tools, Accuracy Checker, and Annotation Converter are not part of the installer. These tools are now only available on [pypi.org](https://pypi.org/project/openvino-dev/).
@ -7,8 +7,8 @@
@sphinxdirective
.. tab:: Operating Systems
* Ubuntu 18.04.x long-term support (LTS), 64-bit
* Ubuntu 20.04.x long-term support (LTS), 64-bit
* Ubuntu 18.04 long-term support (LTS), 64-bit
* Ubuntu 20.04 long-term support (LTS), 64-bit
.. note::
Since the OpenVINO™ 2022.1 release, CentOS 7.6, 64-bit is not longer supported.

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# Install Intel® Distribution of OpenVINO™ toolkit for Linux* Using YUM Repository {#openvino_docs_install_guides_installing_openvino_yum}
# Install Intel® Distribution of OpenVINO™ Toolkit for Linux Using YUM Repository {#openvino_docs_install_guides_installing_openvino_yum}
This guide provides installation steps for the Intel® Distribution of OpenVINO™ toolkit for Linux* distributed through the YUM repository.
This guide provides installation steps for Intel® Distribution of OpenVINO™ toolkit for Linux distributed through the YUM repository.
> **IMPORTANT**: By downloading and using this container and the included software, you agree to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf). Please, review the content inside the `<openvino_install_root>/licensing` folder for more details.
From 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI. If you want to develop or optimize your models with OpenVINO, see [Install OpenVINO Development Tools](installing-model-dev-tools.md) for detailed steps.
> **NOTE**: Intel® Graphics Compute Runtime for OpenCL™ is not a part of OpenVINO™ YUM distribution. You can install it from the [Intel® Graphics Compute Runtime for OpenCL™ GitHub repo](https://github.com/intel/compute-runtime).
> **NOTE**: Only runtime packages are available via the YUM repository.
> **IMPORTANT**: By downloading and using this container and the included software, you agree to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf). Please review the content inside the `<INSTALL_DIR>/licensing` folder for more details.
## System Requirements
The complete list of supported hardware is available in the [Release Notes](https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html#inpage-nav-8).
The complete list of supported hardware is available in the [Release Notes](https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html).
**Operating Systems**
**Operating systems**
- CentOS 7.6, 64-bit
- Red Hat Enterprise Linux 8, 64-bit
## Included with Runtime Package
## Install OpenVINO Runtime
The following components are installed with the OpenVINO runtime package:
### Step 1: Set Up the Repository
| Component | Description|
|-----------|------------|
| [OpenVINO™ Runtime](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md)| The engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
| [OpenCV*](https://docs.opencv.org/master/) | OpenCV* community version compiled for Intel® hardware. |
| Deep Learning Stream (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
## Install Packages
## Set up the Repository
> **NOTE**: You must be logged in as root to set up and install the repository.
<br>
Configure YUM with the OpenVINO repository to install OpenVINO. You have two options for this, using the `yum-config-manager` or manually by creating a text file and pointing YUM to the file.
* **OPTION 1:** Import the `.repo` file using the `yum-config-manager`:
1. `yum-utils` must be installed on your system. If it’s not currently installed, run the command:
```sh
sudo yum install yum-utils
1. Create the YUM repo file in the `/tmp` directory as a normal user:
```
2. Add repository using the `yum-config-manager`:
```sh
sudo yum-config-manager --add-repo https://yum.repos.intel.com/openvino/2021/setup/intel-openvino-2021.repo
```
3. Import the gpg public key for the repository:
```sh
sudo rpm --import https://yum.repos.intel.com/openvino/2021/setup/RPM-GPG-KEY-INTEL-OPENVINO-2021
```
* **OPTION 2:** Create the repository file manually:
1. Create the YUM repo file in the /tmp directory as a normal user:
```sh
tee > /tmp/openvino-2021.repo << EOF
[intel-openvino-2021]
name=Intel(R) Distribution of OpenVINO 2021
baseurl=https://yum.repos.intel.com/openvino/2021
tee > /tmp/openvino-2022.repo << EOF
[OpenVINO]
name=Intel(R) Distribution of OpenVINO 2022
baseurl=https://yum.repos.intel.com/openvino/2022
enabled=1
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-INTEL-OPENVINO-2021
repo_gpgcheck=1
gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
EOF
```
2. Move the newly created openvino-2021.repo file to the YUM configuration directory /etc/yum.repos.d:
```sh
sudo mv /tmp/openvino-2021.repo /etc/yum.repos.d
```
3. Import the gpg public key for the repository:
2. Move the new openvino-2022.repo file to the YUM configuration directory `/etc/yum.repos.d`:
```sh
sudo rpm --import https://yum.repos.intel.com/openvino/2021/setup/RPM-GPG-KEY-INTEL-OPENVINO-2021
sudo mv /tmp/openvino-2022.repo /etc/yum.repos.d
```
3. Verify that the new repo is properly setup by running the following command:
```sh
yum repolist | grep -i openvino
```
You will see the available list of packages.
### Verify that the new repo is properly setup
Run the following command:
```sh
yum repolist | grep -i openvino
To list available OpenVINO packages, use the following command:
```
yum list 'openvino*'
```
Results:
```sh
intel-openvino-2021 Intel(R) Distribution of OpenVINO 2021
```
### To list available OpenVINO packages
Use the following command:
```sh
yum list intel-openvino*
```
### Step 2: Install OpenVINO Runtime Using the YUM Package Manager
---
## Install Runtime Packages Using the YUM Package Manager
Intel® Distribution of OpenVINO™ toolkit will be installed in: `/opt/intel/openvino_<VERSION>.<UPDATE>.<PATCH>`
Intel® OpenVINO will be installed in: `/opt/intel/openvino_<VERSION>.<UPDATE>.<BUILD_NUM>`
<br>
A symlink will be created: `/opt/intel/openvino_<VERSION>`
---
You can select one of the following procedures according to your need:
### To install the latest version
To install the full runtime version of the OpenVINO package:
#### To Install the Latest Version
Run the following command:
```sh
sudo yum install intel-openvino-runtime-centos7
sudo yum install openvino
```
---
#### To Install a Specific Version
### To install a specific version
To install the full runtime version of the OpenVINO package:
Run the following command:
```sh
sudo yum install intel-openvino-runtime-centos7-<VERSION>.<UPDATE>.<BUILD_NUM>
sudo yum install openvino-<VERSION>.<UPDATE>.<PATCH>
```
For example:
```sh
sudo yum install intel-openvino-runtime-centos7-2021.3.394
```
---
### To check for installed packages and version
To check a specific version of an OpenVINO package:
```sh
yum list installed intel-openvino*
sudo yum install openvino-2022.1.0
```
---
#### To Check for Installed Packages and Version
### To Uninstall a specific version
To uninstall a specific full runtime package:
Run the following command:
```sh
sudo yum autoremove intel-openvino-runtime-centos<OS_VERSION>-<VERSION>.<UPDATE>.<BUILD_NUM>
yum list installed 'openvino*'
```
**Additional Resources**
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvino.ai](https://docs.openvino.ai)
#### To Uninstall the Latest Version
Run the following command:
```sh
sudo yum autoremove openvino
```
#### To Uninstall a Specific Version
Run the following command:
```sh
sudo yum autoremove openvino-<VERSION>.<UPDATE>.<PATCH>
```
### Step 3 (Optional): Install OpenCV from YUM
OpenCV is necessary to run C++ demos from Open Model Zoo. Some C++ samples and demos also use OpenCV as a dependency. OpenVINO provides a package to install OpenCV from YUM:
#### To Install the Latest Version of OpenCV
Run the following command:
```sh
sudo yum install openvino-opencv
```
#### To Install a Specific Version of OpenCV
Run the following command:
```sh
sudo yum install openvino-opencv-<VERSION>.<UPDATE>.<PATCH>
```
### Step 4 (Optional): Install Software Dependencies
After you have installed OpenVINO Runtime, if you decided to [install OpenVINO Model Development Tools](installing-model-dev-tools.md), make sure that you install external software dependencies first.
Refer to <a href="#install-external-dependencies">Install External Software Dependencies</a> for detailed steps.
## Configurations for Non-CPU Devices
If you are using Intel® Processor Graphics, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs or Intel® Neural Compute Stick 2, please follow the configuration steps in [Configurations for GPU](configurations-for-intel-gpu.md), [Configurations for VPU](installing-openvino-config-ivad-vpu.md) or [Configurations for NCS2](configurations-for-ncs2.md) accordingly.
## Additional Resources
- Intel® Distribution of OpenVINO™ toolkit home page: <https://software.intel.com/en-us/openvino-toolkit>.
- OpenVINO™ toolkit online documentation: <https://docs.openvino.ai/>.
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [OpenVINO™ Runtime User Guide](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide.md).
- [OpenVINO Runtime User Guide](../OV_Runtime_UG/OpenVINO_Runtime_User_Guide).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../OV_Runtime_UG/Samples_Overview.md).
- For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).