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Merge pull request #233 from linuxserver/readme_hwaccel

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Roxedus
2024-02-13 17:15:37 +01:00
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47
README.md
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@@ -72,29 +72,18 @@ Webui can be found at `http://<your-ip>:8096`
More information can be found on the official documentation [here](https://jellyfin.org/docs/general/quick-start.html).
## Hardware Acceleration
### Hardware Acceleration Enhancements
This section lists the enhancements we have made for hardware acceleration in this image specifically.
### Intel
Hardware acceleration users for Intel Quicksync will need to mount their /dev/dri video device inside of the container by passing the following command when running or creating the container:
`--device=/dev/dri:/dev/dri`
We will automatically ensure the abc user inside of the container has the proper permissions to access this device.
To enable the OpenCL based DV, HDR10 and HLG tone-mapping, please refer to the OpenCL-Intel mod from here:
https://mods.linuxserver.io/?mod=jellyfin
### 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 jellyfin docker container.
### 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:
@@ -104,7 +93,7 @@ Hardware acceleration users for Raspberry Pi MMAL/OpenMAX will need to mount the
-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:
@@ -114,6 +103,31 @@ Hardware acceleration users for Raspberry Pi V4L2 will need to mount their `/dev
--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.
@@ -357,6 +371,7 @@ Once registered you can define the dockerfile to use with `-f Dockerfile.aarch64
## Versions
* **12.02.24:** - Use universal hardware acceleration blurb
* **12.09.23:** - Take ownership of plugin directories.
* **04.07.23:** - Deprecate armhf. As announced [here](https://www.linuxserver.io/blog/a-farewell-to-arm-hf)
* **07.12.22:** - Rebase master to Jammy, migrate to s6v3.

24
readme-vars.yml
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@@ -69,29 +69,18 @@ app_setup_block: |
More information can be found on the official documentation [here](https://jellyfin.org/docs/general/quick-start.html).
## Hardware Acceleration
### Hardware Acceleration Enhancements
This section lists the enhancements we have made for hardware acceleration in this image specifically.
### Intel
Hardware acceleration users for Intel Quicksync will need to mount their /dev/dri video device inside of the container by passing the following command when running or creating the container:
`--device=/dev/dri:/dev/dri`
We will automatically ensure the abc user inside of the container has the proper permissions to access this device.
To enable the OpenCL based DV, HDR10 and HLG tone-mapping, please refer to the OpenCL-Intel mod from here:
https://mods.linuxserver.io/?mod=jellyfin
### 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 jellyfin docker container.
### 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:
@@ -101,7 +90,7 @@ app_setup_block: |
-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:
@@ -110,9 +99,12 @@ app_setup_block: |
--device=/dev/video11:/dev/video11
--device=/dev/video12:/dev/video12
```
readme_hwaccel: true
unraid_template_sync: false
# changelog
changelogs:
- {date: "12.02.24:", desc: "Use universal hardware acceleration blurb"}
- {date: "12.09.23:", desc: "Take ownership of plugin directories."}
- {date: "04.07.23:", desc: "Deprecate armhf. As announced [here](https://www.linuxserver.io/blog/a-farewell-to-arm-hf)"}
- {date: "07.12.22:", desc: "Rebase master to Jammy, migrate to s6v3."}