Switch to readme_hwaccel

README.md

@@ -72,35 +72,55 @@ Webui can be found at `http://<your-ip>:8096`
 
 Emby has very complete and verbose documentation located [here](https://github.com/MediaBrowser/Wiki/wiki) .
 
-Hardware acceleration users for Intel Quicksync and AMD VAAPI will need to mount their /dev/dri video device inside of the container by passing the following command when running or creating the container:
+### Hardware Acceleration Enhancements
 
-```--device=/dev/dri:/dev/dri```
+This list out the enhancements we have explicit made for hardware acceleration in this image.
 
-We will automatically ensure the abc user inside of the container has the proper permissions to access this device.
-
-Hardware acceleration users for Nvidia will need to install the container runtime provided by Nvidia on their host, instructions can be found here:
-
-https://github.com/NVIDIA/nvidia-docker
-
-We automatically add the necessary environment variable that will utilise all the features available on a GPU on the host. Once nvidia-docker is installed on your host you will need to re/create the docker container with the nvidia container runtime `--runtime=nvidia` and add an environment variable `-e NVIDIA_VISIBLE_DEVICES=all` (can also be set to a specific gpu's UUID, this can be discovered by running `nvidia-smi --query-gpu=gpu_name,gpu_uuid --format=csv` ). NVIDIA automatically mounts the GPU and drivers from your host into the emby docker.
+#### OpenMAX (Raspberry Pi)
 
-### OpenMAX (Raspberry Pi)
+Hardware acceleration users for Raspberry Pi MMAL/OpenMAX will need to mount their `/dev/vcsm` and `/dev/vchiq` video devices inside of the container and their system OpenMax libs by passing the following options when running or creating the container:
 
-Hardware acceleration users for Raspberry Pi OpenMAX will need to mount their /dev/vchiq video device inside of the container and their system OpenMax libs by passing the following options when running or creating the container:
 ```
+--device=/dev/vcsm:/dev/vcsm
 --device=/dev/vchiq:/dev/vchiq
 -v /opt/vc/lib:/opt/vc/lib
 ```
 
-### V4L2 (Raspberry Pi)
+#### V4L2 (Raspberry Pi)
+
+Hardware acceleration users for Raspberry Pi V4L2 will need to mount their `/dev/video1X` devices inside of the container by passing the following options when running or creating the container:
 
-Hardware acceleration users for Raspberry Pi V4L2 will need to mount their /dev/video1X devices inside of the container by passing the following options when running or creating the container:
 ```
 --device=/dev/video10:/dev/video10
 --device=/dev/video11:/dev/video11
 --device=/dev/video12:/dev/video12
 ```
 
+### Hardware Acceleration
+
+Many desktop application will need access to a GPU to function properly and even some Desktop Environments have compisitor effects that will not function without a GPU. This is not a hard requirement and all base images will function without a video device mounted into the container.
+
+#### Intel/ATI/AMD
+
+To leverage hardware acceleration you will need to mount /dev/dri video device inside of the container.
+
+```text
+--device=/dev/dri:/dev/dri
+```
+
+We will automatically ensure the abc user inside of the container has the proper permissions to access this device.
+
+#### Nvidia
+
+Hardware acceleration users for Nvidia will need to install the container runtime provided by Nvidia on their host, instructions can be found here:
+https://github.com/NVIDIA/nvidia-docker
+
+We automatically add the necessary environment variable that will utilise all the features available on a GPU on the host. Once nvidia-docker is installed on your host you will need to re/create the docker container with the nvidia container runtime `--runtime=nvidia` and add an environment variable `-e NVIDIA_VISIBLE_DEVICES=all` (can also be set to a specific gpu's UUID, this can be discovered by running `nvidia-smi --query-gpu=gpu_name,gpu_uuid --format=csv` ). NVIDIA automatically mounts the GPU and drivers from your host into the container.
+
+#### Arm Devices
+
+Best effort is made to install tools to allow mounting in /dev/dri on Arm devices. In most cases if /dev/dri exists on the host it should just work. If running a Raspberry Pi 4 be sure to enable `dtoverlay=vc4-fkms-v3d` in your usercfg.txt.
+
 ## Usage
 
 To help you get started creating a container from this image you can either use docker-compose or the docker cli.
@@ -339,6 +359,7 @@ Once registered you can define the dockerfile to use with `-f Dockerfile.aarch64
 
 ## Versions
 
+* **12.02.24:** - Use universal hardware acceleration blurb
 * **19.01.24:** - Fix tonemapping so it's done with hw acceleration.
 * **06.07.23:** - Deprecate armhf. As announced [here](https://www.linuxserver.io/blog/a-farewell-to-arm-hf)
 * **08.06.23:** - Fix package extraction so it doesn't change /tmp perms.

readme-vars.yml

@@ -53,36 +53,34 @@ app_setup_block: |
 
   Emby has very complete and verbose documentation located [here](https://github.com/MediaBrowser/Wiki/wiki) .
 
-  Hardware acceleration users for Intel Quicksync and AMD VAAPI will need to mount their /dev/dri video device inside of the container by passing the following command when running or creating the container:
+  ### Hardware Acceleration Enhancements
 
-  ```--device=/dev/dri:/dev/dri```
+  This list out the enhancements we have explicit made for hardware acceleration in this image.
 
-  We will automatically ensure the abc user inside of the container has the proper permissions to access this device.
+  #### OpenMAX (Raspberry Pi)
 
-  Hardware acceleration users for Nvidia will need to install the container runtime provided by Nvidia on their host, instructions can be found here:
+  Hardware acceleration users for Raspberry Pi MMAL/OpenMAX will need to mount their `/dev/vcsm` and `/dev/vchiq` video devices inside of the container and their system OpenMax libs by passing the following options when running or creating the container:
 
-  https://github.com/NVIDIA/nvidia-docker
-
-  We automatically add the necessary environment variable that will utilise all the features available on a GPU on the host. Once nvidia-docker is installed on your host you will need to re/create the docker container with the nvidia container runtime `--runtime=nvidia` and add an environment variable `-e NVIDIA_VISIBLE_DEVICES=all` (can also be set to a specific gpu's UUID, this can be discovered by running `nvidia-smi --query-gpu=gpu_name,gpu_uuid --format=csv` ). NVIDIA automatically mounts the GPU and drivers from your host into the emby docker.
-
-  ### OpenMAX (Raspberry Pi)
-
-  Hardware acceleration users for Raspberry Pi OpenMAX will need to mount their /dev/vchiq video device inside of the container and their system OpenMax libs by passing the following options when running or creating the container:
   ```
+  --device=/dev/vcsm:/dev/vcsm
   --device=/dev/vchiq:/dev/vchiq
   -v /opt/vc/lib:/opt/vc/lib
   ```
 
-  ### V4L2 (Raspberry Pi)
+  #### V4L2 (Raspberry Pi)
+
+  Hardware acceleration users for Raspberry Pi V4L2 will need to mount their `/dev/video1X` devices inside of the container by passing the following options when running or creating the container:
 
-  Hardware acceleration users for Raspberry Pi V4L2 will need to mount their /dev/video1X devices inside of the container by passing the following options when running or creating the container:
   ```
   --device=/dev/video10:/dev/video10
   --device=/dev/video11:/dev/video11
   --device=/dev/video12:/dev/video12
   ```
+
+readme_hwaccel: true
 # changelog
 changelogs:
+  - {date: "12.02.24:", desc: "Use universal hardware acceleration blurb"}
   - {date: "19.01.24:", desc: "Fix tonemapping so it's done with hw acceleration."}
   - {date: "06.07.23:", desc: "Deprecate armhf. As announced [here](https://www.linuxserver.io/blog/a-farewell-to-arm-hf)"}
   - {date: "08.06.23:", desc: "Fix package extraction so it doesn't change /tmp perms."}