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

@sphinxdirective

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

System Requirements ###################

.. tab:: Target Operating Systems with Python Versions

+----------------------------------------------+-------------------------+ | Operating System | Included Python Version | +==============================================+=========================+ | Ubuntu 18.04 long-term support (LTS), 64-bit | 3.8 | +----------------------------------------------+-------------------------+ | Ubuntu 20.04 long-term support (LTS), 64-bit | 3.8 | +----------------------------------------------+-------------------------+ | Red Hat Enterprise Linux 8, 64-bit | 3.8 | +----------------------------------------------+-------------------------+

.. tab:: Host Operating Systems

  • Linux
  • Windows Subsystem for Linux 2 (WSL2) on CPU or GPU
  • macOS on CPU only

To launch a Linux image on WSL2 when trying to run inferences on a GPU, make sure that the following requirements are met:

  • Only Windows 10 with 21H2 update or above installed and Windows 11 are supported.
  • Intel GPU driver for Windows, version 30.0.100.9684 or newer needs to be installed. For more details, refer to this article at intel.com <https://www.intel.com/content/www/us/en/artificial-intelligence/harness-the-power-of-intel-igpu-on-your-machine.html#articleparagraph_983312434>__.
  • Currently, the Docker images contain preinstalled recommended version of OpenCL Runtime with WSL2 support.

Installation #############

  • Use a prebuilt image:

    1. Get a prebuilt image from provided sources <#getting-a-prebuilt-image-from-provided-sources>__
    2. Run the image on different devices <#running-the-docker-image-on-different-devices>__
    3. Run samples in the Docker image <#running-samples-in-docker-image>__

  • If you want to customize your image, you can also build a Docker image manually:

    1. Prepare a Dockerfile <#preparing-a-dockerfile>__
    2. Configure the Docker image <#configuring-the-image-for-different-devices>__
    3. Run the image on different devices <#running-the-docker-image-on-different-devices>__
    4. Run samples in the Docker image <#running-samples-in-docker-image>__

Getting a Prebuilt Image from Provided Sources ++++++++++++++++++++++++++++++++++++++++++++++

You can find prebuilt images on:

  • Docker Hub <https://hub.docker.com/u/openvino>__
  • Red Hat Quay.io <https://quay.io/organization/openvino>__
  • Red Hat Ecosystem Catalog (runtime image) <https://catalog.redhat.com/software/containers/intel/openvino-runtime/606ff4d7ecb5241699188fb3>__
  • Red Hat Ecosystem Catalog (development image) <https://catalog.redhat.com/software/containers/intel/openvino-dev/613a450dc9bc35f21dc4a1f7>__
  • Azure Marketplace <https://azuremarketplace.microsoft.com/en-us/marketplace/apps/intel_corporation.openvino>__

Preparing a Dockerfile ++++++++++++++++++++++

You can use the available Dockerfiles on GitHub <https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles>__ or generate a Dockerfile with your settings via DockerHub CI Framework <https://github.com/openvinotoolkit/docker_ci>__ which can generate a Dockerfile, build, test and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can also try our Tutorials <https://github.com/openvinotoolkit/docker_ci/tree/master/docs/tutorials>__ which demonstrate the usage of Docker containers with OpenVINO.

Configuring the Image for Different Devices +++++++++++++++++++++++++++++++++++++++++++

If you want to run inference on a CPU no extra configuration is needed. Go to Run the image on different devices <running-the-docker-image-on-different-devices>__ for the next step.

If you want to run inference on a GPU, follow the instructions provided in the guide on :doc:Configuration for Intel GPU <openvino_docs_install_guides_configurations_for_intel_gpu>

Running the Docker Image on Different Devices +++++++++++++++++++++++++++++++++++++++++++++

Running the Image on CPU

Run the Docker image with the following command:

.. code-block:: sh

docker run -it --rm <image_name>

Note the following:

  • Kernel reports the same information for all containers as for native application, for example, CPU, memory information.
  • All instructions that are available to host process available for process in container, including, for example, AVX2, AVX512. No restrictions.
  • Docker does not use virtualization or emulation. The process in Docker is just a regular Linux process, but it is isolated from external world on kernel level. Performance loss is minor.

Running the Image on GPU

.. note::

Only Intel® integrated graphics are supported.

Note the following:

  • GPU is not available in the container by default. You must attach it to the container.
  • Kernel driver must be installed on the host.
  • In the container, non-root user must be in the video and render groups. To add a user to the render group, follow the Configuration Guide for the Intel® Graphics Compute Runtime for OpenCL™ on Ubuntu 20.04 <https://github.com/openvinotoolkit/docker_ci/blob/master/configure_gpu_ubuntu20.md>__.

To make GPU available in the container, attach the GPU to the container using --device /dev/dri option and run the container:

  • Ubuntu 18 or RHEL 8:

    .. code-block:: sh

    docker run -it --rm --device /dev/dri <image_name>

    .. note::

    If your host system is Ubuntu 20, follow the Configuration Guide for the Intel® Graphics Compute Runtime for OpenCL™ on Ubuntu* 20.04 <https://github.com/openvinotoolkit/docker_ci/blob/master/configure_gpu_ubuntu20.md>__.

  • WSL2:

    .. code-block:: sh

    docker run -it --rm --device /dev/dxg --volume /usr/lib/wsl:/usr/lib/wsl <image_name>

    .. note::

    To launch a Linux image on WSL2, make sure that the additional System Requirements <system-requirements>__ are met.

Running Samples in Docker Image ###############################

To run the Hello Classification Sample on a specific inference device, run the following commands:

CPU:

.. code-block:: sh

docker run -it --rm <image_name> /bin/bash -c "cd ~ && omz_downloader --name googlenet-v1 --precisions FP16 && omz_converter --name googlenet-v1 --precision FP16 && curl -O https://storage.openvinotoolkit.org/data/test_data/images/car_1.bmp && python3 /opt/intel/openvino/samples/python/hello_classification/hello_classification.py public/googlenet-v1/FP16/googlenet-v1.xml car_1.bmp CPU"

GPU:

.. code-block:: sh

docker run -itu root:root --rm --device /dev/dri:/dev/dri <image_name> /bin/bash -c "omz_downloader --name googlenet-v1 --precisions FP16 && omz_converter --name googlenet-v1 --precision FP16 && curl -O https://storage.openvinotoolkit.org/data/test_data/images/car_1.bmp && python3 samples/python/hello_classification/hello_classification.py public/googlenet-v1/FP16/googlenet-v1.xml car_1.bmp GPU"

Additional Resources ###############################

  • DockerHub CI Framework <https://github.com/openvinotoolkit/docker_ci>__ for Intel® Distribution of OpenVINO™ toolkit. The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can reuse available Dockerfiles, add your layer and customize the image of OpenVINO™ for your needs.
  • Intel® Distribution of OpenVINO™ toolkit home page <https://software.intel.com/en-us/openvino-toolkit>__
  • OpenVINO Installation Selector Tool <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html>__

@endsphinxdirective