Install Intel® Distribution of OpenVINO™ toolkit for Windows from Docker Image

This guide provides steps for creating a Docker image with Intel® Distribution of OpenVINO™ toolkit for Windows and using the Docker image on different devices.

System Requirements

Operating System

Supported Python Version

Windows Server Core base LTSC 2019

3.8

Windows 10, version 20H2

3.8

  • Windows 10, 64-bit Pro, Enterprise or Education (1607 Anniversary Update, Build 14393 or later) editions

  • Windows Server 2016 or higher

Additional Requirements for GPU

To use GPU Acceleration in Windows containers, make sure that the following requirements for Windows host, OpenVINO and Docker are met:

  • Windows requirements :

    • The container host must be running Windows Server 2019 or Windows 10 of version 1809 or higher.

    • The container base image must be mcr.microsoft.com/windows:1809 or higher. Windows Server Core and Nano Server container images are not currently supported.

    • The container host must be running Docker Engine 19.03 or higher.

    • The container host must have GPU running display drivers of version WDDM 2.5 or higher.

  • GPU requirement for OpenVINO: Intel Graphics Driver for Windows of version 15.65 or higher.

  • Docker isolation mode requirements :

Installation Flow

There are two ways to install OpenVINO with Docker. You can choose either of them according to your needs:

Getting a Prebuilt Image from Provided Sources

You can find prebuilt images on:

Preparing a Dockerfile

You can use the available Dockerfiles on GitHub or generate a Dockerfile with your settings via DockerHub CI Framework which can generate a Dockerfile, build, test and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.

Configuring the Docker Image for Different Devices

Installing Additional Dependencies for CPU

Installing CMake

To add CMake to the image, add the following commands to the Dockerfile:

RUN powershell.exe -Command `
    Invoke-WebRequest -URI https://cmake.org/files/v3.14/cmake-3.14.7-win64-x64.msi -OutFile %TMP%\\cmake-3.14.7-win64-x64.msi ; `
    Start-Process %TMP%\\cmake-3.14.7-win64-x64.msi -ArgumentList '/quiet /norestart' -Wait ; `
    Remove-Item %TMP%\\cmake-3.14.7-win64-x64.msi -Force

RUN SETX /M PATH "C:\Program Files\CMake\Bin;%PATH%"

In case of proxy issues, please add the ARG HTTPS_PROXY and -Proxy %HTTPS_PROXY% settings to the powershell.exe command to the Dockerfile. Then build a Docker image:

docker build . -t <image_name> `
--build-arg HTTPS_PROXY=<https://your_proxy_server:port>

Installing Microsoft Visual Studio Build Tools

You can add Microsoft Visual Studio Build Tools to a Windows OS Docker image using the offline or online installers for Build Tools.

Microsoft Visual Studio Build Tools are licensed as a supplement your existing Microsoft Visual Studio license.

Any images built with these tools should be for your personal use or for use in your organization in accordance with your existing Visual Studio and Windows licenses.

To add MSBuild 2019 to the image, add the following commands to the Dockerfile:

RUN powershell.exe -Command Invoke-WebRequest -URI https://aka.ms/vs/16/release/vs_buildtools.exe -OutFile %TMP%\\vs_buildtools.exe

RUN %TMP%\\vs_buildtools.exe --quiet --norestart --wait --nocache `
     --installPath "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools" `
     --add Microsoft.VisualStudio.Workload.MSBuildTools `
     --add Microsoft.VisualStudio.Workload.UniversalBuildTools `
     --add Microsoft.VisualStudio.Workload.VCTools --includeRecommended `
     --remove Microsoft.VisualStudio.Component.Windows10SDK.10240 `
     --remove Microsoft.VisualStudio.Component.Windows10SDK.10586 `
     --remove Microsoft.VisualStudio.Component.Windows10SDK.14393 `
     --remove Microsoft.VisualStudio.Component.Windows81SDK || IF "%ERRORLEVEL%"=="3010" EXIT 0 && powershell set-executionpolicy remotesigned

In case of proxy issues, please use the offline installer for Build Tools.

Configuring the Image for GPU

Note

Since GPU is not supported in prebuilt images or default Dockerfiles, you must make sure the Additional Requirements for GPU in System Requirements are met, and do the following steps to build the image manually.

  1. Reuse one of available Dockerfiles. You can also use your own Dockerfile.

  2. Check your Windows host and container isolation process compatibility.

  3. Find the appropriate Windows container base image on DockerHub and set up your host/container version in the FROM Dockerfile instruction.

    For example, in the openvino_c_dev_<version>.dockerfile, change:

    FROM mcr.microsoft.com/windows/servercore:ltsc2019 AS ov_base

    to:

    FROM mcr.microsoft.com/windows:20H2

  4. Build the Docker image by running the following command:

    docker build --build-arg package_url=<OpenVINO pkg> -f <Dockerfile> -t <image_name> .

  5. Copy OpenCL.dll from your C:\Windows\System32 host folder to any temp directory:

    mkdir C:\tmp
copy C:\Windows\System32\OpenCL.dll C:\tmp

Running the Docker Image on Different Devices

Running the Image on CPU

To start the interactive session, run the following command:

docker run -it --rm <image_name>

If you want to try some samples, run the image with the following command:

docker run -it --rm <image_name>
cmd /S /C "omz_downloader --name googlenet-v1 --precisions FP16 && omz_converter --name googlenet-v1 --precision FP16 && curl -kO https://storage.openvinotoolkit.org/data/test_data/images/car_1.bmp && python samples\python\hello_classification\hello_classification.py public\googlenet-v1\FP16\googlenet-v1.xml car_1.bmp CPU"

Running the Image on GPU

Note

Since GPU is not supported in prebuilt images or default Dockerfiles, you must make sure the Additional Requirements for GPU in System Requirements are met, and configure and build the image manually before you can run inferences on a GPU.

  1. To try inference on a GPU, run the image with the following command:

    docker run -it --rm -u ContainerAdministrator --isolation process --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 -v C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409:C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409 -v C:\tmp:C:\tmp <image_name>

    where

    • --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 is a reserved interface class GUID for a GPU device.

    • C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409 is the path to OpenCL driver home directory. To find it on your PC, run the C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_\* regular expression.

    • C:\tmp is the folder with the copy of OpenCL.dll from your C:\Windows\System32 host folder.

  2. Copy OpenCL.dll to the C:\Windows\System32 folder inside the container and set appropriate registry entry. Now you can run inference on a GPU device:

    copy C:\tmp\OpenCL.dll C:\Windows\System32\ && reg add "HKLM\SOFTWARE\Khronos\OpenCL\Vendors" /v "C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409\ocl\bin\x64\intelocl64.dll" /t REG_DWORD /d 0

    For example, run the Hello Classification Python sample with the following command:

    omz_downloader --name googlenet-v1 --precisions FP16 && omz_converter --name googlenet-v1 --precision FP16 && curl -kO https://storage.openvinotoolkit.org/data/test_data/images/car_1.bmp && python samples\python\hello_classification\hello_classification.py public\googlenet-v1\FP16\googlenet-v1.xml car_1.bmp GPU

Additional Resources