Hardware acceleration

camera.ui accelerates two things: the video pipeline (decoding camera streams for detection, live view and snapshots, and encoding for streaming) and AI inference (object detection and everything built on it).

Video acceleration is automatic on every platform. camera.ui probes the hardware at runtime, picks the best available backend and falls back to software if there is none. AI accelerators are automatic too once the hardware is reachable; on Docker that means passing the device through (below).

What runs where

Platform	Video pipeline (automatic)	AI backends
macOS (desktop app, Intel & Apple Silicon)	VideoToolbox (Apple GPU + media engine)	CoreML (Apple GPU + Neural Engine), ONNX
Windows (desktop app, x64)	NVIDIA (CUDA/NVENC), Intel (QSV), and any GPU via Direct3D 11/12	OpenVINO (Intel GPU/NPU), ONNX (NVIDIA CUDA)
Linux x64 (Docker, bare-metal, Proxmox)	NVIDIA (CUDA/NVENC), Intel (VA-API/QSV), AMD (VA-API), plus Vulkan/OpenCL	OpenVINO (Intel GPU/NPU), ONNX (CUDA/TensorRT), NCNN (Vulkan), Coral, Hailo
Linux arm64 (Rockchip)	Rockchip (RKMPP)	Coral, Hailo, NCNN (CPU/NEON)

The table shows what camera.ui can use. The matching GPU driver still has to be installed on the host. A backend whose driver is missing is skipped silently, never an error.

The desktop app needs no setup: macOS ships video acceleration with the OS, Windows uses the system graphics driver. Each detection plugin logs what every model runs on (Loaded model: …) and shows it under Active Hardware in its plugin settings.

The rest of this page is for Linux and Docker, where hardware has to cross the container boundary in three steps:

1. Host driver. The kernel-side driver must be installed on the host (the only part a Docker image can never ship).
2. Device passthrough. The device node goes into the devices: section of your compose file.
3. Verify. The container tells you what actually arrived.

GPUs are matched by picking the right image flavor. AI accelerators work with any flavor: the Coral runtime is baked into every image, and the Hailo runtime ships with its plugin. The one exception is the Intel NPU, whose user-space driver is bundled in the intel flavor only.

Check your host

The cameraui/docker repository ships a host helper that diagnoses your machine and installs the drivers that need more than a package manager:

curl -fsSLO https://raw.githubusercontent.com/cameraui/docker/main/scripts/host/cameraui-host.sh
sudo bash cameraui-host.sh check     # read-only: drivers, docker runtime, device nodes
sudo bash cameraui-host.sh nvidia    # install the NVIDIA Container Toolkit
sudo bash cameraui-host.sh hailo     # build + install the Hailo PCIe kernel driver
sudo bash cameraui-host.sh coral     # install the gasket/apex driver (PCIe/M.2 Coral)

check prints a ✓/✗ line for every accelerator class with the fix for anything missing. Run it first.

Verify from the container

On every start the container logs which device nodes it can actually see:

[setup] accelerator devices: /dev/dri ✓ · /dev/kfd ✗ · /dev/accel ✗ · /dev/apex_0 ✓ · /dev/hailo0 ✗ · /dev/bus/usb ✗ · nvidia ✗

If a device shows ✗ here but exists on the host, it is missing from your compose devices: list. The detection plugins additionally log what they found (Available devices: …) and what each model actually runs on (Loaded model: … (AUTO -> CPU)), and show it under Active Hardware in their plugin settings.

Quick reference

Hardware	Accelerates	Host driver	Pass to the container
Intel / AMD GPU	Video (VA-API/QSV) + OpenCL/Vulkan inference	in the kernel (i915/amdgpu)	/dev/dri
NVIDIA GPU	Video (NVENC/NVDEC) + CUDA	NVIDIA driver + Container Toolkit	via the toolkit runtime
Coral Edge TPU (PCIe/M.2)	Object detection	gasket/apex (coral command)	/dev/apex_0
Coral Edge TPU (USB)	Object detection	none	/dev/bus/usb
Hailo-8 / Hailo-8L	Object detection	hailo_pci + firmware (hailo command)	/dev/hailo0
Intel NPU (Core Ultra)	OpenVINO inference	intel_vpu kernel module + firmware	/dev/accel (intel flavor)
AMD ROCm	Compute	ROCm stack	/dev/kfd + /dev/dri

Intel / AMD GPU

The driver is part of the kernel, nothing to install on the host. Use the intel or amd flavor and pass /dev/dri:

    devices:
      - /dev/dri:/dev/dri

Older Intel iGPUs (Gen8 to Gen11, e.g. UHD 630)

Intel's current OpenCL runtime only covers Gen12 and newer. The intel flavor therefore bundles Intel's legacy runtime alongside it, so OpenVINO and OpenCL inference also work on Gen8 to Gen11 iGPUs (Broadwell through Comet Lake). Only chips older than Gen8 (Haswell and earlier) have no OpenCL runtime at all: there inference falls back to CPU, and a Coral or Hailo is the way to fast detection. Video decoding is fully accelerated on all of them either way.

NVIDIA GPU

Two host-side pieces: the NVIDIA driver (e.g. sudo ubuntu-drivers install) and the Container Toolkit, which injects the driver libraries into containers. sudo bash cameraui-host.sh nvidia installs the toolkit and registers it with Docker. Then use the nvidia flavor override from the Docker page. Setting NVIDIA_DRIVER_CAPABILITIES=all also enables the OpenCL and Vulkan paths the detection plugins can use.

Outside Docker (bare-metal Linux, Windows), CUDA detection with the ONNX plugin additionally needs the CUDA 13 libraries and cuDNN 9 installed system-wide from NVIDIA. In Docker, the nvidia image ships them and needs an NVIDIA driver 580 or newer; the nvidia-cuda12 image keeps CUDA 12 for older drivers and pairs with the ONNX Legacy plugin. The nvidia-tensorrt image additionally ships the TensorRT runtime for the ONNX plugin's tensorrt provider, which roughly doubles detection throughput at the cost of about twice the VRAM; outside Docker, install TensorRT 10 yourself if you want it. The line between the two runs at Turing: GTX 16xx / RTX 20xx and newer use the ONNX plugin on the CUDA 13 images, GTX 900 and 10xx (Maxwell, Pascal, Volta) only work with the ONNX Legacy plugin on nvidia-cuda12, and RTX 50xx runs exclusively on the CUDA 13 side. Cards older than GTX 900 are not supported.

Coral Edge TPU

The runtime library (libedgetpu) is already in every image flavor. Only the host side differs by form factor:

• PCIe / M.2. Needs the gasket/apex kernel driver. sudo bash cameraui-host.sh coral installs it (a maintained community build, since Google's original package no longer compiles on kernels ≥ 6.13). Then pass the device:

      devices:
        - /dev/apex_0:/dev/apex_0

• USB. No driver needed, just pass the USB bus:

      devices:
        - /dev/bus/usb:/dev/bus/usb

Without a reachable TPU the Coral plugin runs on CPU and says so in its log. Check its Available devices: line if detection seems slow.

To see which device detection actually runs on, run the benchmark in Metrics → Cameras: its per-camera page names the runtime, the model and the device. See What detection costs.

Hailo-8 / Hailo-8L

The kernel driver is built from source on the host: sudo bash cameraui-host.sh hailo installs build dependencies, compiles the driver, installs the firmware and udev rules. Reboot afterwards so the firmware loads, then pass the device:

    devices:
      - /dev/hailo0:/dev/hailo0

The userspace runtime ships inside the Hailo plugin.

Intel NPU (Core Ultra)

The intel flavor bundles the NPU user-space driver (Level Zero + the Intel NPU UMD and compiler); other flavors do not. On the host you need the intel_vpu kernel module (upstream since Linux 6.8) and its firmware; if /dev/accel is missing, see intel/linux-npu-driver. Then pass the device through:

    devices:
      - /dev/accel:/dev/accel

The OpenVINO plugin then lists NPU in its Available devices: log line and its device setting.