Install the Intel® Distribution of OpenVINO™ toolkit for Linux*

Install the Intel® Distribution of OpenVINO™ toolkit for Linux*

https://software.intel.com/en-us/articles/OpenVINO-Install-Linux

Intel Movidius
https://www.movidius.com/

Intel® Neural Compute Stick 2
https://software.intel.com/en-us/neural-compute-stick

Intel® Distribution of OpenVINO™ Toolkit
https://software.intel.com/en-us/openvino-toolkit

Intel® Distribution of OpenVINO™ Toolkit - Documentation
https://software.intel.com/en-us/openvino-toolkit/documentation/featured

Introduction

Important:

• All steps in this guide are required unless otherwise stated.
• In addition to the downloaded package, you must install dependencies and complete configuration steps.

Your installation is complete when these are all completed:

1. Installed the external software dependencies.
2. Installed the Intel® Distribution of OpenVINO™ toolkit core components.
3. Set the Intel Distribution of OpenVINO toolkit environment variables and (optional) update .bashrc.
4. Configured the Model Optimizer.
5. Optional. Installed software or drivers for:
   • Integrated Graphics
   • Intel® Movidius™ Neural Compute Stick (NCS) and Intel® Neural Compute Stick 2
6. Ran two demos.

About the Intel® Distribution of OpenVINO™ toolkit

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 a CPU, Integrated Graphics and Intel® Movidius™ Neural Compute Stick (NCS) and and Intel® Neural Compute Stick 2
• 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*, OpenCL™, and OpenVX*

Included with the Installation

The following components are installed by default:

System requirements

This guide covers the Linux version of the Intel Distribution of OpenVINO toolkit that does not include FPGA support. For the toolkit that includes FPGA support, see Installing the Intel Distribution of OpenVINO toolkit for Linux with FPGA Support.

Hardware

• 6th-8th Generation Intel® Core™
• Intel® Xeon® v5 family
• Intel® Xeon® v6 family
• Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics
• Intel® Movidius™ NCS
• Intel® Neural Compute Stick 2

Processor Notes

• Processor graphics are not included in all processors. See Product specifications, processors for information about your processor.
• A chipset that supports processor graphics is required if you’re using an Intel Xeon processor. See Product specifications, chipsets for information about your chipset.

https://ark.intel.com/

Operating Systems
Ubuntu* 16.04 long-term support (LTS), 64-bit
CentOS* 7.4 or higher, 64-bit
Yocto Project* Poky Jethro* v2.0.3, 64-bit (for target only)

Installation Steps

This guide assumes you downloaded the Intel Distribution of OpenVINO toolkit for Linux* OS. If you do not have a copy of the toolkit package file, download the latest version and then return to this guide to proceed with the installation.

Install external software dependencies

(1) Open the Terminal* or your preferred console application and go to the directory in which you downloaded the Intel® Distribution of OpenVINO™ toolkit. This document assumes this is your ~/Downloads directory. If not, replace ~/Downloads with the directory where the file is located:

cd ~/Downloads/

By default, the package file is saved as l_openvino_toolkit_p_.tgz

(2) Unpack the .tgz file you downloaded:

tar -zxf l_openvino_toolkit_p_<version>.tgz

The files are unpacked to a directory named l_openvino_toolkit_p_

(3) Go to the l_openvino_toolkit_p_ directory:

cd l_openvino_toolkit_p_<version>

(4) Run a script to automatically download and install external software dependencies. These dependencies are required for the Intel-optimized version of OpenCV, the Deep Learning Inference Engine, and the Deep Learning Model Optimizer tools. Install these before the Intel Distribution of OpenVINO toolkit.

./install_cv_sdk_dependencies.sh

As an option, you can install all the dependencies manually instead of running install_cv_sdk_dependencies.sh. In this case, use the list of dependencies at System Requirements.
https://software.intel.com/en-us/openvino-toolkit/documentation/system-requirements

Note: The Model Optimizer has additional prerequisites that are addressed later in this document.

The dependencies are installed. Continue to the next section to install the Intel Distribution of OpenVINO toolkit core components.

strong@foreverstrong:~$ cd ~/Downloads/
strong@foreverstrong:~/Downloads$ ll
total 1412052
drwxr-xr-x  2 strong strong       4096 11月 20 09:05 ./
drwxr-xr-x 18 strong strong       4096 11月 20 08:48 ../
-rwxrwxrwx  1 strong strong 1445927668 11月 19 22:40 l_openvino_toolkit_p_2018.4.420.tgz*
strong@foreverstrong:~/Downloads$ 
strong@foreverstrong:~/Downloads$ tar xvf l_openvino_toolkit_p_2018.4.420.tgz 
......
strong@foreverstrong:~/Downloads$ 


strong@foreverstrong:~/Downloads$ cd l_openvino_toolkit_p_2018.4.420/
strong@foreverstrong:~/Downloads/l_openvino_toolkit_p_2018.4.420$ ll
total 100
drwxr-xr-x 4 strong strong  4096 11月 10 03:11 ./
drwxr-xr-x 3 strong strong  4096 11月 20 09:06 ../
-rw-r--r-- 1 strong strong 42983 11月  9 23:21 EULA.txt
-rwxr-xr-x 1 strong strong  4547 11月  9 23:21 install_cv_sdk_dependencies.sh*
-rwxr-xr-x 1 strong strong   643 11月 10 03:10 install_GUI.sh*
-rwxr-xr-x 1 strong strong 19118 11月 10 03:10 install.sh*
drwxr-xr-x 6 strong strong  4096 11月 10 03:11 pset/
-rwxr-x--- 1 strong strong   975 11月 10 02:14 PUBLIC_KEY.PUB*
drwxr-xr-x 2 strong strong  4096 11月 10 03:11 rpm/
-rw-r--r-- 1 strong strong  2285 11月 10 03:10 silent.cfg
strong@foreverstrong:~/Downloads/l_openvino_toolkit_p_2018.4.420$ ./install_cv_sdk_dependencies.sh 
ERROR: this script must be run as root to install 3rd party packages.
Please try again with sudo -E ./install_cv_sdk_dependencies.sh, or as root.
strong@foreverstrong:~/Downloads/l_openvino_toolkit_p_2018.4.420$

 
strong@foreverstrong:~/Downloads/l_openvino_toolkit_p_2018.4.420$ sudo ./install_cv_sdk_dependencies.sh 
[sudo] password for strong: 

This script installs the following OpenVINO 3rd-party dependencies:
1. FFmpeg and GStreamer libraries required for OpenCV and Inference Engine
2. libusb library required for Myriad plugin for Inference Engine
3. build dependencies for OpenVINO samples

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Reading package lists... Done
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23 packages can be upgraded. Run 'apt list --upgradable' to see them.
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build-essential is already the newest version (12.1ubuntu2).
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Use 'sudo apt autoremove' to remove it.
The following additional packages will be installed:
  autotools-dev cmake-data debhelper dh-strip-nondeterminism gir1.2-gtk-2.0 libatk1.0-dev libavutil-dev libcairo-script-interpreter2
  libexpat1-dev libfile-stripnondeterminism-perl libfontconfig1-dev libfreetype6-dev libgdk-pixbuf2.0-dev libharfbuzz-dev
  libharfbuzz-gobject0 libice-dev libjsoncpp1 libmail-sendmail-perl libpcre3-dev libpcre32-3 libpcrecpp0v5 libpixman-1-dev
  libpthread-stubs0-dev libset-scalar-perl libsm-dev libswresample-dev libsys-hostname-long-perl libusb-1.0-doc libva-drm1 libva-egl1
  libva-glx1 libva-tpi1 libva-wayland1 libva-x11-1 libwayland-bin libwayland-dev libx11-dev libx11-doc libxau-dev libxcb-render0-dev
  libxcb-shm0-dev libxcb1-dev libxcomposite-dev libxcursor-dev libxdamage-dev libxdmcp-dev libxext-dev libxfixes-dev libxft-dev libxi-dev
  libxinerama-dev libxml2-utils libxrandr-dev libxrender-dev po-debconf x11proto-composite-dev x11proto-core-dev x11proto-damage-dev
  x11proto-fixes-dev x11proto-input-dev x11proto-kb-dev x11proto-randr-dev x11proto-render-dev x11proto-xext-dev x11proto-xinerama-dev
  xorg-sgml-doctools xtrans-dev zlib1g-dev
Suggested packages:
  codeblocks eclipse ninja-build dh-make libcairo2-doc libglib2.0-doc libgtk2.0-doc libice-doc libpango1.0-doc libsm-doc libxcb-doc
  libxext-doc libmail-box-perl
The following NEW packages will be installed:
  autotools-dev cmake cmake-data debhelper dh-strip-nondeterminism gir1.2-gtk-2.0 libatk1.0-dev libavcodec-dev libavformat-dev libavutil-dev
  libcairo-script-interpreter2 libcairo2-dev libdrm-dev libexpat1-dev libfile-stripnondeterminism-perl libfontconfig1-dev libfreetype6-dev
  libgdk-pixbuf2.0-dev libglib2.0-dev libgtk2.0-dev libharfbuzz-dev libharfbuzz-gobject0 libice-dev libjsoncpp1 libmail-sendmail-perl
  libpango1.0-dev libpcre3-dev libpcre32-3 libpcrecpp0v5 libpixman-1-dev libpng12-dev libpthread-stubs0-dev libset-scalar-perl libsm-dev
  libswresample-dev libswscale-dev libsys-hostname-long-perl libusb-1.0-0-dev libusb-1.0-doc libva-dev libva-drm1 libva-egl1 libva-glx1
  libva-tpi1 libva-wayland1 libva-x11-1 libwayland-bin libwayland-dev libx11-dev libx11-doc libxau-dev libxcb-render0-dev libxcb-shm0-dev
  libxcb1-dev libxcomposite-dev libxcursor-dev libxdamage-dev libxdmcp-dev libxext-dev libxfixes-dev libxft-dev libxi-dev libxinerama-dev
  libxml2-utils libxrandr-dev libxrender-dev po-debconf x11proto-composite-dev x11proto-core-dev x11proto-damage-dev x11proto-fixes-dev
  x11proto-input-dev x11proto-kb-dev x11proto-randr-dev x11proto-render-dev x11proto-xext-dev x11proto-xinerama-dev xorg-sgml-doctools
  xtrans-dev zlib1g-dev
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strong@foreverstrong:~/Downloads/l_openvino_toolkit_p_2018.4.420$

Install the Intel® Distribution of OpenVINO™ toolkit core components

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

/home/<user>/inference_engine_samples
/home/<user>/openvino_models

To install the Intel Distribution of OpenVINO toolkit core components:

(1) Choose one of the installation options below and run the related script with root or regular user privileges. The default installation directory path depends on the privileges you choose for the installation.
You can use either a GUI installation wizard or command-line instructions. The only difference between the two options is that the command-line instructions are text-based. This means that instead of clicking options in a GUI, command-line prompts ask for input on a text screen.

Use only one of these options:

• Option 1: GUI Installation Wizard:

sudo ./install_GUI.sh

• Option 2: Command-line Instructions:

sudo ./install.sh

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

(3) If needed, change the components you want to install or the installation directory. Pay attention to the installation directory, because you will need this information later. If you select the default options, the Installation summary GUI screen looks as follows:

• If you used root privileges to run the installer, it installs the Intel Distribution of OpenVINO toolkit in this directory: /opt/intel/computer_vision_sdk_/.
  For simplicity, a symbolic link to the latest installation is also created: /opt/intel/computer_vision_sdk/

• If you used regular user privileges to run the installer, it installs the Intel Distribution of OpenVINO toolkit in this directory: /home//intel/computer_vision_sdk_/
  For simplicity, a symbolic link to the latest installation is also created: /home//intel/computer_vision_sdk/

(4) A Complete screen indicates the installation is finished. Write down the version number of the software beginning with the year. You will need this information later in the document.

The core components are installed. Continue to the next section to set the 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/computer_vision_sdk/bin/setupvars.sh

(Optional) The Intel Distribution of OpenVINO toolkit 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/computer_vision_sdk/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

Important: This section is required. You must configure the Model Optimizer for at least one framework. The Model Optimizer will fail if you do not complete the steps in this section.

The Model Optimizer is a key component of the Intel Distribution of OpenVINO toolkit. You cannot do 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 IR is a pair of files that describe the whole model:

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

The Inference Engine reads, loads, and infers the IR files, using a common API across the CPU, GPU, or VPU hardware.

The Model Optimizer is a Python*-based command line tool (mo.py), which is located in /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer.

Use this tool on models trained with popular deep learning frameworks such as Caffe*, TensorFlow*, MXNet*, and ONNX* to convert them to an optimized IR format that the Inference Engine can use.

This section explains how to use scripts to configure the Model Optimizer either for all of the supported frameworks at the same time or for individual frameworks. If you want to manually configure the Model Optimizer instead of using scripts, see the using manual configuration process section in the Model Optimizer Developer Guide.

For more information about the Model Optimizer, see the Model Optimizer Developer Guide.

Model Optimizer configuration steps

You can either configure the Model Optimizer for all supported frameworks at once, or for 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: If you did not install the Intel Distribution of OpenVINO toolkit to the default installation directory, replace /intel/ with the directory where you installed the software to.

Option 1: Configure the Model Optimizer for all supported frameworks at the same time:

(1) Go to the Model Optimizer prerequisites directory:

cd /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites

(2) Run the script to configure the Model Optimizer for Caffe, TensorFlow, MXNet, Kaldi*, and ONNX:

sudo ./install_prerequisites.sh

strong@foreverstrong:~$ cd /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites/
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites$ ll
total 32
drwxr-xr-x 2 root root 4096 11月 20 09:17 ./
drwxr-xr-x 6 root root 4096 11月 20 09:17 ../
-rwxr-xr-x 1 root root  638 11月 20 09:17 install_prerequisites_caffe.sh*
-rwxr-xr-x 1 root root  638 11月 20 09:17 install_prerequisites_kaldi.sh*
-rwxr-xr-x 1 root root  638 11月 20 09:17 install_prerequisites_mxnet.sh*
-rwxr-xr-x 1 root root  636 11月 20 09:17 install_prerequisites_onnx.sh*
-rwxr-xr-x 1 root root 2852 11月 20 09:17 install_prerequisites.sh*
-rwxr-xr-x 1 root root  635 11月 20 09:17 install_prerequisites_tf.sh*
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites$ ./install_prerequisites.sh
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  Running setup.py install for termcolor ... done
  Running setup.py install for networkx ... done
Successfully installed absl-py-0.6.1 astor-0.7.1 decorator-4.3.0 gast-0.2.0 graphviz-0.10.1 grpcio-1.16.1 markdown-3.0.1 mxnet-1.0.0 networkx-2.2 numpy-1.15.4 onnx-1.3.0 protobuf-3.5.1 tensorboard-1.9.0 tensorflow-1.9.0 termcolor-1.1.0 typing-3.6.6 typing-extensions-3.6.6 werkzeug-0.14.1
You are using pip version 8.1.1, however version 18.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
[WARNING] All Model Optimizer dependencies are installed globally.
[WARNING] If you want to keep Model Optimizer in separate sandbox
[WARNING] run install_prerequisites.sh venv {caffe|tf|mxnet|kaldi|onnx}
strong@foreverstrong:/opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites$ 

Option 2: Configure the Model Optimizer for each framework separately:

(1) Go to the Model Optimizer prerequisites directory:

cd /opt/intel/computer_vision_sdk/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:

sudo ./install_prerequisites_tf.sh

• For MXNet:

sudo ./install_prerequisites_mxnet.sh

• For Kaldi:

sudo ./install_prerequisites_kaldi.sh

• For ONNX:

sudo ./install_prerequisites_onnx.sh

The Model Optimizer is configured for one or more frameworks. If you want to use a GPU or VPU, read through the next section for additional steps. After that, you are ready to use two short demos to see the results of running the Intel Distribution of OpenVINO toolkit and to verify your installation was successful. The demo scripts are required since they perform additional configuration steps.

Optional Steps

Use these steps to prepare your computer for to use the Intel® Processor Graphics, Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2.

Additional installation steps for processor graphics (GPU)

Note: These steps are only required if you want to enable the toolkit components to utilize processor graphics (GPU) on your system.

(1) Go to the install_dependencies directory:

cd /opt/intel/computer_vision_sdk/install_dependencies/

(2) Enter the super user mode:

sudo -E su

(3) Check your kernel version. The minimum supported kernel is 4.14:

uname -r

Note: You can use a kernel at or above 4.14.

(4) If your kernel version is below 4.14, update it to the minimum of 4.14:

./install_4_14_kernel.sh

(5) Install the Intel® Graphics Compute Runtime for OpenCL™ Driver components required to use the GPU plugin and write custom layers for Intel® Integrated Graphics:

./install_NEO_OCL_driver.sh

Note: Two command-line suggestions display:
— Add OpenCL user to video group
— Run script to install the 4.14 kernel script
Both suggestions are incorrect. Disregard them and continue.

(6) Reboot the machine:

reboot

(7) (Optional) Install header files to allow compiling a new code. You can find the header files at KhronosGroup OpenCL-Headers.
https://github.com/KhronosGroup/OpenCL-Headers

Additional installation steps for the Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2

Note: 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: https://software.intel.com/en-us/neural-compute-stick/get-started

https://software.intel.com/en-us/neural-compute-stick/get-started

(1) Add the current Linux user to the users group:

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

(2) To perform inference on Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2, install the USB rules as follows:

cat <<EOF > 97-usbboot.rules
SUBSYSTEM=="usb", ATTRS{idProduct}=="2150", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
EOF

sudo cp 97-usbboot.rules /etc/udev/rules.d/

sudo udevadm control --reload-rules

sudo udevadm trigger

sudo ldconfig

rm 97-usbboot.rules

strong@foreverstrong:~$ cd Downloads/
strong@foreverstrong:~/Downloads$ cat <<EOF > 97-usbboot.rules
> SUBSYSTEM=="usb", ATTRS{idProduct}=="2150", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
> SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
> SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
> EOF
strong@foreverstrong:~/Downloads$ ll
total 1412060
drwxr-xr-x  3 strong strong       4096 11月 20 20:39 ./
drwxr-xr-x 19 strong strong       4096 11月 20 09:14 ../
-rw-rw-r--  1 strong strong        378 11月 20 20:39 97-usbboot.rules
drwxr-xr-x  4 strong strong       4096 11月 10 03:11 l_openvino_toolkit_p_2018.4.420/
-rwxrwxrwx  1 strong strong 1445927668 11月 19 22:40 l_openvino_toolkit_p_2018.4.420.tgz*
strong@foreverstrong:~/Downloads$ sudo cp 97-usbboot.rules /etc/udev/rules.d/
[sudo] password for strong: 
strong@foreverstrong:~/Downloads$ 
strong@foreverstrong:~/Downloads$ sudo udevadm control --reload-rules
strong@foreverstrong:~/Downloads$ 
strong@foreverstrong:~/Downloads$ sudo udevadm trigger
strong@foreverstrong:~/Downloads$ 
strong@foreverstrong:~/Downloads$ sudo ldconfig
/sbin/ldconfig.real: /opt/intel/common/mdf/lib64/igfxcmrt64.so is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libva-x11.so.2 is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libva-drm.so.2 is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libva.so.2 is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libmfxhw64.so.1 is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libmfx.so.1 is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libva-glx.so.2 is not a symbolic link

/sbin/ldconfig.real: /opt/intel/mediasdk/lib64/libigdgmm.so.1 is not a symbolic link

strong@foreverstrong:~/Downloads$ 
strong@foreverstrong:~/Downloads$ rm 97-usbboot.rules
strong@foreverstrong:~/Downloads$

Continue to the next section to use short demos to see the results of running Intel Distribution of OpenVINO toolkit and to verify your installation was successful. The demo scripts are required since they perform additional configuration steps.

Use the Demo Scripts to Verify Your Installation

Important: This section is required. In addition to confirming that your installation was successful, the demo scripts perform additional steps, such as setting up your computer to use the Model Optimizer samples.

Note: To run the demo applications on Intel® Processor Graphics, Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2, make sure you completed the Additional Installation Steps first.

To learn more about the demo applications, see README.txt in /opt/intel/computer_vision_sdk/deployment_tools/demo/.

For detailed description of the pre-trained object detection and object recognition models, go to /opt/intel/computer_vision_sdk/deployment_tools/intel_models/ and open index.html.

Note:
The paths in this section assume you used the default installation directory to install the Intel Distribution of OpenVINO toolkit. If you installed the software to a directory other than /opt/intel/, update the directory path with the location where you installed the toolkit to.
If you installed the product as a root user, you must switch to the root mode before you continue: sudo -i

Run the Image Classification demo

(1) Go to the Inference Engine demo directory:

cd /opt/intel/computer_vision_sdk/deployment_tools/demo

(2) Run the Image Classification demo:

./demo_squeezenet_download_convert_run.sh

The Image Classification demo uses the Model Optimizer to convert a SqueezeNet model to .bin and .xml Intermediate Representation (IR) files. The Inference Engine component uses these files.

For a brief description of the Intermediate Representation .bin and .xml files, see Configuring the Model Optimizer.

This demo creates the directory /home/<user>/inference_engine_samples/.

This demo uses car.png in the demo directory. When the demo completes, you will see the label and confidence for the top-10 categories:

This demo is complete. Continue to the next section to run the Inference Pipeline demo.

Run the Inference Pipeline demo

(1) While still in /opt/intel/computer_vision_sdk/deployment_tools/demo/, run the Inference Pipeline demo:

./demo_security_barrier_camera.sh

This demo uses car.png in /opt/intel/computer_vision_sdk/deployment_tools/demo/ to show an inference pipeline. This demo uses three pre-trained models. The demo uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.
The demo works as follows:

• 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.
• The attributes identified as the license plate are used as input to the third model, which recognizes specific characters in the license plate.

For more information about the demo, see the Security Camera Sample.
https://software.intel.com/en-us/articles/OpenVINO-IE-Samples#security-barrier-camera

(2) When the demo completes, two windows are open:

• A console window that displays information about the tasks performed by the demo
• An image viewer window that displays a picture similar to the following:

(3) Close the image viewer screen to end the demo.

In this section, you saw a preview of the Intel Distribution of OpenVINO toolkit capabilities.

Your have completed all required installation, configuration, and build steps in this guide to use your CPU, GPU or VPU to work with your trained models.

Note: If you are migrating from the Intel® Computer Vision SDK 2017 R3 Beta version to the Intel Distribution of OpenVINO toolkit, read this information about porting your applications.
How to port your application from Intel® Computer Vision SDK 2017 R3 Beta to OpenVINO™ Toolkit.
https://software.intel.com/en-us/blogs/2018/05/14/how-to-port-your-application-from-intelr-computer-vision-sdk-2017-r3-beta-to-intelr

Summary

In this document, you installed the Intel Distribution of OpenVINO toolkit and the external dependencies. In addition, you might have installed a driver that will let you use a GPU to infer your models.

After the software was installed, you ran two demo applications to compile the extensions library and configured the Model Optimizer for one or more frameworks.

You are now ready to learn more about converting models trained with popular deep learning frameworks to the Inference Engine format, following the links below, or you can move on to running the sample applications.

To learn more about converting models, go to:

• Using the Model Optimizer to Convert Caffe* Models
  https://software.intel.com/en-us/articles/OpenVINO-Using-Caffe

• Using the Model Optimizer to Convert MXNet* Models
  https://software.intel.com/en-us/articles/OpenVINO-Using-MXNet

• Using the Model Optimizer to Convert TensorFlow* Models
  https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow

• Using the Model Optimizer to Convert ONNX* Models
  https://software.intel.com/en-us/articles/OpenVINO-Using-ONNX

Additional Resources

Intel Distribution of OpenVINO Toolkit home page
https://software.intel.com/en-us/openvino-toolkit

Intel Distribution of OpenVINO Toolkit documentation
https://software.intel.com/en-us/openvino-toolkit/documentation/featured

Intel Distribution of OpenVINO Toolkit Hello World Activities, see the Inference Tutorials for Face Detection and Car Detection Exercises.
https://github.com/intel-iot-devkit/inference-tutorials-generic

Intel® Neural Compute Stick 2 Get Started page
https://software.intel.com/en-us/neural-compute-stick/get-started

For more complete information about compiler optimizations, see our Optimization Notice.
https://software.intel.com/en-us/articles/optimization-notice

Wordbook

Intel® Deep Learning Deployment Toolkit，Intel® DLDT
Intermediate Representation，IR