NOTES:
- Intel® Distribution of OpenVINO™ toolkit was formerly known as the Intel® Computer Vision SDK.
- This guide applies to Microsoft Windows* 10 64-bit. For Linux* OS information and instructions, see the Installation Guide for Linux.
IMPORTANT:
- All steps in this guide are required, unless otherwise stated.
- In addition to the download package, you must install dependencies and complete configuration steps.
Your installation is complete when these are all completed:
IMPORTANT: This is a two-part installation that requires two separate downloads from the Internet and two installation processes: one for Microsoft Visual Studio and one for the Build Tools. You must complete both installations.
IMPORTANT: As part of this installation, make sure you click the option to add the application to your
PATH
environment variable.
The Intel® Distribution of OpenVINO™ toolkit speeds the deployment of applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware to maximize performance.
The Intel® Distribution of OpenVINO™ toolkit includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT). For more information, see the online Intel® Distribution of OpenVINO™ toolkit Overview page.
The Intel® Distribution of OpenVINO™ toolkit for Windows* 10 OS:
The following components are 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. NOTE: Popular frameworks include such frameworks as 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. |
OpenCV* | OpenCV* community version compiled for Intel® hardware. Includes PVL libraries for computer vision |
OpenVX* | Intel's implementation of OpenVX* optimized for running on an Intel CPU, GPU, or IPU (Image processing unit). |
Pre-trained models | A set of Intel's pre-trained models for learning and demo purposes or to develop deep learning software |
Sample Applications | A set of simple console applications demonstrating how to use Intel's Deep Learning Inference Engine in your applications. For additional information about building and running the samples, refer to the Inference Engine Samples Overview |
Only the Intel® CPU, Intel® Processor Graphics, Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2 and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs options are supported for the Windows* installation. Linux* is required to use the FPGA.
Hardware
Processor Notes:
Operating System
Downloads
directory as w_openvino_toolkit_p_<version>.exe
.Downloads
folder.Double-click w_openvino_toolkit_p_<version>.exe
. A window opens to let you choose your installation directory and components. The default installation directory is C:\Intel
. If you choose a different installation directory, the installer will create the directory for you:
If you are missing external dependencies, you will see a warning screen. Write down the dependencies you are missing. You need to take no other action at this time. After installing the Intel® Distribution of OpenVINO™ toolkit core components, you will be provided instructions to install the missing dependencies. The screen example below indicates you are missing two dependencies:
When the first part of installation is complete, the final screen informs you that the core components have been installed and additional steps still required:
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:
If you have no missing dependencies, skip ahead to Configure the Model Optimizer.
Microsoft Visual Studio with Visual Studio C++ is required for building the Intel® Deep Learning Deployment Toolkit samples and demonstration applications. You can install the free Community version of Microsoft Visual Studio.
IMPORTANT: The Microsoft Visual Studio dependency is a two-part installation that consists of Microsoft Visual Studio 2017 or 2015 and the Microsoft Visual Studio Build Tools. This guide includes steps for installing both parts of this dependency. These are separate installations. MAKE SURE YOU INSTALL BOTH COMPONENTS.
The steps below apply to Microsoft Visual Studio 2017. If you prefer to use Microsoft Visual Studio 2015, see Installing Microsoft Visual Studio 2015 for Intel® Distribution of OpenVINO™ toolkit.
Click Free Download in the Visual Studio 2017 box, Community section:
An executable file named vs_community__313888930.1524151023.exe
, or similar, is saved in your Downloads
folder.
From the Workloads tab, use the check boxes to select Universal Windows Platform development and **Desktop development with C++**.
Under the Individual components tab, select MSBuild:
The Summary at the right side of the screen displays your installation selections:
Continue to the next section to install the Build Tools for Visual Studio 2017.
The Build Tools for Visual Studio 2017 is the second part of the Microsoft Visual Studio dependency. You must complete this installation.
Click the Download button next to Build Tools for Visual Studio 2017:
vs_buildtools.exe
, or similar, is saved in your Downloads
folder.The installation opens to the Workloads tab. Select Visual C++ build tools:
The Summary on the right side shows the features you chose to install:
You have completed the Visual Studio 2017 installation.
Install your next dependency:
Or if you have installed all the dependencies, you are ready to set the environment variables.
These steps guide you through installing CMake 3.4 or higher, which is required to build the Intel® Distribution of OpenVINO™ toolkit samples.
.msi
. The file is saved to your Downloads
folder.Downloads
folder.NOTE: If you have a previous version of CMake installed, you are prompted to uninstall it. You must uninstall the previous version before installing the new version. Follow the instructions on the screen and then launch the installer again to install the new version.
In the installer, select the option to Add CMake to the system PATH for all users:
You have completed the CMake installation. Next, install Python 3.6.5 if the Intel® Distribution of OpenVINO™ toolkit installation indicated you are missing the software.
Python 3.6.5 with pip is required to run the Model Optimizer. Use these steps to install the correct version of the Python software.
python-3.6.5-amd64.exe
in your Downloads
folder. IMPORTANT: At the bottom of the install screen, select Add Python 3.6 to PATH.
You have completed the Python installation and are ready to set environment variables. Continue to the next section.
You must update several environment variables before you can compile and run OpenVINO™ applications. Open the Command Prompt and run the following batch file to temporarily set your environment variables:
(Optional): OpenVINO toolkit environment variables are removed when you close the Command Prompt window. As an option, you can permanently set the environment variables manually.
The environment variables are set. Continue to the next section to configure the Model Optimizer.
IMPORTANT: These steps are 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.
NOTE: If you see an error indicating Python is not installed when you know you installed it, your computer might not be able to find the program. For the instructions to add Python to your system environment variables, see Update Your Windows Environment Variables.
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 dataThe 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 C:\Program Files (x86)\IntelSWTools\openvino\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 on the Configuring the Model Optimizer page.
For more information about the Model Optimizer, see the Model Optimizer Developer Guide.
You can configure the Model Optimizer either 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: In the steps below:
- If you you want to use the Model Optimizer from another installed versions of Intel® Distribution of OpenVINO™ toolkit installed, replace
openvino
withopenvino_<version>
.- If you installed the Intel® Distribution of OpenVINO™ toolkit to the non-default installation directory, replace
C:\Program Files (x86)\IntelSWTools
with the directory where you installed the software.
These steps use a command prompt to make sure you see error messages.
Open a command prompt. To do so, type cmd
in your Search Windows box and then press Enter. Type commands in the opened window:
The Model Optimizer is configured for one or more frameworks. Success is indicated by a screen similar to this:
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. Continue to the next section.
If you want to use a GPU or VPU, or update your Windows* environment variables, read through the Optional Steps section.
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 Model Optimizer samples.
NOTE: To run the demo applications on Intel® Processor Graphics, Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, or Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, make sure you completed the Additional Installation Steps first.
To learn more about the demo applications, see README.txt
in C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\
.
For detailed description of the pre-trained object detection and object recognition models, see the Overview of OpenVINO toolkit Pre-Trained Models page.
NOTES:
- The paths in this section assume you used the default installation directory. If you used a directory other than
C:\Program Files (x86)\IntelSWTools
, update the directory with the location where you installed the software.- 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.
To run the script, start the demo_squeezenet_download_convert_run.bat
file:
This script downloads a SqueezeNet model, uses the Model Optimizer to convert the model to the <tt>.bin and <tt>.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 application and run it with the car.png
image in the demo directory. For a brief description of the Intermediate Representation, see Configuring the Model Optimizer.
When the verification script completes, you will have the label and confidence for the top-10 categories:
This demo is complete. Leave the console open and continue to the next section to run the Inference Pipeline demo.
To run the script, start the demo_security_barrier_camera.bat
file while still in the console:
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 demo completes, you have two windows open:
Close the image viewer window to end the demo.
To learn about the verification scripts, see the README.txt
file in /opt/intel/openvino/deployment_tools/demo
.
In this section, you saw a preview of the Intel® Distribution of OpenVINO™ toolkit capabilities.
You have completed all the required installation, configuration, and build steps to work with your trained models using CPU.
If you want to use Intel® Processor graphics (GPU), Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2 or Intel® Vision Accelerator Design with Intel® Movidius™ (VPU), or add CMake* and Python* to your Windows* environment variables, read through the next section for additional steps.
Read the Summary for your next steps.
Use the optional steps below if you want to:
If you prefer to use Microsoft Visual Studio 2015 instead of Microsoft Visual Studio 2017 to build your sample applications, refer to Installing Microsoft Visual Studio* 2015 for Intel® Distribution of OpenVINO™ toolkit.
NOTE: These steps are required only if you want to use a GPU.
If your applications offload computation to Intel® Integrated Graphics, you must have the Intel Graphics Driver for Windows version 15.65 or higher. To see if you have this driver installed:
Click the drop-down arrow to view the Display adapters. You see the adapter that is installed in your computer:
Click the Driver tab to see the driver version. Make sure the version number is 15.65 or higher.
You are done updating your device driver and are ready to use your GPU.
NOTE: These steps are only required if you want to perform inference on Intel® Movidius™ Neural Compute Stick powered by the Intel® Movidius™ Myriad™ 2 VPU or the
Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X VPU. See also Intel® Neural Compute Stick 2 Get Started
For Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2, the OpenVINO™ toolkit provides the Movidius™ VSC driver. To install the driver:
<INSTALL_DIR>\deployment_tools\inference-engine\external\MovidiusDriver
directory, where <INSTALL_DIR>
is the directory in which the Intel Distribution of OpenVINO toolkit is installed.Movidius_VSC_Device.inf
file and choose Install from the pop up menu.You have installed the driver for your Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2.
NOTE: These steps are required only if you want to use Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
To perform inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, the following additional installation steps are required:
<INSTALL_DIR>\deployment_tools\inference-engine\external\MovidiusDriver
directory, where <INSTALL_DIR>
is the directory in which the Intel Distribution of OpenVINO toolkit is installed.Movidius_VSC_Device.inf
file and choose Install from the pop up menu.<INSTALL_DIR>\deployment_tools\inference-engine\external\hddl\SMBusDriver
directory, where <INSTALL_DIR>
is the directory in which the Intel Distribution of OpenVINO toolkit is installed.hddlsmbus.inf
file and choose Install from the pop up menu.You are done installing your device driver and are ready to use your Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
NOTE: These steps are only required under special circumstances, such as if you forgot to check the box during the CMake* or Python* installation to add the application to your Windows
PATH
environment variable.
Use these steps to update your Windows PATH
if a command you execute returns an error message stating that an application cannot be found. This might happen if you do not add CMake or Python to your PATH
environment variable during the installation.
PATH
, browse to the directory in which you installed CMake. The default directory is C:\Program Files\CMake
.PATH
, browse to the directory in which you installed Python. The default directory is C:\Users\<USER_ID>\AppData\Local\Programs\Python\Python36\Python
.Your PATH
environment variable is updated.
In this document, you installed the Intel® Distribution of OpenVINO™ toolkit and its dependencies. You also configured the Model Optimizer for one or more frameworks. After the software was installed and configured, you ran two demo applications. You might have also installed drivers that will let you use a GPU or VPU to infer your models.
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 deep learning models, go to: