claude-home/media-servers/CONTEXT.md

Media Servers - Technology Context

Overview

Media server infrastructure for home lab environments, covering streaming services like Jellyfin and Plex with hardware-accelerated transcoding, library management, and client discovery.

Current Deployments

Jellyfin on ubuntu-manticore

• Location: 10.10.0.226:8096
• GPU: NVIDIA GTX 1070 (NVENC/NVDEC)
• Documentation: jellyfin-ubuntu-manticore.md

Plex (Existing)

• Location: TBD (potential migration to ubuntu-manticore)
• Note: Currently running elsewhere, may migrate for GPU access

Architecture Patterns

GPU-Accelerated Transcoding

Pattern: Hardware encoding/decoding for real-time streaming

# Docker Compose GPU passthrough
deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia
          count: all
          capabilities: [gpu]
environment:
  - NVIDIA_DRIVER_CAPABILITIES=all
  - NVIDIA_VISIBLE_DEVICES=all

Storage Strategy

Pattern: Tiered storage for different access patterns

• Config: Local SSD (small, fast database access)
• Cache: Local NVMe (transcoding temp, thumbnails)
• Media: Network storage (large capacity, read-only mount)

Multi-Service GPU Sharing

Pattern: Resource allocation when multiple services share GPU

• Limit background tasks (Tdarr) to fewer concurrent jobs
• Prioritize real-time services (Jellyfin/Plex playback)
• Consumer GPUs limited to 2-3 concurrent NVENC sessions

Common Configurations

NVIDIA GPU Setup

# Verify GPU in container
docker exec <container> nvidia-smi

# Check encoder/decoder utilization
nvidia-smi dmon -s u

Media Volume Mounts

volumes:
  - /mnt/truenas/media:/media:ro  # Read-only for safety

Client Discovery

• Jellyfin: UDP 7359
• Plex: UDP 32410-32414, GDM

Integration Points

Watch History Sync

• Tool: watchstate (ghcr.io/arabcoders/watchstate)
• Method: API-based sync between services
• Note: NFO files do NOT store watch history

Tdarr Integration

• Tdarr pre-processes media for optimal streaming
• Shared GPU resources require coordination
• See tdarr/CONTEXT.md for transcoding system details

Best Practices

Performance

1. Use NVMe for cache/transcoding temp directories
2. Mount media read-only to prevent accidental modifications
3. Enable hardware transcoding for all supported codecs
4. Limit concurrent transcodes based on GPU capability

Reliability

1. Use restart: unless-stopped for containers
2. Separate config from cache (different failure modes)
3. Monitor disk space on cache volumes
4. Regular database backups (config directory)

Security

1. Run containers as non-root (PUID/PGID)
2. Use read-only media mounts
3. Limit network exposure (internal LAN only)
4. Regular container image updates

GPU Compatibility Notes

NVIDIA Pascal (GTX 10-series)

• NVENC: H.264, HEVC (no B-frames for HEVC)
• NVDEC: H.264, HEVC, VP8, VP9
• Sessions: 2 concurrent (consumer card limit)

NVIDIA Turing+ (RTX 20-series and newer)

• NVENC: H.264, HEVC (with B-frames), AV1
• NVDEC: H.264, HEVC, VP8, VP9, AV1
• Sessions: 3+ concurrent

Troubleshooting

Common Issues

1. No GPU in container: Check Docker/Podman GPU passthrough config
2. Transcoding failures: Verify codec support for your GPU generation
3. Slow playback start: Check network mount performance
4. Cache filling up: Monitor trickplay/thumbnail generation

Diagnostic Commands

# GPU status
nvidia-smi

# Container GPU access
docker exec <container> nvidia-smi

# Encoder/decoder utilization
nvidia-smi dmon -s u

# Container logs
docker logs <container> 2>&1 | tail -50