[HW Accel Support]: with Beelink SER5 #21958
Replies: 13 comments 27 replies
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I can see your main issue is camera connectivity ("Connection refused", "No route to host", "404 Stream Not Found"), not hardware acceleration. Let's address both: Camera Connection Issues (Primary Problem)Your ffmpeg logs show network connectivity problems with your cameras, not GPU issues. The errors indicate:
First, verify your camera streams are accessible and the RTSP paths are correct for your Reolink models. AMD GPU Hardware Acceleration SetupFor your AMD Ryzen 5 5500U with integrated graphics, you need to use the ROCm-enabled Frigate image.(1) Step 1: Update Docker ComposeChange your docker-compose.yml image line from: image: ghcr.io/blakeblackshear/frigate:stableto: image: ghcr.io/blakeblackshear/frigate:stable-rocmStep 2: Add Device AccessAdd these lines to your docker-compose.yml under the frigate service: devices:
- /dev/dri
- /dev/kfdStep 3: Update Frigate ConfigIn your config.yml, change the ffmpeg section to: ffmpeg:
hwaccel_args: preset-vaapiHowever, note that AMD ROCm support is primarily for object detection via ONNX, not video decoding. For video decoding on AMD integrated GPUs, VAAPI is the correct approach (which you already have configured).(1) Step 4: Install AMD Drivers on HostOn your Debian host, you need to install the mesa VAAPI drivers: sudo apt install mesa-va-drivers vainfoThen verify with: vainfo
ls /dev/driStep 5: Environment Variable (if needed)You may need to add this environment variable to your docker-compose.yml: environment:
LIBVA_DRIVER_NAME: "radeonsi"Complete Updated docker-compose.ymlservices:
frigate:
container_name: frigate
restart: unless-stopped
stop_grace_period: 30s
image: ghcr.io/blakeblackshear/frigate:stable-rocm
shm_size: "106mb"
devices:
- /dev/dri
- /dev/kfd
volumes:
- ./config:/config
- ./storage:/media/frigate
- type: tmpfs
target: /tmp/cache
tmpfs:
size: 1000000000
ports:
- "8971:8971"
- "8554:8554"
- "5000:5000"
- "1984:1984"
environment:
PLUS_API_KEY: key_here
LIBVA_DRIVER_NAME: "radeonsi"Next Steps
Once cameras are connecting properly and hardware acceleration is working, you can proceed with Frigate+ model setup. 📚 Sources: Have another question? Just tag @inkeep. |
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After all that, I get 502 Bad Gateway. |
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I reverted to the docker compose file I started with, then reverted to frigate config I started with, and now I have my connectivity back, but obviously no GPU detection. Back to square one. |
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Current Frigate logs: 2026-02-10 15:40:12.465618513 [INFO] Preparing Frigate... |
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Current Go2rtc logs: Type info |
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Performed Step 1 as above and still have use of cameras, and access through home assistant as normal. |
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As soon as I add either - /dev/kfd under devices, or anything other than cpu under detectors, it breaks. |
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When I run docker exec -it frigate /opt/rocm/bin/rocminfo, I get this: ROCk module is loadedHSA System AttributesRuntime Version: 1.14 ==========
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I have this after adding ONNX detectors. 2026-02-10 17:49:41.917107727 [INFO] Preparing Frigate... |
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It's acting as though there is a data bottleneck. Very slow reaction when interacting with the server. |
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The core issue is that you've defined ONNX detectors without specifying a model configuration. The error Required Model Configuration for ONNXWhen using ONNX detectors with AMD ROCm, you must configure a model. Based on your hardware (AMD Ryzen 5 5500U with Radeon Graphics), the ONNX detector will automatically use ROCm when available in the Add this to your Frigate config: model:
model_type: yolonas
width: 320
height: 320
input_pixel_format: bgr
input_tensor: nchw
path: /config/yolo_nas_s.onnx
labelmap_path: /labelmap/coco-80.txtSteps to Get ONNX Working
Alternative: Use Frigate+ ModelsIf you have a Frigate+ subscription (you have detectors:
onnx:
type: onnx
model:
path: plus://<your_model_id>With Frigate+ models, you only need to specify the About Your Hardware AccelerationYour logs show Recommended Next Steps
The video dropping/black screen issues are likely related to the detector crashing due to the missing model configuration, not your hardware setup. ** 📚 Sources: Have another question? Just tag @inkeep. |
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Based on your hardware (AMD Ryzen 5 5500U with integrated Radeon Graphics) and the thread context, you're on the right track but need to configure a model for the ONNX detector to work. Key IssueThe error Recommended Configuration for Your HardwareFor AMD integrated graphics with the Since you have a Frigate+ API key configured, the simplest approach is to use a Frigate+ model. Add this to your config: detectors:
onnx:
type: onnx
model:
path: plus://<your_model_id>With Frigate+ models, you should not define model parameters except for Alternative: Self-Provided ONNX ModelIf you want to use your own ONNX model (like YOLO-NAS), you need to:
detectors:
onnx:
type: onnx
model:
model_type: yolonas
width: 320
height: 320
input_pixel_format: bgr
input_tensor: nchw
path: /config/yolo_nas_s.onnx
labelmap_path: /labelmap/coco-80.txtAbout Your Current SetupYour current docker-compose configuration with the image: ghcr.io/blakeblackshear/frigate:stable-rocm
devices:
- /dev/dri
- /dev/kfdThe Why Multiple Detectors?You currently have detectors:
onnx:
type: onnxNext Steps
The video dropping and slow performance you're experiencing is likely because the detector process keeps crashing due to the missing model configuration, not a hardware issue. 📚 Sources: Have another question? Just tag @inkeep. |
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Am I missing something here? I have been told twice to define a model after I defined a model, and I still have the same problem. user: mqtt detectors: model: cameras: Johnny5: # <------ Name the camera version: 0.16-0 |



I'd suggest you try OpenVINO, eg: