From edbdbb7f0724634c377b029cc7d41a48d3c6268a Mon Sep 17 00:00:00 2001
From: sev <git@sev.monster>
Date: Sun, 22 Jan 2023 10:27:13 -0600
Subject: [PATCH] Update NVIDIA hwdecode docs (#5182)

Closes #4716
---
 .../configuration/hardware_acceleration.md    | 28 ++++++++++++-------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/docs/docs/configuration/hardware_acceleration.md b/docs/docs/configuration/hardware_acceleration.md
index d98b8d22a..3d1dcff30 100644
--- a/docs/docs/configuration/hardware_acceleration.md
+++ b/docs/docs/configuration/hardware_acceleration.md
@@ -39,15 +39,19 @@ ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-### NVIDIA GPU
+### NVIDIA GPUs
 
-[Supported Nvidia GPUs for Decoding](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new)
+While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
 
-These instructions are based on the [jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux)
+A more complete list of cards and ther compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
 
-Additional configuration is needed for the docker container to be able to access the Nvidia GPU and this depends on how docker is being run:
+If your distribution does not offer NVIDIA driver packages, you can [download them here](https://www.nvidia.com/en-us/drivers/unix/).
 
-#### Docker Compose
+#### Docker Configuration
+
+Additional configuration is needed for the Docker container to be able to access the NVIDIA GPU. The supported method for this is to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) and specify the GPU to Docker. How you do this depends on how Docker is being run:
+
+##### Docker Compose
 
 ```yaml
 services:
@@ -64,7 +68,7 @@ services:
               capabilities: [gpu]
 ```
 
-#### Docker Run CLI
+##### Docker Run CLI
 
 ```bash
 docker run -d \
@@ -78,9 +82,9 @@ docker run -d \
 
 The decoder you need to pass in the `hwaccel_args` will depend on the input video.
 
-A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
+A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get the ones your card supports)
 
-```shell
+```
  V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
  V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
  V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
@@ -101,12 +105,12 @@ ffmpeg:
 ```
 
 If everything is working correctly, you should see a significant improvement in performance.
-Verify that hardware decoding is working by running `nvidia-smi`, which should show the ffmpeg
+Verify that hardware decoding is working by running `nvidia-smi`, which should show `ffmpeg`
 processes:
 
 :::note
 
-nvidia-smi may not show ffmpeg processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458)
+`nvidia-smi` may not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
 
 :::
 
@@ -137,3 +141,7 @@ nvidia-smi may not show ffmpeg processes when run inside the container [due to d
 |    0   N/A  N/A     12827      C   ffmpeg                            417MiB |
 +-----------------------------------------------------------------------------+
 ```
+
+If you do not see these processes, check the `docker logs` for the container and look for decoding errors.
+
+These instructions were originally based on the [Jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux).