Installing the OpenVINO™ Toolkit for Linux*

Introduction

NOTES:
The OpenVINO™ toolkit was formerly known as the Intel® Computer Vision SDK.
These steps apply to Ubuntu*, CentOS*, and Yocto*.

The 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 OpenVINO™ Toolkit includes the Intel® Deep Learning Deployment Toolkit.

The OpenVINO™ toolkit for Linux:

• Enables CNN-based deep learning inference on the edge
• Supports heterogeneous execution across a CPU, Intel® Integrated Graphics, and Intel® Movidius™ Neural Compute Stick
• 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*

The installation package is free and comes as an archive that contains the software and installation scripts.

These instructions describe:

• What is included in the free download
• System requirements
• Software dependencies
• Installing the OpenVINO™ toolkit for Linux
• Next steps

What's Included

Where to Download This Release

https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-linux

System Requirements

This guide covers the Linux* version of the OpenVINO™ toolkit that does not includes FPGA support. For the toolkit that includes FPGA support, see https://software.intel.com/en-us/articles/OpenVINO-Install-Linux-FPGA.

Development and Target Platform

The development and target platforms have the same requirements, but you can select different components during the installation.

Processors

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

Processor Notes:

• Processor graphics are not included in all processors. See https://ark.intel.com/ for information about your processor.
• A chipset that supports processor graphics is required for Intel® Xeon® processors.

Operating Systems:

• Ubuntu* 16.04.3 long-term support (LTS), 64-bit
• CentOS* 7.4, 64-bit
• Yocto Project* Poky Jethro* v2.0.3, 64-bit (for target only)

Pre-Installation

Use these steps to prepare your development machine for the OpenVINO™ toolkit.

1. Download the OpenVINO™ toolkit. By default, the file is saved as l_intel_cv_sdk_p_2018.0.<version>.tar
2. Unpack the .tar file:

   tar -xf l_intel_cv_sdk_p_2018.0.<version>.tar

3. The files are unpacked to a directory named l_intel_cv_sdk_p_2018.0.<version>
4. Go to the l_intel_cv_sdk_p_2018.0.<version> directory:

   cd l_intel_cv_sdk_p_2018.0.<version>

External Software Dependencies

The dependencies are packages required for Intel-optimized OpenCV 3.4, the Deep Learning Inference Engine, and the Deep Learning Model Optimizer tools. Before installing the OpenVINO™ toolkit, use an installation script that's in l_intel_cv_sdk_p_2018.0.<version>:

./install_cv_sdk_dependencies.sh

Installation Steps

1. Go to the intel_cv_sdk_2018.0.<version> directory.
2. Start the GUI-based installation wizard:

   ./install_GUI.sh

   Or the CLI installer:

   ./install.sh

3. Choose between a GUI installation wizard or command-line instructions. The only difference between them is that the command-line installer is text-based. Instead of clicking options from a GUI, command-line prompts ask for input on a text screen.
4. The Prerequisites screen tells you if you are missing any required or recommended components, and the effect the missing component has on installing or using the product.
5. Click Next to begin the installation, or make final changes to your component selections and choose your installation directory.
6. The Installation summary screen shows you the options that will be installed if you make no changes.
7. Choose Install if you are ready to start the installation, or if you want to change the selected components and/or specify the installation directory, choose Customize. To proceed with the standard installation, choose Install.
8. A Complete screen indicates the software is installed. Click Finish to close the wizard.
9. Open the Getting Started page in the browser in the same mode in which you completed the installation. For example, if you performed your installation in sudo mode, use this command to go to the default installation directory:

   cd /opt/intel/computer_vision_sdk_2018.0.<version>/

Set Environment Variables

Updates to several environment variables are required to compile and run OpenVINO™ toolkit applications. You can permanently set environment variables in a way matching your system's conventions. A method to set these variables temporarily (lasting only as long as the shell) is provided. For a standard OpenVINO™ toolkit installation:​

source /opt/intel/computer_vision_sdk_2018.0.<version>/bin/setupvars.sh

Post-Installation

Set External Software Dependencies (Processor Graphics)

The installation automatically creates the install_dependencies directory under /opt/intel/computer_vision_sdk. This directory contains the following scripts to enable components to utilize processor graphics on your system:

• install_4_14_kernel.sh has steps to update your kernel to 4.14. This is the minimum kernel supported, and the configuration that is validated, but you can choose newer kernels as well. These are mainline kernels, not the kernels officially validated with your operating system. To see if you need to run this script, check your kernel version use uname –r.
• The install_NEO_OCL_driver.sh script installs the OpenCL™ NEOdriver components needed to use the clDNN GPU plugin and write custom layers for Intel® Integrated Graphics. For full functionality from this driver you must be running a 4.14 or newer kernel.
• The ./MediaStack/install_media.sh script installs the Intel® Media SDK. This SDK offers access to hardware accelerated video codecs and frame processing. This version of Intel Media SDK also requires a 4.14 or newer kernel. For more information, see https://github.com/Intel-Media-SDK/MediaSDK/releases.

NOTE: You must reboot the machine after running the scripts.

Run these scripts and then reboot:

cd /opt/intel/computer_vision_sdk/install_dependencies/
sudo –E su
./install_4_14_kernel.sh
./install_OCL_driver.sh
MediaStack/install_media.sh
reboot

The OpenCL components can be made more useful by installing header files to allow compiling new code. You can get these  from https://github.com/KhronosGroup/OpenCL-Headers.git

To make libOpenCL easier to find you may also want to consider adding symbolic links:

ln -s /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 /usr/lib/x86_64-linux-gnu/libOpenCL.so
ln -s /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 /opt/intel/opencl/libOpenCL.so

To run clDNN, libOpenCL.so.1 must be in the library search path (LD_LIBRARY_PATH). You can do this by the many standard ways of setting environment variables, or update the /opt/intel/computer_vision_sdk_2018.0.<version>/bin/setupvars.sh script to include the libOpenCL.so.1 directory.

USB Rules (Intel® Movidius™ Neural Compute Stick)

To perform inference on Intel® Movidius™ Neural Compute Stick, install 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}=="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

Next Steps

Learn About the OpenVINO™ Toolkit

Before using the OpenVINO™ toolkit, read through the information on the Web site to gain a better understanding of how the product works.

Compile the Extensions Library

Some topology-specific layers, like DetectionOutput used in the SSD*, are delivered in source code that assumes the extensions library is compiled and loaded. The extensions are required for pre-trained models inference. While you can build the library manually, the best way to compile the extensions library is to execute the demo scripts.

Run the Demonstration Applications

To verify the installation, run the demo apps in <INSTALL_DIR>/deployment_tools/demo. For demo app documentation, see README.txt in <INSTALL_DIR>/deployment_tools/demo.

The demo apps and their functions are:

• demo_squeezenet_download_convert_run.sh. Illustrates the basic steps used to convert a model and run it. This enables the Intel® Deep Learning Deployment Toolkit to perform a classification task with the SqueezeNet model. This demo:
  • Downloads a public SqueezeNet model.
  • Installs all prerequisites to run the Model Optimizer.
  • Converts the model to an Intermediate Representation.
  • Builds the Image Classification Sample in <INSTALL_DIR>/deployment_tools/inference_engine/samples/classification_sample
  • Runs the sample using cars.png from the demo directory.
  • Shows the label and confidence for the top-10 categories.
• demo_security_barrier_camera_sample.sh. Shows an inference pipeline using three of the pre-trained models included with the OpenVINO™ toolkit. The region found by one model becomes the input to the next. Vehicle regions found by object recognition in the first phase become the input to the vehicle attributes model, which locates the license plate. The region identified in this step becomes the input to a license plate character recognition model. This demo:
  • Builds the Inference Engine Security Barrier Camera Sample from <INSTALL_DIR>/deployment_tools/inference_engine/samples/security_barrier_camera_sample.
  • Runs the sample using car_1.bmp from the demo directory.
  • Displays the resulting frame with detections rendered as bounding boxes and text.

For documentation on the demo apps, see the README.txt file in the <INSTALL_DIR>/deployment_tools/demo directory.

Other Important Information

• See the <INSTALL_DIR>/deployment_tools/inference_engine/samples/ directory and the samples overview documentation to learn about the range of samples available for the Inference Engine.
• Before using the Model Optimizer to start working with your trained model, make sure your Caffe*, TensorFlow*, or MXNet* framework is prepared for any custom layers you have in place.
• For developer guides, API references, tutorials, and other online documentation, see the OpenVINO Toolkit documentation.

Helpful Links

NOTE: Links open in a new window.

OpenVINO™ toolkit home page: https://software.intel.com/en-us/openvino-toolkit

OpenVINO™ toolkit documentation: https://software.intel.com/en-us/openvino-toolkit/documentation/featured

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