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​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

Our images support multiple architectures such as x86-64, arm64 and armhf. We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here.

Simply pulling linuxserver/jellyfin 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:

Usage

Here are some example snippets to help you get started creating a container from this image.

docker

docker create \
  --name=jellyfin \
  -e PUID=1000 \
  -e PGID=1000 \
  -e TZ=Europe/London \
  -e UMASK_SET=<022> `#optional` \
  -p 8096:8096 \
  -p 8920:8920 `#optional` \
  -v </path/to/library>:/config \
  -v <path/to/tvseries>:/data/tvshows \
  -v </path/to/movies>:/data/movies \
  -v </path for transcoding>:/transcode `#optional` \
  --device /dev/dri:/dev/dri `#optional` \
  --restart unless-stopped \
  linuxserver/jellyfin

docker-compose

Compatible with docker-compose v2 schemas.

---
version: "2"
services:
  jellyfin:
    image: linuxserver/jellyfin
    container_name: jellyfin
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=Europe/London
      - UMASK_SET=<022> #optional
    volumes:
      - </path/to/library>:/config
      - <path/to/tvseries>:/data/tvshows
      - </path/to/movies>:/data/movies
    volumes:
      - </path for transcoding>:/transcode #optional
    ports:
      - 8096:8096
    ports:
      - 8920:8920 #optional
    devices:
      - /dev/dri:/dev/dri #optional
    restart: unless-stopped

Parameters

Docker images are configured using parameters passed at runtime (such as those above). These parameters are separated by a colon and indicate <external>:<internal> respectively. For example, -p 8080:80 would expose port 80 from inside the container to be accessible from the host's IP on port 8080 outside the container.

Ports (-p)

Environment Variables (-e)

Volume Mappings (-v)

Device Mappings (--device)

User / Group Identifiers

When using volumes (-v flags), permissions issues can arise between the host OS and the container, we avoid this issue by allowing you to specify the user PUID and group PGID.

Ensure any volume directories on the host are owned by the same user you specify and any permissions issues will vanish like magic.

In this instance PUID=1000 and PGID=1000, to find yours use id user as below:

  $ id username
    uid=1000(dockeruser) gid=1000(dockergroup) groups=1000(dockergroup)

Application Setup

Webui can be found at http://<your-ip>:8096

More information can be found in their official documentation here .

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.

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.

Support Info

• Shell access whilst the container is running:

  • docker exec -it jellyfin /bin/bash

• To monitor the logs of the container in realtime:

  • docker logs -f jellyfin

• Container version number

  • docker inspect -f '{{ index .Config.Labels "build_version" }}' jellyfin

• Image version number

  • docker inspect -f '{{ index .Config.Labels "build_version" }}' linuxserver/jellyfin

Versions

• 02.10.19: - Improve permission fixing for render & dvb devices.

• 31.07.19: - Add AMD drivers for vaapi support on x86.

• 13.06.19: - Add Intel drivers for vaapi support on x86.

• 07.06.19: - Initial release.