- The Intel® Distribution of OpenVINO™ is supported on macOS* 10.14.4 or higher versions.
- This installation has been validated on macOS 10.14.4.
- An internet connection is required to follow the steps in this guide. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
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.
The Intel® Distribution of OpenVINO™ 2019 R1.1 toolkit for macOS*:
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 provides step-by-step instructions on how to install the Intel® Distribution of OpenVINO™ 2019 R1.1 toolkit for macOS*.
The following steps will be covered:
If you have a previous version of the Intel® Distribution of OpenVINO™ toolkit installed, rename or delete these two directories:
Download the latest version of OpenVINO toolkit for macOS* then return to this guide to proceed with the installation.
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.
You need to 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. 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.
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 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 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 on the CPU hardware.
For more information about the Model Optimizer, see the Model Optimizer Developer Guide.
You can choose to 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, verify that you installed all dependencies listed under Software Requirements at the top of this guide.
NOTE: If you installed OpenVINO to a non-default installation directory, replace
/opt/intel/with the directory where you installed the software.
Option 1: Configure the Model Optimizer for all supported frameworks at the same time:
Option 2: Configure the Model Optimizer for each framework separately:
Configure individual frameworks separately ONLY if you did not select Option 1 above.
The Model Optimizer is configured for one or more frameworks.
You are ready to verify the installation by running the verification scripts.
- The steps shown here 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.
- If you installed the product as a root user, you must switch to the root mode before you continue:
To verify the installation and compile two Inference Engine samples, run the verification applications provided with the product on the CPU:
The Image Classification verification script downloads a public SqueezeNet Caffe* model and runs the Model Optimizer to convert the model to
.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 creates the directory
/home/<user>/inference_engine_samples/, builds the Image Classification Sample application and runs with the model IR and
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:
For a brief description of the Intermediate Representation
.xml files, see Configuring the Model Optimizer.
This script is complete. Continue to the next section to run the Inference Pipeline verification script.
/opt/intel/openvino/deployment_tools/demo/, run the Inference Pipeline verification script:
car_1.bmpimage from the
demodirectory 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:
Congratulations, you have completed the Intel® Distribution of OpenVINO™ 2019 R1.1 installation for macOS. To learn more about what you can do with the Intel® Distribution of OpenVINO™ toolkit, see the additional resources provided below.
Visit the Intel Distribution of OpenVINO Toolkit Inference Tutorials for Face Detection and Car Detection Exercises