Deep Learning Streamer (DL Streamer) Hardware accelerated video analytics pipelines — CPU, GPU and NPU, from a single line of code to production grade edge AI Get Started • Run Your Pipeline • Samples • Elements • Documentation • Contributing 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 . 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 Quick Start Installation Step 1 — Install GPU/NPU drivers (required for Docker and native install) [安装/使用命令见下方] This script detects your Intel GPU/NPU, installs the correct drivers for Ubuntu 22.04 or 24.04, and adds your user