Install Guide Ubuntu 24.04 on WSL2#
This page describes steps required to install Deep Learning Streamer Pipeline Framework on Ubuntu, when hosted on a Windows machine using WSL2.
On Windows Host System#
Step 1: Update the GPU drivers#
Download and install the latest Intel® GPU drivers from intel-arc-iris-xe-graphics-windows
Step 2: Install WSL#
Open a PowerShell prompt as an Administrator and run
wsl --install
or
wsl --update
in case you have installed it before.
Visit install-ubuntu-wsl2 in case of any Ubuntu-WSL2 installation issues.
Step 3: Install Ubuntu 24.04 LTS#
Open a PowerShell prompt as an Administrator and run
wsl --install Ubuntu-24.04
wsl --set-default Ubuntu-24.04
On Linux WSL System#
Open an Ubuntu WSL terminal and follow the instructions.
Step 1: [OPTIONAL] Setup proxy#
Edit /etc/bash.bashrc and add two lines with http_proxy and https_proxy:
export http_proxy=""
export https_proxy=""
Open visudo:
sudo visudo
After the line with: Defaults env_reset, add another line with:
Defaults env_keep = "http_proxy https_proxy"
Apply changes by sourcing the file:
source /etc/profile
Step 2: Provide access to the /dev/dri/renderD12* directory#
Check if the /dev/dri/renderD12* directory exists. The output from the ls command should be similar to this:
ls -ltrah /dev/dri
total 0
drwxr-xr-x 3 root root 100 Mar 24 16:00 .
crw-rw---- 1 root render 226, 128 Mar 24 16:00 renderD128
crw-rw---- 1 root video 226, 0 Mar 24 16:00 card0
drwxr-xr-x 2 root root 80 Mar 24 16:00 by-path
drwxr-xr-x 16 root root 3.5K Mar 24 16:00 ..
If /dev/dri/renderD12* is not there, run:
sudo modprobe vgem
and check again.
Step 3: Add a user to the render group#
To use a GPU device, the user has to belong to the render group.
Follow these steps:
sudo gpasswd -a ${USER} render
newgrp render
Confirm that the list of groups to which you belong includes the render group:
groups ${USER}
Step 4: Install drivers#
cd $HOME
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | sudo gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
echo 'deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu noble unified' | sudo tee /etc/apt/sources.list.d/intel.gpu.noble.list
sudo apt update
sudo apt-get install -y libze-dev intel-opencl-icd intel-media-va-driver-non-free libmfx1 libvpl2 libegl-mesa0 libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all
Step 5: Add OpenVINO™ Toolkit and Deep Learning Streamer repositories#
cd $HOME
sudo -E wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/intel-gpg-archive-keyring.gpg > /dev/null
sudo -E wget -O- https://apt.repos.intel.com/edgeai/dlstreamer/GPG-PUB-KEY-INTEL-DLS.gpg | sudo tee /usr/share/keyrings/dls-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/dls-archive-keyring.gpg] https://apt.repos.intel.com/edgeai/dlstreamer/ubuntu24 ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-dlstreamer.list
sudo bash -c 'echo "deb [signed-by=/usr/share/keyrings/intel-gpg-archive-keyring.gpg] https://apt.repos.intel.com/openvino/2025 ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2025.list'
Step 6: Install Deep Learning Streamer Pipeline Framework#
sudo apt update
sudo apt install intel-dlstreamer
Step 7: Download the yolo11s model#
If you want to execute sample pipelines, download the yolo11s model as the sample one for these pipelines:
mkdir $HOME/models
export MODELS_PATH=$HOME/models
sudo apt install -y python3.12-venv
/opt/intel/dlstreamer/samples/download_public_models.sh yolo11s coco128
Step 8: Execute sample pipelines#
The Deep Learning Streamer Framework is ready to use. Now you can source the environment setup:
source /opt/intel/dlstreamer/scripts/setup_dls_env.sh
and execute a sample pipeline with inference on the CPU:
/opt/intel/dlstreamer/scripts/hello_dlstreamer.sh --device=CPU
NOTE: There is no current support for Video Acceleration API (VA-API) within WSL.
* Other names and brands may be claimed as the property of others.