70 lines
2.2 KiB
YAML
70 lines
2.2 KiB
YAML
version: '3.8'
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# Jellyfin - Media Server with GPU Transcoding
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# Backlog: Application Deployment
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# Ports: 8096 (HTTP), 8920 (HTTPS), 7359 (Auto-discovery), 1900 (DLNA)
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# GPU: YES - Requires NVIDIA Container Toolkit
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# Storage: SSD (config/cache), HDD (media files)
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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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user: 1000:1000 # Replace with your UID:GID (run: id)
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network_mode: host
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restart: unless-stopped
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volumes:
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# Config and cache on SSD (performance-critical)
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- /home/jpmschweitzer/docker-data/jellyfin/config:/config
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- /home/jpmschweitzer/docker-data/jellyfin/cache:/cache
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# Media files on HDD (read-only for safety)
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- /mnt/media/jellyfin/movies:/media/movies:ro
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- /mnt/media/jellyfin/series:/media/series:ro
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=all
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- TZ=Europe/Amsterdam
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu, video, compute, utility]
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# GPU Transcoding Setup:
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# 1. Deploy stack
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# 2. Verify GPU access: docker exec jellyfin nvidia-smi
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# 3. Access http://localhost:8096
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# 4. Complete initial setup wizard
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# 5. Navigate to: Dashboard → Playback → Transcoding
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# 6. Configure hardware acceleration:
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# - Hardware acceleration: NVIDIA NVENC
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# - Enable hardware decoding: Check all applicable formats
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# - Enable hardware encoding: Enabled
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# - Encoding preset: Auto or High Quality
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# 7. Test with video playback
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# 8. Monitor GPU: watch -n 1 nvidia-smi
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#
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# Expected Results:
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# - Dashboard shows "(hw)" during transcoding
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# - nvidia-smi shows Video Engine usage
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# - CPU usage remains low during transcoding
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# - RTX 2080 Ti can handle multiple 4K transcodes simultaneously
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#
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# Media Organization:
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# /mnt/media/jellyfin/
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# ├── movies/
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# │ ├── Movie Title (Year)/
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# │ │ └── Movie Title (Year).mkv
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# ├── tv/
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# │ ├── TV Show Name/
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# │ │ ├── Season 01/
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# │ │ │ ├── S01E01.mkv
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# │ │ │ └── S01E02.mkv
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# └── music/
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# ├── Artist/
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# │ ├── Album/
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# │ │ └── Track.mp3
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