- This guide applies to Microsoft Windows* 10 64-bit. For Linux* OS information and instructions, see the Installation Guide for Linux.
TIP: If you want to quick start with OpenVINO™ toolkit, you can use the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI that enables you to import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment on various Intel® platforms.
- 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:
Also, the following steps will be covered in the guide:
OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
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:
|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|
|Inference Engine Samples||A set of simple console applications demonstrating how to use Intel's Deep Learning Inference Engine in your applications.|
|Demos||A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases|
|Additional Tools||A set of tools to work with your models including Accuracy Checker utility, Post-Training Optimization Tool Guide, Model Downloader and other|
|Documentation for Pre-Trained Models||Documentation for the pre-trained models available in the Open Model Zoo repo|
NOTE: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is no longer supported.
Go to the
Downloads folder and 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:\Program Files (x86)\Intel\openvino_<version>, for simplicity, a shortcut to the latest installation is also created:
C:\Program Files (x86)\Intel\openvino_2021. If you choose a different installation directory, the installer will create the directory for you.
NOTE: If there is an OpenVINO™ toolkit version previously installed on your system, the installer will use the same destination directory for next installations. If you want to install a newer version to a different directory, you need to uninstall the previously installed versions.
3. Click Next.
NOTE: If you installed the Intel® Distribution of OpenVINO™ to the non-default install directory, replace
C:\Program Files (x86)\Intelwith the directory in which you installed the software.
You must update several environment variables before you can compile and run OpenVINO™ applications. Open the Command Prompt, and run the
setupvars.bat batch file to temporarily set your environment variables:
IMPORTANT: Windows PowerShell* is not recommended to run the configuration commands, please use the Command Prompt instead.
(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.
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 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.
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
C:\Program Files (x86)\Intel\openvino_2021\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
<version>is the required version.
- If you installed the Intel® Distribution of OpenVINO™ toolkit to the non-default installation directory, replace
C:\Program Files (x86)\Intelwith 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 have completed all required installation, configuration and build steps in this guide to use your CPU to work with your trained models.
If you want to use a GPU or VPU, or update your Windows* environment variables, read through the Optional Steps section:
Or proceed to the Get Started to get started with running code samples and demo applications.
NOTE: These steps are required only if you want to use an Intel® integrated GPU.
If your applications offload computation to Intel® Integrated Graphics, you must have the Intel Graphics Driver for Windows installed for your hardware. Download and install the recommended version.
To check 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.
You are done updating your device driver and are ready to use your GPU. Proceed to the Get Started to get started with running code samples and demo applications.
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>is the directory in which the Intel Distribution of OpenVINO toolkit is installed.
hddlsmbus.inffile 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.
After configuration is done, you are ready to Get Started with running code samples and demo applications.
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
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
PATH, browse to the directory in which you installed Python. The default directory is
C:\Users\<USER_ID>\AppData\Local\Programs\Python\Python36\Python. Note that the
AppDatafolder is hidden by default. To view hidden files and folders, see the Windows 10 instructions.
PATH environment variable is updated. If the changes don't take effect immediately, you may need to reboot.
Now you are ready to get started. To continue, see the following pages:
Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:
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 verification scripts. You might have also installed drivers that will let you use a GPU or VPU to infer your models and run the Image Classification Sample application.
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: