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

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 includes the Intel® Deep Learning Deployment Toolkit.

This guide provides device specifics for a Docker* image creation with Intel® Distribution of OpenVINO™ toolkit for Linux* and its further usage.

System Requirements

Target Operating Systems

  • Ubuntu* 18.04 long-term support (LTS), 64-bit
  • Ubuntu* 20.04 long-term support (LTS), 64-bit
  • CentOS* 7
  • Red Hat* Enterprise Linux* 8 (64 bit)

Host Operating Systems

  • Linux

Prebuilt images

Prebuilt images are available on:

Build a Docker* Image

You can use available Dockerfiles or generate a Dockerfile with your setting via DockerHub CI Framework. The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can also try our Tutorials which demonstrate the usage of Docker containers with Intel® Distribution of OpenVINO™ toolkit. You can find device specific steps to configure an Intel® Distribution of OpenVINO™ toolkit Dockerfile below.

Use Docker* Image for CPU

  • 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 penalty is small.

Configure a Docker* Image for CPU

You don't need to do specific steps to configure an Intel® Distribution of OpenVINO™ toolkit Dockerfile for CPU. You can use available Dockerfiles or generate a Dockerfile with your setting via DockerHub CI Framework.

Run the Docker* Image for CPU

Run the image with the following command:

docker run -it --rm <image_name>

Use a Docker* Image for GPU

Configure a Docker* Image for GPU

NOTE: Only Intel® integrated graphics are supported.

Prerequisites:

To configure a OpenVINO Docker* image with access to GPU, add the following commands to a Dockerfile:

Ubuntu 18.04/20.04:

WORKDIR /tmp/opencl
RUN useradd -ms /bin/bash -G video,users openvino && \
chown openvino -R /home/openvino
RUN apt-get update && \
apt-get install -y --no-install-recommends ocl-icd-libopencl1 && \
rm -rf /var/lib/apt/lists/* && \
curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-gmmlib_19.3.2_amd64.deb" --output "intel-gmmlib_19.3.2_amd64.deb" && \
curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-igc-core_1.0.2597_amd64.deb" --output "intel-igc-core_1.0.2597_amd64.deb" && \
curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-igc-opencl_1.0.2597_amd64.deb" --output "intel-igc-opencl_1.0.2597_amd64.deb" && \
curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-opencl_19.41.14441_amd64.deb" --output "intel-opencl_19.41.14441_amd64.deb" && \
curl -L "https://github.com/intel/compute-runtime/releases/download/19.41.14441/intel-ocloc_19.41.14441_amd64.deb" --output "intel-ocloc_19.04.12237_amd64.deb" && \
dpkg -i /tmp/opencl/*.deb && \
ldconfig && \
rm /tmp/opencl

or you can use the installation script install_NEO_OCL_driver.sh if you previously installed OpenVINO in the Dockerfile, where INTEL_OPENCL is the variable to store the default version of Intel® Graphics Compute Runtime for OpenCL™ Driver:

WORKDIR /tmp/opencl
RUN useradd -ms /bin/bash -G video,users openvino && \
chown openvino -R /home/openvino
# Please use `20.35.17767` for 10th generation Intel® Core™ processor (formerly Ice Lake) or 11th generation Intel® Core™ processor (formerly Tiger Lake)
ARG INTEL_OPENCL=19.41.14441
WORKDIR ${INTEL_OPENVINO_DIR}/install_dependencies
RUN ./install_NEO_OCL_driver.sh --no_numa -y --install_driver ${INTEL_OPENCL} && \
rm -rf /var/lib/apt/lists/*

CentOS 7/RHEL 8:

WORKDIR /tmp/opencl
RUN useradd -ms /bin/bash -G video,users openvino && \
chown openvino -R /home/openvino
RUN groupmod -g 44 video
RUN yum update -y && yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm && \
yum update -y && yum install -y ocl-icd ocl-icd-devel && \
yum clean all && rm -rf /var/cache/yum && \
curl -L https://sourceforge.net/projects/intel-compute-runtime/files/19.41.14441/centos-7/intel-gmmlib-19.3.2-1.el7.x86_64.rpm/download -o intel-gmmlib-19.3.2-1.el7.x86_64.rpm && \
curl -L https://sourceforge.net/projects/intel-compute-runtime/files/19.41.14441/centos-7/intel-gmmlib-devel-19.3.2-1.el7.x86_64.rpm/download -o intel-gmmlib-devel-19.3.2-1.el7.x86_64.rpm && \
curl -L https://sourceforge.net/projects/intel-compute-runtime/files/19.41.14441/centos-7/intel-igc-core-1.0.2597-1.el7.x86_64.rpm/download -o intel-igc-core-1.0.2597-1.el7.x86_64.rpm && \
curl -L https://sourceforge.net/projects/intel-compute-runtime/files/19.41.14441/centos-7/intel-igc-opencl-1.0.2597-1.el7.x86_64.rpm/download -o intel-igc-opencl-1.0.2597-1.el7.x86_64.rpm && \
curl -L https://sourceforge.net/projects/intel-compute-runtime/files/19.41.14441/centos-7/intel-igc-opencl-devel-1.0.2597-1.el7.x86_64.rpm/download -o intel-igc-opencl-devel-1.0.2597-1.el7.x86_64.rpm && \
curl -L https://sourceforge.net/projects/intel-compute-runtime/files/19.41.14441/centos-7/intel-opencl-19.41.14441-1.el7.x86_64.rpm/download -o intel-opencl-19.41.14441-1.el7.x86_64.rpm \
rpm -ivh ${TEMP_DIR}/*.rpm && \
ldconfig && \
rm -rf ${TEMP_DIR} && \
yum remove -y epel-release

or you can use the installation script install_NEO_OCL_driver.sh if you previously installed OpenVINO in the Dockerfile, where INTEL_OPENCL is the variable to store the default version of Intel® Graphics Compute Runtime for OpenCL™ Driver:

WORKDIR /tmp/opencl
RUN useradd -ms /bin/bash -G video,users openvino && \
chown openvino -R /home/openvino
RUN groupmod -g 44 video
# Please use `20.35.17767` for 10th generation Intel® Core™ processor (formerly Ice Lake) or 11th generation Intel® Core™ processor (formerly Tiger Lake)
ARG INTEL_OPENCL=19.41.14441
WORKDIR ${INTEL_OPENVINO_DIR}/install_dependencies
RUN ./install_NEO_OCL_driver.sh --no_numa -y --install_driver ${INTEL_OPENCL} && \
yum clean all && rm -rf /var/cache/yum && \
yum remove -y epel-release

Run the Docker* Image for GPU

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

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.

Use a Docker* Image for Intel® Neural Compute Stick 2

Configure and Run the Docker* Image for Intel® Neural Compute Stick 2

Known limitations:

  • Intel® Neural Compute Stick 2 device changes its VendorID and DeviceID during execution and each time looks for a host system as a brand new device. It means it cannot be mounted as usual.
  • UDEV events are not forwarded to the container by default it does not know about device reconnection.
  • Only one device per host is supported.

Use one of the following options as Possible solutions for Intel® Neural Compute Stick 2:

Option 1

  1. Get rid of UDEV by rebuilding libusb without UDEV support in the Docker* image (add the following commands to a Dockerfile):
    • Ubuntu 18.04/20.04:
      ARG BUILD_DEPENDENCIES="autoconf \
      automake \
      build-essential \
      libtool \
      unzip \
      udev"
      RUN apt-get update && \
      apt-get install -y --no-install-recommends ${BUILD_DEPENDENCIES} && \
      rm -rf /var/lib/apt/lists/*
      WORKDIR /opt
      RUN curl -L https://github.com/libusb/libusb/archive/v1.0.22.zip --output v1.0.22.zip && \
      unzip v1.0.22.zip
      WORKDIR /opt/libusb-1.0.22
      RUN ./bootstrap.sh && \
      ./configure --disable-udev --enable-shared && \
      make -j4
      WORKDIR /opt/libusb-1.0.22/libusb
      RUN /bin/mkdir -p '/usr/local/lib' && \
      /bin/bash ../libtool --mode=install /usr/bin/install -c libusb-1.0.la '/usr/local/lib' && \
      /bin/mkdir -p '/usr/local/include/libusb-1.0' && \
      /usr/bin/install -c -m 644 libusb.h '/usr/local/include/libusb-1.0' && \
      /bin/mkdir -p '/usr/local/lib/pkgconfig'
      WORKDIR /opt/libusb-1.0.22/
      RUN /usr/bin/install -c -m 644 libusb-1.0.pc '/usr/local/lib/pkgconfig' && \
      cp /opt/intel/openvino_2021/deployment_tools/inference_engine/external/97-myriad-usbboot.rules /etc/udev/rules.d/ && \
      ldconfig
    • CentOS 7:
      ARG BUILD_DEPENDENCIES="autoconf \
      automake \
      libtool \
      unzip \
      udev"
      RUN yum update -y && yum install -y ${BUILD_DEPENDENCIES} && \
      yum group install -y "Development Tools" && \
      yum clean all && rm -rf /var/cache/yum
      WORKDIR /opt
      RUN curl -L https://github.com/libusb/libusb/archive/v1.0.22.zip --output v1.0.22.zip && \
      unzip v1.0.22.zip && rm -rf v1.0.22.zip
      WORKDIR /opt/libusb-1.0.22
      RUN ./bootstrap.sh && \
      ./configure --disable-udev --enable-shared && \
      make -j4
      WORKDIR /opt/libusb-1.0.22/libusb
      RUN /bin/mkdir -p '/usr/local/lib' && \
      /bin/bash ../libtool --mode=install /usr/bin/install -c libusb-1.0.la '/usr/local/lib' && \
      /bin/mkdir -p '/usr/local/include/libusb-1.0' && \
      /usr/bin/install -c -m 644 libusb.h '/usr/local/include/libusb-1.0' && \
      /bin/mkdir -p '/usr/local/lib/pkgconfig' && \
      printf "\nexport LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:/usr/local/lib\n" >> /opt/intel/openvino_2021/bin/setupvars.sh
      WORKDIR /opt/libusb-1.0.22/
      RUN /usr/bin/install -c -m 644 libusb-1.0.pc '/usr/local/lib/pkgconfig' && \
      cp /opt/intel/openvino_2021/deployment_tools/inference_engine/external/97-myriad-usbboot.rules /etc/udev/rules.d/ && \
      ldconfig
  2. Run the Docker* image:
    docker run -it --rm --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>

Option 2

Run container in the privileged mode, enable the Docker network configuration as host, and mount all devices to the container:

docker run -it --rm --privileged -v /dev:/dev --network=host <image_name>

NOTES:

  • It is not secure.
  • Conflicts with Kubernetes* and other tools that use orchestration and private networks may occur.

Use a Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs

Configure Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs

To use the Docker container for inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs:

  1. Set up the environment on the host machine, that is going to be used for running Docker*. It is required to execute hddldaemon, which is responsible for communication between the HDDL plugin and the board. To learn how to set up the environment (the OpenVINO package or HDDL package must be pre-installed), see Configuration guide for HDDL device or Configuration Guide for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
  2. Prepare the Docker* image (add the following commands to a Dockerfile).
    • Ubuntu 18.04:
      WORKDIR /tmp
      RUN apt-get update && \
      apt-get install -y --no-install-recommends \
      libboost-filesystem1.65-dev \
      libboost-thread1.65-dev \
      libjson-c3 libxxf86vm-dev && \
      rm -rf /var/lib/apt/lists/* && rm -rf /tmp/*
    • Ubuntu 20.04:
      WORKDIR /tmp
      RUN apt-get update && \
      apt-get install -y --no-install-recommends \
      libboost-filesystem-dev \
      libboost-thread-dev \
      libjson-c4 \
      libxxf86vm-dev && \
      rm -rf /var/lib/apt/lists/* && rm -rf /tmp/*
    • CentOS 7:
      WORKDIR /tmp
      RUN yum update -y && yum install -y \
      boost-filesystem \
      boost-thread \
      boost-program-options \
      boost-system \
      boost-chrono \
      boost-date-time \
      boost-regex \
      boost-atomic \
      json-c \
      libXxf86vm-devel && \
      yum clean all && rm -rf /var/cache/yum
  3. Run hddldaemon on the host in a separate terminal session using the following command:
    $HDDL_INSTALL_DIR/hddldaemon

Run the Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs

To run the built Docker* image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, use the following command:

docker run -it --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp <image_name>

NOTES:

  • The device /dev/ion needs to be shared to be able to use ion buffers among the plugin, hddldaemon and the kernel.
  • Since separate inference tasks share the same HDDL service communication interface (the service creates mutexes and a socket file in /var/tmp), /var/tmp needs to be mounted and shared among them.

In some cases, the ion driver is not enabled (for example, due to a newer kernel version or iommu (Input-Output Memory Management Unit) incompatibility). lsmod | grep myd_ion returns empty output. To resolve, use the following command:

docker run -it --rm --net=host -v /var/tmp:/var/tmp –-ipc=host <image_name>

NOTES:

  • When building Docker images, create a user in the Dockerfile that has the same UID(User Identifier) and GID(Group Identifier) as the user which runs hddldaemon on the host.
  • Run the application in the Docker image with this user.
  • Alternatively, you can start hddldaemon with the root user on host, but this approach is not recommended.

Run Demos in the Docker* Image

To run the Security Barrier Camera Demo on a specific inference device, run the following commands with the root privileges (additional third-party dependencies will be installed):

CPU:

docker run -itu root:root --rm <image_name>
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d CPU -sample-options -no_show"

GPU:

docker run -itu root:root --rm --device /dev/dri:/dev/dri <image_name>
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d GPU -sample-options -no_show"

MYRIAD:

docker run -itu root:root --rm --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d MYRIAD -sample-options -no_show"

HDDL:

docker run -itu root:root --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp <image_name>
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d HDDL -sample-options -no_show"

Use a Docker* Image for FPGA

Intel will be transitioning to the next-generation programmable deep-learning solution based on FPGAs in order to increase the level of customization possible in FPGA deep-learning. As part of this transition, future standard releases (i.e., non-LTS releases) of Intel® Distribution of OpenVINO™ toolkit will no longer include the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA.

Intel® Distribution of OpenVINO™ toolkit 2020.3.X LTS release will continue to support Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. For questions about next-generation programmable deep-learning solutions based on FPGAs, please talk to your sales representative or contact us to get the latest FPGA updates.

For instructions for previous releases with FPGA Support, see documentation for the 2020.4 version or lower.

Troubleshooting

If you got proxy issues, please setup proxy settings for Docker. See the Proxy section in the Install the DL Workbench from Docker Hub* topic.

Additional Resources