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upgrade deps (#10257)

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* upgrade web deps

* docs deps

* actions deps
This commit is contained in:
Blake Blackshear
2024-03-05 13:00:27 +00:00
committed by GitHub
parent 390403d957
commit 43c623be25
8 changed files with 814 additions and 740 deletions
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47
docs/docs/configuration/object_detectors.md
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@@ -123,9 +123,9 @@ or when using docker compose:
```yaml
services:
frigate:
...
environment:
DOWNLOAD_YOLOV8: "1"
---
environment:
DOWNLOAD_YOLOV8: "1"
```
When this variable is set then frigate will at startup fetch [yolov8.small.models.tar.gz](https://github.com/harakas/models/releases/download/yolov8.1-1.1/yolov8.small.models.tar.gz) and extract it into the `/config/model_cache/yolov8/` directory.
@@ -313,7 +313,7 @@ frigate:
### Configuration Parameters
The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration.md#nvidia-gpu) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration.md#nvidia-gpus) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
The TensorRT detector uses `.trt` model files that are located in `/config/model_cache/tensorrt` by default. These model path and dimensions used will depend on which model you have generated.
@@ -484,11 +484,10 @@ When using docker compose:
```yaml
services:
frigate:
...
devices:
- /dev/dri
- /dev/kfd
...
---
devices:
- /dev/dri
- /dev/kfd
```
For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
@@ -503,9 +502,9 @@ For chipset specific frigate rocm builds this variable is already set automatica
For the general rocm frigate build there is some automatic detection:
- gfx90c -> 9.0.0
- gfx1031 -> 10.3.0
- gfx1103 -> 11.0.0
- gfx90c -> 9.0.0
- gfx1031 -> 10.3.0
- gfx1103 -> 11.0.0
If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
@@ -519,18 +518,18 @@ When using docker compose:
```yaml
services:
frigate:
...
environment:
HSA_OVERRIDE_GFX_VERSION: "9.0.0"
---
environment:
HSA_OVERRIDE_GFX_VERSION: "9.0.0"
```
Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
- if things are not working check the frigate docker logs
- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
- if things are not working check the frigate docker logs
#### Figuring out if AMD/ROCm is working and found your GPU
@@ -566,9 +565,9 @@ or when using docker compose:
```yaml
services:
frigate:
...
environment:
DOWNLOAD_YOLOV8: "1"
---
environment:
DOWNLOAD_YOLOV8: "1"
```
Download can be triggered also in regular frigate builds using that environment variable. The following files will be available under `/config/model_cache/yolov8/`:
@@ -608,4 +607,4 @@ Other settings available for the rocm detector
### Expected performance
On an AMD Ryzen 3 5400U with integrated GPU (gfx90c) the yolov8n runs in around 9ms per image (about 110 detections per second) and 18ms (55 detections per second) for yolov8s (at 320x320 detector resolution).
On an AMD Ryzen 3 5400U with integrated GPU (gfx90c) the yolov8n runs in around 9ms per image (about 110 detections per second) and 18ms (55 detections per second) for yolov8s (at 320x320 detector resolution).