- These steps apply to Apple macOS*.
- An internet connection is required to follow the steps in this guide.
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 for macOS* includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT) and OpenCV* to deploy applications for accelerated inference on Intel® CPUs.
Included with the Installation
The following components are installed by default:
|Model Optimizer||This tool imports, converts, and optimizes models, which 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 a deep learning model. It includes a set of libraries for an easy inference integration into your applications.|
|OpenCV*||OpenCV* community version compiled for Intel® hardware. Includes PVL libraries for computer vision.|
|Sample Applications||A set of simple console applications demonstrating how to use the Inference Engine in your applications.|
The development and target platforms have the same requirements, but you can select different components during the installation, based on your intended use.
NOTE: The current version of the Intel® Distribution of OpenVINO™ toolkit for macOS* supports inference on Intel CPUs only.
This guide assumes you downloaded the OpenVINO toolkit for macOS*. 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.
- 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:
If you have a previous version of the Intel® Distribution of OpenVINO™ toolkit installed, rename or delete two directories:
To install the OpenVINO toolkit core components:
Downloadsdirectory. By default, the disk image file is saved as
m_openvino_toolkit_p_<version>.dmgfile to mount. The disk image is mounted to
/Volumes/m_openvino_toolkit_p_<version>and automatically opened in a separate window.
On the User Selection screen, choose a user account for the installation:
The default installation directory path depends on the privileges you choose for the installation.
If you used root or administrator privileges to run the installer, it installs the OpenVINO toolkit to
For simplicity, a symbolic link to the latest installation is also created:
If you used regular user privileges to run the installer, it installs the OpenVINO toolkit to
For simplicity, a symbolic link to the latest installation is also created:
If needed, click Customize to change the installation directory or the components you want to install:
Click Next to save the installation options and show the Installation summary screen.
If the installation process indicated if you are missing dependencies, you must install each missing dependency. Click the link for the first dependency you must install:
These steps guide you through installing CMake 3.4 or higher, which is required to build the Intel® Distribution of OpenVINO™ toolkit samples.
.dmgfile name to download the installer image. The file is saved to your
.dmgfile to launch the installer.
PATHenvironment variable. Open a Terminal window and run the following command:
Python with pip is required to run the Model Optimizer. Use these steps to install the correct version of the Python software.
The dependencies are installed. Continue to the next section to to set environment variables.
NOTE: The Model Optimizer has additional prerequisites that are addressed later in this document.
Apple Xcode* IDE is required for building the Intel® Deep Learning Deployment Toolkit С++ samples and demonstration applications.
Command Line Tools are required to install. MAKE SURE YOU INSTALL THEM.
You must update several environment variables before you can compile and run OpenVINO™ applications. Open the macOS Terminal* or a command-line interface shell you prefer and run the following script to temporarily set your environment variables:
(Optional): The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:
.bash_profilefile in the current user home directory:
:wqand press the Enter key.
[setupvars.sh] OpenVINO environment initialized.
The environment variables are set. Continue to the next section to 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 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
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 Configuring the Model Optimizer document.
For more information about the Model Optimizer, see the Model Optimizer Developer Guide.
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.
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 environment.
NOTE: If you installed OpenVINO to the non-default installation directory, replace
/opt/intel/with the directory where you installed the software to.
Option 1: Configure the Model Optimizer for all supported frameworks at the same time:
Option 2: Configure the Model Optimizer for each framework separately:
The Model Optimizer is configured for one or more frameworks. You are ready to use two short demos to see the results of running the OpenVINO toolkit and to verify your installation was successful. The demo scripts are required since they perform additional configuration steps. Continue to the next section.
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.
To learn more about the demo applications, see
For detailed description of the pre-trained object detection and object recognition models, go to the Overview of OpenVINO toolkit Pre-Trained Models page.
- The paths in this section assume you used the default installation directory to install the 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:
The Image Classification verification script uses the Model Optimizer to convert a SqueezeNet model to
.xml Intermediate Representation (IR) files. The Inference Engine component uses these files.
For a brief description of the Intermediate Representation
.xml files, see Configuring the Model Optimizer.
This verification script creates the directory
/home/<user>/inference_engine_samples/, builds the Image Classification Sample 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:
This script is complete. Continue to the next section to run the Inference Pipeline verification script.
While still in
/opt/intel/openvino/deployment_tools/demo/, run the Inference Pipeline verification script:
This script downloads three pre-trained models IRs, builds the Security Barrier Camera Demo application and run 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:
To learn about the verification scripts, see the
README.txt file in
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.
In this section, you saw a preview of the OpenVINO toolkit capabilities.
Your have completed all the required installation, configuration, and build steps to work with your trained models using CPU on macOS.
Read the Summary for your next steps.
In this document, you installed the Intel® Distribution of OpenVINO™ toolkit and the external dependencies .
After the software was installed, you configured the Model Optimizer for one or more frameworks and ran two demo applications to compile and run two sample applications.
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: