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What is DL Streamer?

DL Streamer is an open-source media analytics framework built on GStreamer. It lets you build video and audio intelligence pipelines — from a simple object detection command line to a multi-stream, multi-sensor production deployment, all with minimal code.

• Powered by OpenVINO™ for optimized inference on Intel CPU, GPU, and NPU.
• Pipelines are described as simple strings (or Python/C++ code) and executed with full hardware acceleration.
• Ships with 30+ ready-to-run samples covering detection, classification, tracking, VLMs, LiDAR and more.
• Part of the Intel Open Edge Platform.

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Why DL Streamer?

Benefit	Details
One-line pipelines	Build a working detection pipeline in a single gst-launch-1.0 command
Hardware acceleration	Targets CPU, GPU, and NPU on Intel platforms from a single codebase
VLM & GenAI ready	Run Vision-Language Models (MiniCPM-V, CLIP, Whisper) in a GStreamer pipeline
GstAnalytics compliance	Supports the GStreamer industry metadata standard for interoperability with other GStreamer-based tools
Messaging integration	Publish inference results directly to MQTT or Kafka with built-in elements — no extra code required
Python-first extensibility	Add custom logic as Python callbacks or full Python GStreamer elements — no C++ required
Multi-stream, multi-sensor	Mux/demux dozens of RTSP streams, LiDAR frames, and radar point clouds in one process
Geti™, Ultralytics & HuggingFace support	Deploy models from Geti™ Studio, Ultralytics, Hugging Face, or any ONNX/OpenVINO IR model directly

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Quick Start - Installation

Step 1 — Install GPU/NPU drivers (required for Docker and native install)

cd ~
wget https://raw.githubusercontent.com/open-edge-platform/dlstreamer/main/scripts/DLS_install_prerequisites.sh
chmod +x DLS_install_prerequisites.sh
./DLS_install_prerequisites.sh

This script detects your Intel GPU/NPU, installs the correct drivers for Ubuntu 22.04 or 24.04, and adds your user to the required groups. Use --reinstall-npu-driver=yes to force-reinstall the NPU driver. Run ./DLS_install_prerequisites.sh --help for all options.

Step 2 — Install DL Streamer

Option A — Docker (recommended, zero setup):

# Run once on the host to allow X11 forwarding from containers
xhost +local:docker

docker run -it --rm \
  --device /dev/dri \
  --group-add $(stat -c "%g" /dev/dri/render*) \
  -e DISPLAY=$DISPLAY \
  -e XDG_RUNTIME_DIR=/tmp \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  intel/dlstreamer:latest

To use the NPU, also add --device /dev/accel --group-add $(stat -c "%g" /dev/accel/accel*) -e ZE_ENABLE_ALT_DRIVERS=libze_intel_npu.so to the docker run command.

Option B — Native install (Ubuntu 24.04):

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 ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-openvino.list'
sudo apt update && sudo apt-get install -y intel-dlstreamer

Full installation guide: Install Guide for Ubuntu | Windows

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Quick Start - Run Your Pipeline

Step 1 — Set up models and environment (inside the container or on a native install):

cd ~
python3 -m venv .dls-venv && source .dls-venv/bin/activate
pip install openvino==2026.2.0 nncf==3.0.0 ultralytics==8.4.57
python3 /opt/intel/dlstreamer/scripts/download_models/download_ultralytics_models.py \
  --model yolo11n.pt \
  --outdir ~/models/yolo11n \
  --int8
source /opt/intel/dlstreamer/scripts/setup_dls_env.sh

Step 2 — Run the pipeline. Change device=GPU to device=CPU or device=NPU — no other code changes needed.

gst-launch-1.0 \
  urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 ! \
  decodebin3 ! \
  gvadetect model=~/models/yolo11n/yolo11n_int8_openvino_model/yolo11n.xml device=GPU ! \
  queue ! \
  gvawatermark ! \
  gvafpscounter ! \
  videoconvert ! autovideosink sync=false

Output to JSON (works everywhere, including headless Docker):

gst-launch-1.0 \
  urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 ! \
  decodebin3 ! \
  gvadetect model=~/models/yolo11n/yolo11n_int8_openvino_model/yolo11n.xml device=GPU ! \
  queue ! \
  gvafpscounter ! \
  gvametaconvert format=json ! \
  gvametapublish file-format=json-lines file-path=output.json ! fakesink async=false

Python API

Create a file detect.py:

import gi
gi.require_version("Gst", "1.0")
from gi.repository import Gst
import os

Gst.init([])

video_url = "https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4"
model = os.path.expanduser("~/models/yolo11n/yolo11n_int8_openvino_model/yolo11n.xml")
pipeline = Gst.parse_launch(f"""
    urisourcebin buffer-size=4096 uri={video_url} !
    decodebin3 !
    gvadetect model={model} device=GPU !
    queue !
    gvafpscounter !
    gvametaconvert format=json !
    gvametapublish file-format=json-lines file-path=output_from_python.json ! fakesink async=false
""")

pipeline.set_state(Gst.State.PLAYING)
bus = pipeline.get_bus()
bus.timed_pop_filtered(Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR)
pipeline.set_state(Gst.State.NULL)

Then run it and wait for results:

python3 detect.py
# Detection results are written to output_from_python.json as the pipeline processes frames
# Each line is a JSON object with detected objects, labels, and bounding boxes
cat output_from_python.json

If any Python dependencies are missing, refer to the Python dependencies install guide.

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DL Streamer Elements

Category	Key Elements
Inference	gvadetect · gvaclassify · gvainference · gvagenai · gvaaudiotranscribe
Analytics	gvatrack · gvaanalytics · gvastreammux / gvastreamdemux · gvamotiondetect
Output	gvawatermark · gvametaconvert · gvametapublish · gvafpscounter
3D / Sensors	g3dlidarparse · g3dinference · g3dradarprocess

Full elements reference →

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Samples

30+ samples across Python, C++, and gst-launch command lines:

Category	Samples
Detection	YOLO detection, Face detection + classification, Depth estimation
Segmentation & Pose	Instance segmentation, Human pose estimation
Tracking	Vehicle & pedestrian tracking, Vehicle counter with tripwires
VLM / GenAI	VLM video summarization, VLM alerts, VLM self-checkout
Multi-stream	Multi-camera deployment, Stream mux/demux
3D Sensors	LiDAR parsing, PointPillars 3D detection, Radar processing
Integration	ONVIF camera discovery, Geti™ model deployment, Metadata to MQTT/Kafka
Python extensibility	Custom Python GStreamer elements, Smart NVR with recording

Browse all samples →

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Supported Platforms

Hardware	CPU	GPU	NPU
Intel Core Ultra series 1–3 (Meteor / Lunar / Arrow / Panther Lake)	✅	✅	✅
Intel Arc discrete GPU (Alchemist, Battlemage)	—	✅	—
11th–13th Gen Intel Core	✅	✅	—

Operating systems: Ubuntu 22.04 / 24.04, Windows 11.

Full system requirements →

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Documentation

Resource	Link
Get Started (tutorial + install)	Get Started
Developer Guide	Developer Guide
Elements Reference	Elements Reference
API Reference	API Reference
Metadata Guide	Metadata Guide
Supported Models	Supported Models

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Contributing

We welcome contributions! Please read CONTRIBUTING.md and follow the Code Style Guide.

For security issues, see SECURITY.md.

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License

DL Streamer is licensed under the MIT License.

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