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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-12 09:00:44 -05:00

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Jellyfin Setup on ubuntu-manticore

Overview

Jellyfin media server deployed on ubuntu-manticore (10.10.0.226) with NVIDIA GTX 1070 for hardware-accelerated transcoding.

Date: 2025-12-04

Architecture

ubuntu-manticore (10.10.0.226)
└── jellyfin (container)
    ├── Web UI: http://10.10.0.226:8096
    ├── Discovery: UDP 7359
    ├── Config: ~/docker/jellyfin/config/
    ├── Cache: /mnt/NV2/jellyfin-cache (NVMe)
    ├── Media: /mnt/truenas/media (read-only)
    └── GPU: GTX 1070 (NVENC/NVDEC)

Docker Compose Configuration

Location: ~/docker/jellyfin/docker-compose.yml

services:
  jellyfin:
    image: jellyfin/jellyfin:latest
    container_name: jellyfin
    restart: unless-stopped
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=America/Chicago
      - NVIDIA_DRIVER_CAPABILITIES=all
      - NVIDIA_VISIBLE_DEVICES=all
    ports:
      - "8096:8096"      # Web UI
      - "7359:7359/udp"  # Client discovery
    volumes:
      - ./config:/config
      - /mnt/NV2/jellyfin-cache:/cache
      - /mnt/truenas/media:/media:ro
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

Storage Strategy

Path	Location	Purpose
/config	~/docker/jellyfin/config/	Database, settings (small, OS drive)
/cache	/mnt/NV2/jellyfin-cache	Transcoding temp, trickplay images (large, NVMe)
/media	/mnt/truenas/media	Media library (read-only, network)

Why NVMe for cache:

Trickplay (timeline thumbnails) can grow to several GB
Chapter images add up across large libraries
Transcoding temp files need fast I/O
Prevents filling up the boot drive

GPU Hardware Acceleration

Available Encoders

Encoder	Status
h264_nvenc	Working
hevc_nvenc	Working
av1_nvenc	Working

Available Decoders

Decoder	Status
h264_cuvid	Working
hevc_cuvid	Working
av1_cuvid	Working
vp8_cuvid	Working
vp9_cuvid	Working

Hardware Acceleration Types

CUDA
VAAPI
QSV (not applicable - Intel)
Vulkan
OpenCL

Configuring Hardware Transcoding

Go to Dashboard → Playback → Transcoding
Select NVIDIA NVENC as the hardware acceleration
Enable desired codecs for hardware decoding/encoding
Save changes

Ports

Port	Protocol	Purpose
8096	TCP	Web UI and API
7359	UDP	Client auto-discovery on LAN

Initial Setup

Access http://10.10.0.226:8096
Complete the setup wizard:
- Create admin account
- Add media libraries (Movies: /media/Movies, TV: /media/TV)
- Configure metadata providers
- Skip NFO savers (not needed unless sharing with Kodi)
Configure hardware transcoding in Dashboard

Both Jellyfin and Tdarr share the GTX 1070. To prevent conflicts:

Tdarr: Limited to 1 GPU worker
Jellyfin: Handles real-time transcoding on-demand
GTX 1070: Supports 2 concurrent NVENC sessions (consumer card limit)

Jellyfin transcodes are more latency-sensitive (users waiting for playback), so Tdarr yields priority by limiting concurrent jobs.

Watch History Sync (Future)

For syncing watch history between Plex and Jellyfin:

Use ghcr.io/arabcoders/watchstate
Syncs via API, not NFO files
NFO files don't store watch state

NVIDIA Driver Management

Auto-Update Prevention

Issue: NVIDIA driver auto-updates can cause driver/library version mismatches, breaking GPU access until the host is rebooted. This causes Jellyfin downtime.

Solution: Driver packages are held to prevent automatic updates:

# Packages currently held (as of 2026-02-05):
nvidia-driver-570
nvidia-kernel-common-570
nvidia-dkms-570

Verify held packages:

apt-mark showhold

Update Monitoring

A monitoring script checks for NVIDIA driver updates weekly and sends Discord alerts when new versions are available:

Script: /home/cal/scripts/nvidia_update_checker.py Schedule: Every Monday at 9 AM Logs: /home/cal/logs/nvidia-update-checker.log

Manual check:

python3 /home/cal/scripts/nvidia_update_checker.py --check --discord-alerts

Test Discord integration:

python3 /home/cal/scripts/nvidia_update_checker.py --discord-test

Planned Driver Updates

When Discord alerts about available updates, plan a maintenance window:

Unhold packages:
```
sudo apt-mark unhold nvidia-driver-570
```

Update drivers:

sudo apt update && sudo apt upgrade nvidia-driver-570

Reboot immediately (driver changes require reboot):
```
sudo reboot
```

Verify after reboot:

nvidia-smi
docker exec jellyfin nvidia-smi

Re-hold packages:

sudo apt-mark hold nvidia-driver-570 nvidia-kernel-common-570 nvidia-dkms-570

GPU Health Monitoring

Jellyfin GPU access is monitored every 5 minutes:

Script: /home/cal/scripts/jellyfin_gpu_monitor.py Features:

Detects GPU access loss
Sends Discord alerts
Auto-restarts container (if GPU accessible)
Logs to /home/cal/logs/jellyfin-gpu-monitor.log

Note: Container restart cannot fix host-level driver issues. If Discord alerts show "Restart failed" with driver/library mismatch, a host reboot is required.

Troubleshooting

GPU Not Detected in Transcoding

# Verify GPU access in container
docker exec jellyfin nvidia-smi

Check Available Encoders

Container logs show available encoders on startup:

docker logs jellyfin 2>&1 | grep -i "available encoders"

Transcoding Failures

Check Jellyfin logs in Dashboard → Logs or:

docker logs jellyfin 2>&1 | tail -50

Driver/Library Version Mismatch

Symptoms:

nvidia-smi fails with "driver/library version mismatch"
Jellyfin container won't start with NVML error
GPU monitoring alerts show "Restart failed"

Cause: NVIDIA driver updated but kernel modules not reloaded

Solution: Reboot the host

sudo reboot

Server inventory: networking/server-inventory.md
Tdarr setup: tdarr/ubuntu-manticore-setup.md
GPU monitoring: monitoring/scripts/jellyfin_gpu_monitor.py
Update monitoring: monitoring/scripts/nvidia_update_checker.py

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