openvino/docs/install_guides/installing-openvino-linux.md

Install Intel® Distribution of OpenVINO™ toolkit for Linux*

NOTES:

• These steps apply to Ubuntu*, CentOS*, and Yocto*.
• If you are using Intel® Distribution of OpenVINO™ toolkit on Windows* OS, see the Installation Guide for Windows*.
• CentOS and Yocto installations will require some modifications that are not covered in this guide.
• An internet connection is required to follow the steps in this guide.
• Intel® System Studio is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to Get Started with Intel® System Studio.

Introduction

The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The Intel® Distribution of OpenVINO™ toolkit includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT).

The Intel® Distribution of OpenVINO™ toolkit for Linux*:

• 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
• Includes optimized calls for computer vision standards including OpenCV* and OpenCL™

Included with the Installation and installed by default:

Component	Description
Model Optimizer	This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine.  Popular frameworks include Caffe*, TensorFlow*, MXNet*, and ONNX*.
Inference Engine	This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.
Intel® Media SDK	Offers access to hardware accelerated video codecs and frame processing
OpenCV	OpenCV* community version compiled for Intel® hardware
Inference Engine Code Samples	A set of simple console applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
[Demo Applications](@ref omz_demos_README)	A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios.
Additional Tools	A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other
[Documentation for Pre-Trained Models ](@ref omz_models_intel_index)	Documentation for the pre-trained models available in the Open Model Zoo repo

System Requirements

Hardware

• 6th to 11th generation Intel® Core™ processors and Intel® Xeon® processors
• Intel® Xeon® processor E family (formerly code named Sandy Bridge, Ivy Bridge, Haswell, and Broadwell)
• 3rd generation Intel® Xeon® Scalable processor (formerly code named Cooper Lake)
• Intel® Xeon® Scalable processor (formerly Skylake and Cascade Lake)
• Intel Atom® processor with support for Intel® Streaming SIMD Extensions 4.1 (Intel® SSE4.1)
• Intel Pentium® processor N4200/5, N3350/5, or N3450/5 with Intel® HD Graphics
• Intel® Neural Compute Stick 2
• Intel® Vision Accelerator Design with Intel® Movidius™ VPUs

Note

: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is no longer supported.

Processor Notes:

• Processor graphics are not included in all processors. See Product Specifications for information about your processor.
• A chipset that supports processor graphics is required for Intel® Xeon® processors.

Operating Systems

• Ubuntu 18.04.x long-term support (LTS), 64-bit
• CentOS 7.6, 64-bit (for target only)
• Yocto Project v3.0, 64-bit (for target only and requires modifications)

Overview

This guide provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ toolkit. Links are provided for each type of compatible hardware including downloads, initialization and configuration steps. The following steps will be covered:

1. Install the Intel® Distribution of OpenVINO™ Toolkit
2. Install External software dependencies
3. Set the OpenVINO™ Environment Variables: Optional Update to .bashrc.
4. Configure the Model Optimizer
5. Run the Verification Scripts to Verify Installation and Compile Samples
6. Steps for Intel® Processor Graphics (GPU)
7. Steps for Intel® Neural Compute Stick 2
8. Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPU
   After installing your Intel® Movidius™ VPU, you will return to this guide to complete OpenVINO™ installation.
9. Run a Sample Application
10. Use the Face Detection Tutorial

Install the Intel® Distribution of OpenVINO™ Toolkit Core Components

Download the Intel® Distribution of OpenVINO™ toolkit package file from Intel® Distribution of OpenVINO™ toolkit for Linux*. Select the Intel® Distribution of OpenVINO™ toolkit for Linux package from the dropdown menu.

1. Open a command prompt terminal window.
2. Change directories to where you downloaded the Intel Distribution of OpenVINO toolkit for Linux* package file.
   If you downloaded the package file to the current user's Downloads directory:

cd ~/Downloads/

By default, the file is saved as l_openvino_toolkit_p_<version>.tgz.

3. Unpack the .tgz file:

tar -xvzf l_openvino_toolkit_p_<version>.tgz

The files are unpacked to the l_openvino_toolkit_p_<version> directory.

4. Go to the l_openvino_toolkit_p_<version> directory:

cd l_openvino_toolkit_p_<version>

If you have a previous version of the Intel Distribution of OpenVINO toolkit installed, rename or delete these two directories:

• ~/inference_engine_samples_build
• ~/openvino_models

Installation Notes:

• Choose an installation option and run the related script as root.
• You can use either a GUI installation wizard or command-line instructions (CLI).
• Screenshots are provided for the GUI, but not for CLI. The following information also applies to CLI and will be helpful to your installation where you will be presented with the same choices and tasks.

5. Choose your installation option:

• Option 1: GUI Installation Wizard:

sudo ./install_GUI.sh

• Option 2: Command-Line Instructions:

sudo ./install.sh

6. Follow the instructions on your screen. Watch for informational messages such as the following in case you must complete additional steps:

7. If you select the default options, the Installation summary GUI screen looks like this:

   • Optional: You can choose Customize to change the installation directory or the components you want to install: When installed as root the default installation directory for the Intel Distribution of OpenVINO is /opt/intel/openvino_<version>/.
     For simplicity, a symbolic link to the latest installation is also created: /opt/intel/openvino_2021/.

   Note

   : The Intel® Media SDK component is always installed in the /opt/intel/mediasdk directory regardless of the OpenVINO installation path chosen.

8. A Complete screen indicates that the core components have been installed:

The first core components are installed. Continue to the next section to install additional dependencies.

Install External Software Dependencies

Note

: If you installed the Intel® Distribution of OpenVINO™ to the non-default install directory, replace /opt/intel with the directory in which you installed the software.

These dependencies are required for:

• Intel-optimized build of OpenCV library
• Deep Learning Inference Engine
• Deep Learning Model Optimizer tools

1. Change to the install_dependencies directory:

cd /opt/intel/openvino_2021/install_dependencies

2. Run a script to download and install the external software dependencies:

sudo -E ./install_openvino_dependencies.sh

The dependencies are installed. Continue to the next section to set your environment variables.

Set the Environment Variables

You must update several environment variables before you can compile and run OpenVINO™ applications. Run the following script to temporarily set your environment variables:

source /opt/intel/openvino_2021/bin/setupvars.sh

Optional: The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:

1. Open the .bashrc file in <user_directory>:

vi <user_directory>/.bashrc

2. Add this line to the end of the file:

source /opt/intel/openvino_2021/bin/setupvars.sh

3. Save and close the file: press the Esc key and type :wq.

4. To test your change, open a new terminal. You will see [setupvars.sh] OpenVINO environment initialized.

The environment variables are set. Continue to the next section to configure the Model Optimizer.

Configure the Model Optimizer

The Model Optimizer is a Python*-based command line tool for importing trained models from popular deep learning frameworks such as Caffe*, TensorFlow*, Apache MXNet*, ONNX* and Kaldi*.

The Model Optimizer is a key component of the Intel Distribution of OpenVINO toolkit. You cannot perform inference on your trained model without running the model through the Model Optimizer. When you run a pre-trained model through the Model Optimizer, your output is an Intermediate Representation (IR) of the network. The Intermediate Representation is a pair of files that describe the whole model:

• .xml: Describes the network topology
• .bin: Contains the weights and biases binary data

For more information about the Model Optimizer, refer to the Model Optimizer Developer Guide. 

Model Optimizer Configuration Steps

You can choose to either configure all supported frameworks at once OR configure one framework at a time. Choose the option that best suits your needs. If you see error messages, make sure you installed all dependencies.

Note

: Since the TensorFlow framework is not officially supported on CentOS*, the Model Optimizer for TensorFlow can't be configured and ran on those systems.

Important

: The Internet access is required to execute the following steps successfully. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.

Option 1: Configure all supported frameworks at the same time

1. Go to the Model Optimizer prerequisites directory:

cd /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites

2. Run the script to configure the Model Optimizer for Caffe, TensorFlow 1.x, MXNet, Kaldi*, and ONNX:

sudo ./install_prerequisites.sh

Option 2: Configure each framework separately

Configure individual frameworks separately ONLY if you did not select Option 1 above.

1. Go to the Model Optimizer prerequisites directory:

cd /opt/intel/openvino_2021/deployment_tools/model_optimizer/install_prerequisites

2. Run the script for your model framework. You can run more than one script:

• For Caffe:

sudo ./install_prerequisites_caffe.sh

• For TensorFlow 1.x:

sudo ./install_prerequisites_tf.sh

• For TensorFlow 2.x:

sudo ./install_prerequisites_tf2.sh

• For MXNet:

sudo ./install_prerequisites_mxnet.sh

• For ONNX:

sudo ./install_prerequisites_onnx.sh

• For Kaldi:

sudo ./install_prerequisites_kaldi.sh

The Model Optimizer is configured for one or more frameworks.

You are ready to compile the samples by running the verification scripts.

Run the Verification Scripts to Verify Installation

Important

: This section is required. In addition to confirming your installation was successful, demo scripts perform other steps, such as setting up your computer to use the Inference Engine samples.

To verify the installation and compile two samples, use the steps below to run the verification applications provided with the product on the CPU.

NOTE: To run the demo applications on Intel® Processor Graphics or Intel® Neural Compute Stick 2 devices, make sure you first completed the additional Steps for Intel® Processor Graphics (GPU) or Steps for Intel® Neural Compute Stick 2.

1. Go to the Inference Engine demo directory:

cd /opt/intel/openvino_2021/deployment_tools/demo

2. Run the Image Classification verification script:

./demo_squeezenet_download_convert_run.sh

This verification script downloads a SqueezeNet model, uses the Model Optimizer to convert the model to the .bin and .xml Intermediate Representation (IR) files. The Inference Engine requires this model conversion so it can use the IR as input and achieve optimum performance on Intel hardware.
This verification script builds the Image Classification Sample Async application and run it with the car.png image located in the demo directory. When the verification script completes, you will have the label and confidence for the top-10 categories:

3. Run the Inference Pipeline verification script:

./demo_security_barrier_camera.sh

This script downloads three pre-trained model IRs, builds the [Security Barrier Camera Demo](@ref omz_demos_security_barrier_camera_demo_README) application, and runs it with the downloaded models and the car_1.bmp image from the demo directory to show an inference pipeline. The verification script uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.

First, an object is identified as a vehicle. This identification is used as input to the next model, which identifies specific vehicle attributes, including the license plate. Finally, the attributes identified as the license plate are used as input to the third model, which recognizes specific characters in the license plate.

When the verification script completes, you will see an image that displays the resulting frame with detections rendered as bounding boxes, and text:

4. Close the image viewer window to complete the verification script.

To learn about the verification scripts, see the README.txt file in /opt/intel/openvino_2021/deployment_tools/demo.

For a description of the Intel Distribution of OpenVINO™ pre-trained object detection and object recognition models, see [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).

You have completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models. To use other hardware, see;

• Steps for Intel® Processor Graphics (GPU)
• Steps for Intel® Neural Compute Stick 2
• Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
  

Steps for Intel® Processor Graphics (GPU)

The steps in this section are required only if you want to enable the toolkit components to use processor graphics (GPU) on your system.

1. Go to the install_dependencies directory:

cd /opt/intel/openvino_2021/install_dependencies/

2. Enter the super user mode:

sudo -E su

3. Install the Intel® Graphics Compute Runtime for OpenCL™ driver components required to use the GPU plugin and write custom layers for Intel® Integrated Graphics. Run the installation script:

./install_NEO_OCL_driver.sh

The drivers are not included in the package and the script downloads them. Make sure you have the internet connection for this step.

The script compares the driver version on the system to the current version. If the driver version on the system is higher or equal to the current version, the script does not install a new driver. If the version of the driver is lower than the current version, the script uninstalls the lower and installs the current version with your permission: Higher hardware versions require a higher driver version, namely 20.35 instead of 19.41. If the script fails to uninstall the driver, uninstall it manually.
During the script execution, you may see the following command line output:

• Add OpenCL user to video group
  Ignore this suggestion and continue.

4. Optional Install header files to allow compiling a new code. You can find the header files at Khronos OpenCL™ API Headers.

Steps for Intel® Neural Compute Stick 2

These steps are only required if you want to perform inference on Intel® Movidius™ NCS powered by the Intel® Movidius™ Myriad™ 2 VPU or Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X VPU. See also the Get Started page for Intel® Neural Compute Stick 2:

1. Add the current Linux user to the users group:

sudo usermod -a -G users "$(whoami)"

Log out and log in for it to take effect.

2. To perform inference on Intel® Neural Compute Stick 2, install the USB rules as follows:

sudo cp /opt/intel/openvino_2021/inference_engine/external/97-myriad-usbboot.rules /etc/udev/rules.d/

sudo udevadm control --reload-rules

sudo udevadm trigger

sudo ldconfig

Note

: You may need to reboot your machine for this to take effect.

Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs

To install and configure your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, see the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs Configuration Guide.

Note

: After installing your Intel® Movidius™ VPU, you will return to this guide to complete the Intel® Distribution of OpenVINO™ installation.

After configuration is done, you are ready to run the verification scripts with the HDDL Plugin for your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs:

1. Go to the Inference Engine demo directory:

cd /opt/intel/openvino_2021/deployment_tools/demo

2. Run the Image Classification verification script. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.

./demo_squeezenet_download_convert_run.sh -d HDDL

3. Run the Inference Pipeline verification script:

./demo_security_barrier_camera.sh -d HDDL

Run a Sample Application

Important

: This section requires that you have Run the Verification Scripts to Verify Installation. This script builds the Image Classification sample application and downloads and converts the required Caffe* Squeezenet model to an IR.

In this section you will run the Image Classification sample application, with the Caffe* Squeezenet1.1 model on three types of Intel® hardware: CPU, GPU and VPUs.

Image Classification sample application binary file was automatically built and the FP16 model IR files are created when you Ran the Image Classification Verification Script.

The Image Classification sample application binary file located in the /home/<user>/inference_engine_samples_build/intel64/Release directory. The Caffe* Squeezenet model IR files (.bin and .xml) are located in the /home/<user>/openvino_models/ir/public/squeezenet1.1/FP16/ directory.

Note

: If you installed the Intel® Distribution of OpenVINO™ to the non-default install directory, replace /opt/intel with the directory in which you installed the software.

To run the sample application:

1. Set up environment variables:

source /opt/intel/openvino_2021/bin/setupvars.sh

2. Go to the samples build directory:

cd ~/inference_engine_samples_build/intel64/Release

3. Run the sample executable with specifying the car.png file from the demo directory as an input image, the IR of your FP16 model and a plugin for a hardware device to perform inference on.

Note

: Running the sample application on hardware other than CPU requires performing additional hardware configuration steps.

• For CPU:

./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d CPU

• For GPU:

./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d GPU

• For MYRIAD:

Note

: Running inference on Intel® Neural Compute Stick 2 with the MYRIAD plugin requires performing additional hardware configuration steps.

./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d MYRIAD

• For HDDL:

Note

: Running inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs with the HDDL plugin requires performing additional hardware configuration steps

./classification_sample_async -i /opt/intel/openvino_2021/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d HDDL

For information on Sample Applications, see the Inference Engine Samples Overview.

Congratulations, you have finished the installation of the Intel® Distribution of OpenVINO™ toolkit for Linux*. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and other resources are provided below.

Hello World Face Detection Tutorial

See the OpenVINO™ Hello World Face Detection Exercise.

Troubleshooting

PRC developers might encounter pip installation related issues during OpenVINO™ installation. To resolve the issues, you may use one of the following options at your discretion:

• Add the download source with -i parameter in the pip command. For example:

pip install numpy.py -i https://mirrors.aliyun.com/pypi/simple/

Use the --trusted-host parameter if the URL above is http instead of https.

• Modify or create ~/.pip/pip.conf file to change the default download source with the content below:

[global]
index-url = http://mirrors.aliyun.com/pypi/simple/
[install]
trusted-host = mirrors.aliyun.com

Additional Resources

• Intel® Distribution of OpenVINO™ toolkit home page: https://software.intel.com/en-us/openvino-toolkit
• OpenVINO™ toolkit online documentation: https://docs.openvinotoolkit.org
• Model Optimizer Developer Guide.
• Inference Engine Developer Guide.
• For more information on Sample Applications, see the Inference Engine Samples Overview.
• For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
• For information on Inference Engine Tutorials, see the Inference Tutorials
• For IoT Libraries and Code Samples see the Intel® IoT Developer Kit.

To learn more about converting models, go to:

• Convert Your Caffe* Model
• Convert Your TensorFlow* Model
• Convert Your MXNet* Model
• Convert Your ONNX* Model