kopia lustrzana https://github.com/linuxserver/docker-documentation
169 wiersze
7.6 KiB
Markdown
Executable File
169 wiersze
7.6 KiB
Markdown
Executable File
# [linuxserver/jellyfin](https://github.com/linuxserver/docker-jellyfin)
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[](https://github.com/linuxserver/docker-jellyfin/releases)
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[](https://microbadger.com/images/linuxserver/jellyfin "Get your own version badge on microbadger.com")
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[](https://microbadger.com/images/linuxserver/jellyfin "Get your own version badge on microbadger.com")
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[](https://hub.docker.com/r/linuxserver/jellyfin)
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[](https://hub.docker.com/r/linuxserver/jellyfin)
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[](https://ci.linuxserver.io/job/Docker-Pipeline-Builders/job/docker-jellyfin/job/master/)
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[](https://lsio-ci.ams3.digitaloceanspaces.com/linuxserver/jellyfin/latest/index.html)
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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.
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## Supported Architectures
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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/).
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Simply pulling `linuxserver/jellyfin` should retrieve the correct image for your arch, but you can also pull specific arch images via tags.
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The architectures supported by this image are:
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| Architecture | Tag |
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| :----: | --- |
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| x86-64 | amd64-latest |
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| arm64 | arm64v8-latest |
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| armhf | arm32v7-latest |
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## Usage
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Here are some example snippets to help you get started creating a container from this image.
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### docker
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```
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docker create \
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--name=jellyfin \
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-e PUID=1000 \
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-e PGID=1000 \
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-e TZ=Europe/London \
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-e UMASK_SET=<022> `#optional` \
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-p 8096:8096 \
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-p 8920:8920 `#optional` \
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-v </path/to/library>:/config \
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-v <path/to/tvseries>:/data/tvshows \
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-v </path/to/movies>:/data/movies \
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-v </path for transcoding>:/transcode `#optional` \
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--device /dev/dri:/dev/dri `#optional` \
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--restart unless-stopped \
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linuxserver/jellyfin
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```
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### docker-compose
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Compatible with docker-compose v2 schemas.
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```yaml
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---
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version: "2"
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services:
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jellyfin:
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image: linuxserver/jellyfin
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container_name: jellyfin
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environment:
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- PUID=1000
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- PGID=1000
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- TZ=Europe/London
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- UMASK_SET=<022> #optional
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volumes:
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- </path/to/library>:/config
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- <path/to/tvseries>:/data/tvshows
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- </path/to/movies>:/data/movies
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volumes:
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- </path for transcoding>:/transcode #optional
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ports:
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- 8096:8096
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ports:
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- 8920:8920 #optional
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devices:
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- /dev/dri:/dev/dri #optional
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restart: unless-stopped
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```
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## Parameters
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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.
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### Ports (`-p`)
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| Parameter | Function |
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| :----: | --- |
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| `8096` | Http webUI. |
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| `8920` | Https webUI (you need to setup your own certificate). |
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### Environment Variables (`-e`)
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| Env | Function |
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| :----: | --- |
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| `PUID=1000` | for UserID - see below for explanation |
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| `PGID=1000` | for GroupID - see below for explanation |
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| `TZ=Europe/London` | Specify a timezone to use EG Europe/London |
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| `UMASK_SET=<022>` | for umask setting of Emby, default if left unset is 022. |
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### Volume Mappings (`-v`)
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| Volume | Function |
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| :----: | --- |
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| `/config` | Jellyfin data storage location. *This can grow very large, 50gb+ is likely for a large collection.* |
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| `/data/tvshows` | Media goes here. Add as many as needed e.g. `/data/movies`, `/data/tv`, etc. |
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| `/data/movies` | Media goes here. Add as many as needed e.g. `/data/movies`, `/data/tv`, etc. |
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| `/transcode` | Path for transcoding folder, *optional*. |
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#### Device Mappings (`--device`)
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| Parameter | Function |
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| :-----: | --- |
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| `/dev/dri` | Only needed if you want to use your Intel GPU for hardware accelerated video encoding (vaapi). |
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## User / Group Identifiers
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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`.
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Ensure any volume directories on the host are owned by the same user you specify and any permissions issues will vanish like magic.
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In this instance `PUID=1000` and `PGID=1000`, to find yours use `id user` as below:
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```
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$ id username
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uid=1000(dockeruser) gid=1000(dockergroup) groups=1000(dockergroup)
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```
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## Application Setup
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Webui can be found at `http://<your-ip>:8096`
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More information can be found in their official documentation [here](https://github.com/MediaBrowser/Wiki/wiki) .
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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:
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```--device=/dev/dri:/dev/dri```
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We will automatically ensure the abc user inside of the container has the proper permissions to access this device.
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Hardware acceleration users for Nvidia will need to install the container runtime provided by Nvidia on their host, instructions can be found here:
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https://github.com/NVIDIA/nvidia-docker
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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.
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## Support Info
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* Shell access whilst the container is running:
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* `docker exec -it jellyfin /bin/bash`
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* To monitor the logs of the container in realtime:
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* `docker logs -f jellyfin`
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* Container version number
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* `docker inspect -f '{{ index .Config.Labels "build_version" }}' jellyfin`
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* Image version number
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* `docker inspect -f '{{ index .Config.Labels "build_version" }}' linuxserver/jellyfin`
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## Versions
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* **31.07.19:** - Add AMD drivers for vaapi support on x86.
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* **13.06.19:** - Add Intel drivers for vaapi support on x86.
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* **07.06.19:** - Initial release.
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