v_11
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services:
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jellyfin:
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image: jellyfin/jellyfin:latest
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container_name: jellyfin
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# Optional: run as a specific user/group instead of root
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# Replace 1000:1000 with your actual UID:GID (run `id` in terminal to find yours)
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# user: "1000:1000"
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ports:
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- 8096:8096/tcp # Main HTTP web UI & API
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- 8920:8920/tcp # HTTPS web UI (optional, requires cert setup)
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- 7359:7359/udp # Local network auto-discovery (DLNA)
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- 1900:1900/udp # DLNA service discovery (optional, requires host network)
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volumes:
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# Jellyfin configuration & metadata database
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- ./jellyfin/config:/config
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# Transcoding cache (can be tmpfs for better performance)
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- ./jellyfin/cache:/cache
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# --- Media Libraries ---
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# Add as many bind mounts as you need for your media folders.
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# Set read_only: true for libraries Jellyfin should not modify.
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- type: bind
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source: ./media/movies
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target: /media/movies
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read_only: true
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- type: bind
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source: ./media/tvshows
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target: /media/tvshows
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read_only: true
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- type: bind
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source: ./media/music
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target: /media/music
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read_only: true
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# Optional: custom fonts for subtitle burn-in during transcoding
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# - type: bind
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# source: ./fonts
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# target: /usr/local/share/fonts/custom
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# read_only: true
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# Optional: fallback fonts directory
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# (set fallback font path to /fallback_fonts in Jellyfin server settings)
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# - type: bind
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# source: ./fallback_fonts
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# target: /fallback_fonts
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# read_only: true
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environment:
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# Optional: set this to your server's public URL for correct autodiscovery
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- JELLYFIN_PublishedServerUrl=http://tube.martinhal.tech:8096
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# Timezone (change to your local timezone)
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- TZ=Europe/Lisbon
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# Optional: enable GPU hardware acceleration (Intel/AMD iGPU via /dev/dri)
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# Uncomment the section below if your host has a compatible GPU
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# devices:
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# - /dev/dri:/dev/dri
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# Required if using host network mode for full DLNA support
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# network_mode: host
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# Needed for Docker healthcheck to pass in host network mode
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extra_hosts:
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- "host.docker.internal:host-gateway"
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restart: unless-stopped
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# Optional: resource limits to prevent Jellyfin from consuming all system resources
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# deploy:
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# resources:
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# limits:
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# memory: 4G
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# cpus: "4.0"
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