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[Jellyfin](https://jellyfin.github.io/) 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.
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](https://github.com/docker/distribution/blob/master/docs/spec/manifest-v2-2.md#manifest-list) and our announcement [here](https://blog.linuxserver.io/2019/02/21/the-lsio-pipeline-project/).
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.
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`.
More information can be found in their official documentation [here](https://github.com/MediaBrowser/Wiki/wiki) .
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:
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.
