mirror of
https://github.com/linuxserver/docker-jellyfin.git
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69 lines
4.3 KiB
YAML
69 lines
4.3 KiB
YAML
---
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# project information
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project_name: jellyfin
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project_url: "https://jellyfin.github.io/"
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project_logo: "https://raw.githubusercontent.com/jellyfin/jellyfin-ux/master/branding/SVG/banner-logo-solid.svg?sanitize=true"
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project_lsio_github_repo_url: "https://github.com/linuxserver/docker-{{ project_name }}"
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project_blurb: "[{{ project_name|capitalize }}]({{ project_url }}) 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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available_architectures:
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- { arch: "{{ arch_x86_64 }}", tag: "amd64-latest"}
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- { arch: "{{ arch_arm64 }}", tag: "arm64v8-latest"}
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- { arch: "{{ arch_armhf }}", tag: "arm32v7-latest"}
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# container parameters
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common_param_env_vars_enabled: true #PGID, PUID, etc
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param_container_name: "{{ project_name }}"
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param_usage_include_vols: true
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param_volumes:
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- { vol_path: "/config", vol_host_path: "</path/to/library>", desc: "Jellyfin data storage location. *This can grow very large, 50gb+ is likely for a large collection.*" }
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- { vol_path: "/data/tvshows", vol_host_path: "<path/to/tvseries>", desc: "Media goes here. Add as many as needed e.g. `/data/movies`, `/data/tv`, etc." }
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- { vol_path: "/data/movies", vol_host_path: "</path/to/movies>", desc: "Media goes here. Add as many as needed e.g. `/data/movies`, `/data/tv`, etc." }
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param_usage_include_ports: true
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param_ports:
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- { external_port: "8096", internal_port: "8096", port_desc: "Http webUI." }
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param_usage_include_env: true
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param_env_vars:
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- { env_var: "TZ", env_value: "Europe/London", desc: "Specify a timezone to use EG Europe/London"}
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# optional container parameters
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opt_param_usage_include_env: true
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opt_param_env_vars:
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- { env_var: "UMASK_SET", env_value: "<022>", desc: "for umask setting of Emby, default if left unset is 022."}
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opt_param_usage_include_vols: true
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opt_param_volumes:
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- { vol_path: "/transcode", vol_host_path: "</path for transcoding>", desc: "Path for transcoding folder, *optional*." }
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opt_param_device_map: true
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opt_param_devices:
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- { device_path: "/dev/dri", device_host_path: "/dev/dri", desc: "Only needed if you want to use your Intel GPU for hardware accelerated video encoding (vaapi)." }
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opt_param_usage_include_ports: true
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opt_param_ports:
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- { external_port: "8920", internal_port: "8920", port_desc: "Https webUI (you need to setup your own certificate)." }
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# application setup block
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app_setup_block_enabled: true
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app_setup_block: |
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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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# changelog
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changelogs:
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- { date: "31.07.19:", desc: "Add AMD drivers for vaapi support on x86." }
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- { date: "13.06.19:", desc: "Add Intel drivers for vaapi support on x86." }
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- { date: "07.06.19:", desc: "Initial release." }
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