Advanced Installation - Compilation From Source#

NOTE: Installation of Deep Learning Streamer Pipeline Framework from pre-built Debian packages using one-click script is the easiest approach.

The instructions below are intended for building Deep Learning Streamer Pipeline Framework from the source code provided in

Open Edge Platform repository.

Step 1: Install prerequisites (only for Ubuntu)#

Follow the instructions in the prerequisites section.

Step 2: Install build dependencies#

sudo apt-get update && \
sudo apt-get install -y wget vainfo xz-utils python3-pip python3-gi gcc-multilib libglib2.0-dev \
    flex bison autoconf automake libtool libogg-dev make g++ libva-dev yasm libglx-dev libdrm-dev \
    python-gi-dev python3-dev unzip libgflags-dev libcurl4-openssl-dev \
    libgirepository1.0-dev libx265-dev libx264-dev libde265-dev gudev-1.0 libusb-1.0 nasm python3-venv \
    libcairo2-dev libxt-dev libgirepository1.0-dev libgles2-mesa-dev wayland-protocols \
    libssh2-1-dev cmake git valgrind numactl libvpx-dev libopus-dev libsrtp2-dev libxv-dev \
    linux-libc-dev libpmix2t64 libhwloc15 libhwloc-plugins libxcb1-dev libx11-xcb-dev \
    ffmpeg libpaho-mqtt-dev libopencv-dev libpostproc-dev libavfilter-dev libavdevice-dev \
    libswscale-dev libswresample-dev libavutil-dev libavformat-dev libavcodec-dev libtbb12 libxml2-dev \
    ocl-icd-opencl-dev

sudo apt-get update && \
sudo apt-get install -y wget vainfo xz-utils python3-pip python3-gi gcc-multilib libglib2.0-dev \
    flex bison autoconf automake libtool libogg-dev make g++ libva-dev yasm libglx-dev libdrm-dev \
    python-gi-dev python3-dev unzip libgflags-dev \
    libgirepository1.0-dev libx265-dev libx264-dev libde265-dev gudev-1.0 libusb-1.0 nasm python3-venv \
    libcairo2-dev libxt-dev libgirepository1.0-dev libgles2-mesa-dev wayland-protocols libcurl4-openssl-dev \
    libssh2-1-dev cmake git valgrind numactl libvpx-dev libopus-dev libsrtp2-dev libxv-dev \
    linux-libc-dev libpmix2 libhwloc15 libhwloc-plugins libxcb1-dev libx11-xcb-dev \
    ffmpeg libpaho-mqtt-dev libpostproc-dev libavfilter-dev libavdevice-dev \
    libswscale-dev libswresample-dev libavutil-dev libavformat-dev libavcodec-dev libtbb12  libxml2-dev ocl-icd-opencl-dev \
    opencl-headers

sudo dnf install -y \
    https://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm \
    https://download1.rpmfusion.org/nonfree/fedora/rpmfusion-nonfree-release-$(rpm -E %fedora).noarch.rpm
sudo dnf install -y wget libva-utils xz python3-pip python3-gobject gcc gcc-c++ glibc-devel glib2-devel \
    flex bison autoconf automake libtool libogg-devel make libva-devel yasm mesa-libGL-devel libdrm-devel \
    python3-gobject-devel python3-devel tbb gnupg2 unzip opencv-devel gflags-devel openssl-devel openssl-devel-engine \
    gobject-introspection-devel x265-devel x264-devel libde265-devel libgudev-devel libusb1 libusb1-devel nasm python3-virtualenv \
    cairo-devel cairo-gobject-devel libXt-devel mesa-libGLES-devel wayland-protocols-devel libcurl-devel which \
    libssh2-devel cmake git valgrind numactl libvpx-devel opus-devel libsrtp-devel libXv-devel paho-c-devel \
    kernel-headers pmix pmix-devel hwloc hwloc-libs hwloc-devel libxcb-devel libX11-devel libatomic intel-media-driver

sudo dnf install -y uuid libuuid-devel openssl-devel gcc gcc-c++ make curl ca-certificates librdkafka-devel libva-devel alsa-lib-devel unzip glibc libstdc++ libgcc cmake sudo pkgconf pkgconf-pkg-config ocl-icd-devel libva-intel-media-driver python3-devel libXaw-devel ncurses-devel libva2 intel-compute-runtime intel-opencl intel-level-zero-gpu intel-ocloc-devel nasm

Step 3: Set up a Python environment#

Create a Python virtual environment and install required Python packages:

python3 -m venv ~/python3venv
source ~/python3venv/bin/activate

pip install --upgrade pip==24.0
pip install meson==1.4.1 ninja==1.11.1.1

Step 4: Clone Deep Learning Streamer repository#

cd ~
git clone --recursive https://github.com/open-edge-platform/dlstreamer.git
cd dlstreamer

Step 5: Install OpenVINO™ Toolkit#

cd ~/dlstreamer
sudo ./scripts/install_dependencies/install_openvino.sh

In case of any problems with the installation scripts, Follow OpenVINO™ Toolkit instruction guide here to install OpenVINO™ on Linux.

  • Environment: Runtime

  • Operating System: Linux

  • Version: Latest

  • Distribution: OpenVINO™ Archives

After successful OpenVINO™ Toolkit package installation, run the following commands to install OpenVINO™ Toolkit dependencies and enable OpenVINO™ Toolkit development environment:

sudo -E /opt/intel/openvino_2026/install_dependencies/install_openvino_dependencies.sh
source /opt/intel/openvino_2026/setupvars.sh

wget https://storage.openvinotoolkit.org/repositories/openvino/packages/2026.1/linux/openvino_toolkit_ubuntu24_2026.1.0.21367.63e31528c62_x86_64.tgz
tar -xvzf openvino_toolkit_ubuntu24_2026.1.0.21367.63e31528c62_x86_64.tgz
sudo mv openvino_toolkit_ubuntu24_2026.1.0.21367.63e31528c62_x86_64.tgz /opt/intel/openvino_2026.1.0
cd /opt/intel/openvino_2026.1.0/
sudo -E python3 -m pip install -r ./python/requirements.txt
cd /opt/intel
sudo ln -s openvino_2026.1.0 openvino_2026

(Optional) Step 6: Install OpenVINO™ GenAI (only for Ubuntu)#

To use gvagenai element you need to install the OpenVINO GenAI archive package.

wget -O- https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2026.1/linux/openvino_genai_ubuntu24_2026.1.0.0_x86_64.tar.gz | tar -xz &&
sudo mv openvino_genai_ubuntu24_2026.1.0.0_x86_64 /opt/intel/openvino_genai
source /opt/intel/openvino_genai/setupvars.sh

wget -O- https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2026.1/linux/openvino_genai_ubuntu22_2026.1.0.0_x86_64.tar.gz | tar -xz &&
sudo mv openvino_genai_ubuntu22_2026.1.0.0_x86_64 /opt/intel/openvino_genai
source /opt/intel/openvino_genai/setupvars.sh

Step 7: Build Deep Learning Streamer#

To build DL Streamer is it recommended to use the provided makefile for ease of use:

make build

Running this command will build any major missing dependencies and then compile DL Streamer itself.

Step 8: Install Deep Learning Streamer (optional)#

After building DL Streamer you can install it on your local system by running:

sudo -E make install

Step 9: Set up environment#

Set up the required environment variables:

export LIBVA_DRIVER_NAME=iHD
export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH"
export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:/opt/intel/dlstreamer/rdkafka/lib:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib:$HOME/dlstreamer/build/deps/opencv-bin/lib:$HOME/dlstreamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH"
export LIBVA_DRIVERS_PATH="/usr/lib/x86_64-linux-gnu/dri"
export GST_VA_ALL_DRIVERS="1"
export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/dlstreamer/build/intel64/Release/bin:$HOME/dlstreamer/build/deps/gstreamer-bin/bin:$HOME/dlstreamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH"
export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/dlstreamer/build/intel64/Release/lib/pkgconfig:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH"
export GST_PLUGIN_FEATURE_RANK=${GST_PLUGIN_FEATURE_RANK},ximagesink:MAX
export GI_TYPELIB_PATH="/opt/intel/dlstreamer/gstreamer/lib/girepository-1.0:$HOME/dlstreamer/build/src/gst/metadata/:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/girepository-1.0:/usr/lib/x86_64-linux-gnu/girepository-1.0"
export PYTHONPATH="/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:/opt/intel/dlstreamer/python:/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:$PYTHONPATH"

export LIBVA_DRIVER_NAME=iHD
export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH"
export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib:$HOME/dlstreamer/build/deps/opencv-bin/lib:$HOME/dlstreamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH"
export LIBVA_DRIVERS_PATH="/usr/lib64/dri-nonfree"
export GST_VA_ALL_DRIVERS="1"
export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/dlstreamer/build/intel64/Release/bin:$HOME/dlstreamer/build/deps/gstreamer-bin/bin:$HOME/dlstreamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH"
export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/dlstreamer/build/intel64/Release/lib/pkgconfig:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH"
export GST_PLUGIN_FEATURE_RANK=${GST_PLUGIN_FEATURE_RANK},ximagesink:MAX
export PYTHONPATH="/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:/opt/intel/dlstreamer/python:/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:$PYTHONPATH"

Enable i915 graphics driver in the system:

sudo vim /etc/default/grub
### Extend the GRUB_CMDLINE_LINUX with i915.force_probe=* ###
sudo grub2-mkconfig -o /boot/grub2/grub.cfg "$@"
sudo reboot

After a reboot, before trying the Deep Learning Streamer pipelines, you can export the following environment variables for the current terminal session (temporary solution):

export LIBVA_DRIVER_NAME=iHD
export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH"
export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib:$HOME/dlstreamer/build/deps/opencv-bin/lib:$HOME/dlstreamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH"
export LIBVA_DRIVERS_PATH="/usr/lib/dri"
export GST_VA_ALL_DRIVERS="1"
export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/dlstreamer/build/intel64/Release/bin:$HOME/dlstreamer/build/deps/gstreamer-bin/bin:$HOME/dlstreamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH"
export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/dlstreamer/build/intel64/Release/lib/pkgconfig:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH"
export GST_PLUGIN_FEATURE_RANK=${GST_PLUGIN_FEATURE_RANK},ximagesink:MAX
export PYTHONPATH="/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:/opt/intel/dlstreamer/python:/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:$PYTHONPATH"

NOTE: For a permanent solution, open \~/.bashrc and add the variables above to set up Linux to use them for every terminal session.

Step 10: Install Python dependencies (optional)#

If you intend to use Python elements or samples, you need to install the necessary dependencies using the following commands:

sudo apt-get install -y -q --no-install-recommends gcc cmake python3-full python-gi-dev python3-dev python3-pip \
    libglib2.0-dev libcairo2-dev libopencv-objdetect-dev libopencv-photo-dev libopencv-stitching-dev libopencv-video-dev \
    libopencv-calib3d-dev libopencv-core-dev libopencv-dnn-dev libgirepository1.0-dev

source ~/python3venv/bin/activate
cd ~/dlstreamer
python3 -m pip install -r requirements.txt

(Optional) Install DL Streamer ONVIF Python package#

If you want to use the dlstreamer.onvif in your own Python code, install the intel-dlstreamer wheel on top of the DL Streamer. See ONVIF sample for more details of ONVIF support.

The easiest way is to download and install the wheel directly from the GitHub Release:

pip install https://github.com/open-edge-platform/dlstreamer/releases/download/v2026.1.0/intel_dlstreamer-2026.1.0-py3-none-any.whl

Alternatively, build the wheel from the cloned sources:

pip install build
python -m build --wheel ~/dlstreamer/python
pip install ~/dlstreamer/python/dist/intel_dlstreamer-*.whl

This installs:

  • dlstreamer.onvif — ONVIF camera discovery and pipeline integration

  • All required Python dependencies (numpy, onvif-zeep, urllib3) automatically

After installation, no PYTHONPATH configuration is needed:

from dlstreamer.onvif import DlsOnvifDiscoveryEngine, discover_onvif_cameras

NOTE (advanced): As an alternative to pip, dlstreamer.onvif is also accessible via PYTHONPATH set in Step 9. This approach requires manual installation of the dependencies declared in python/pyproject.toml.