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Refactor AMD GPU support (#20239)

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* Update ROCm to 7.0.1

* Update ONNXRuntime

* Add back in

* Get basic detection working

* Use env vars

* Handle complex migraphx models

* Enable model caching

* Remove unused

* Add tip to docs
This commit is contained in:
Nicolas Mowen
2025-09-27 13:43:11 -06:00
committed by GitHub
parent e6cbc93703
commit c207009d8a
6 changed files with 72 additions and 33 deletions
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docs/docs/configuration/object_detectors.md
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@@ -555,6 +555,17 @@ $ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/
### ROCm Supported Models
:::tip
The AMD GPU kernel is known problematic especially when converting models to mxr format. The recommended approach is:
1. Disable object detection in the config.
2. Startup Frigate with the onnx detector configured, the main object detection model will be converted to mxr format and cached in the config directory.
3. Once this is finished as indicated by the logs, enable object detection in the UI and confirm that it is working correctly.
4. Re-enable object detection in the config.
:::
See [ONNX supported models](#supported-models) for supported models, there are some caveats:
- D-FINE models are not supported
@@ -781,19 +792,19 @@ To verify that the integration is working correctly, start Frigate and observe t
# Community Supported Detectors
## MemryX MX3
## MemryX MX3
This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
See the [installation docs](../frigate/installation.md#memryx-mx3) for information on configuring the MemryX hardware.
To configure a MemryX detector, simply set the `type` attribute to `memryx` and follow the configuration guide below.
### Configuration
### Configuration
To configure the MemryX detector, use the following example configuration:
To configure the MemryX detector, use the following example configuration:
#### Single PCIe MemryX MX3
#### Single PCIe MemryX MX3
```yaml
detectors:
@@ -819,7 +830,7 @@ detectors:
device: PCIe:2
```
### Supported Models
### Supported Models
MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
@@ -833,9 +844,9 @@ The input size for **YOLO-NAS** can be set to either **320x320** (default) or **
- The default size of **320x320** is optimized for lower CPU usage and faster inference times.
##### Configuration
##### Configuration
Below is the recommended configuration for using the **YOLO-NAS** (small) model with the MemryX detector:
Below is the recommended configuration for using the **YOLO-NAS** (small) model with the MemryX detector:
```yaml
detectors:
@@ -857,13 +868,13 @@ model:
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
```
#### YOLOv9
#### YOLOv9
The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
##### Configuration
Below is the recommended configuration for using the **YOLOv9** (small) model with the MemryX detector:
Below is the recommended configuration for using the **YOLOv9** (small) model with the MemryX detector:
```yaml
detectors:
@@ -872,7 +883,7 @@ detectors:
device: PCIe:0
model:
model_type: yolo-generic
model_type: yolo-generic
width: 320 # (Can be set to 640 for higher resolution)
height: 320 # (Can be set to 640 for higher resolution)
input_tensor: nchw
@@ -885,13 +896,13 @@ model:
# └── yolov9_post.onnx (optional; only if the model includes a cropped post-processing network)
```
#### YOLOX
#### YOLOX
The model is sourced from the [OpenCV Model Zoo](https://github.com/opencv/opencv_zoo) and precompiled to DFP.
##### Configuration
##### Configuration
Below is the recommended configuration for using the **YOLOX** (small) model with the MemryX detector:
Below is the recommended configuration for using the **YOLOX** (small) model with the MemryX detector:
```yaml
detectors:
@@ -912,13 +923,13 @@ model:
# ├── yolox.dfp (a file ending with .dfp)
```
#### SSDLite MobileNet v2
#### SSDLite MobileNet v2
The model is sourced from the [OpenMMLab Model Zoo](https://mmdeploy-oss.openmmlab.com/model/mmdet-det/ssdlite-e8679f.onnx) and has been converted to DFP.
##### Configuration
##### Configuration
Below is the recommended configuration for using the **SSDLite MobileNet v2** model with the MemryX detector:
Below is the recommended configuration for using the **SSDLite MobileNet v2** model with the MemryX detector:
```yaml
detectors: