linuxserver/jellyfin
Jellyfin is a Free Software Media System that puts you in control of managing and streaming your media. It is an alternative to the proprietary Emby and Plex, to provide media from a dedicated server to end-user devices via multiple apps. Jellyfin is descended from Emby's 3.5.2 release and ported to the .NET Core framework to enable full cross-platform support. There are no strings attached, no premium licenses or features, and no hidden agendas: just a team who want to build something better and work together to achieve it.
Supported Architectures
We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here.
Simply pulling lscr.io/linuxserver/jellyfin:latest should retrieve the correct image for your arch, but you can also pull specific arch images via tags.
The architectures supported by this image are:
| Architecture | Available | Tag |
|---|---|---|
| x86-64 | ✅ | amd64-\<version tag> |
| arm64 | ✅ | arm64v8-\<version tag> |
| armhf | ❌ |
Version Tags
This image provides various versions that are available via tags. Please read the descriptions carefully and exercise caution when using unstable or development tags.
| Tag | Available | Description |
|---|---|---|
| latest | ✅ | Stable Jellyfin releases |
| nightly | ✅ | Nightly Jellyfin releases |
| ## Application Setup |
Webui can be found at http://<your-ip>:8096
More information can be found on the official documentation here.
Hardware Acceleration
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)
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:
--device=/dev/vcsm:/dev/vcsm
+ jellyfin - LinuxServer.io linuxserver/jellyfin
Jellyfin is a Free Software Media System that puts you in control of managing and streaming your media. It is an alternative to the proprietary Emby and Plex, to provide media from a dedicated server to end-user devices via multiple apps. Jellyfin is descended from Emby's 3.5.2 release and ported to the .NET Core framework to enable full cross-platform support. There are no strings attached, no premium licenses or features, and no hidden agendas: just a team who want to build something better and work together to achieve it.
Supported Architectures
We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here.
Simply pulling lscr.io/linuxserver/jellyfin:latest should retrieve the correct image for your arch, but you can also pull specific arch images via tags.
The architectures supported by this image are:
Architecture Available Tag x86-64 ✅ amd64-\<version tag> arm64 ✅ arm64v8-\<version tag> armhf ❌
Version Tags
This image provides various versions that are available via tags. Please read the descriptions carefully and exercise caution when using unstable or development tags.
Tag Available Description latest ✅ Stable Jellyfin releases nightly ✅ Nightly Jellyfin releases ## Application Setup
Webui can be found at http://<your-ip>:8096
More information can be found on the official documentation here.
Hardware Acceleration
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)
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:
--device=/dev/vcsm:/dev/vcsm
--device=/dev/vchiq:/dev/vchiq
-v /opt/vc/lib:/opt/vc/lib
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:
--device=/dev/video10:/dev/video10
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