Cal Corum db47ee2c07 CLAUDE: Convert Tdarr node from unmapped to mapped configuration

- Updated start-tdarr-gpu-podman-clean.sh to use mapped node with direct media access
- Changed container name from tdarr-node-gpu-unmapped to tdarr-node-gpu-mapped
- Changed node name from nobara-pc-gpu-unmapped to nobara-pc-gpu-mapped
- Updated volume mounts to map TV and Movies directories separately
- Preserved NVMe cache and temp directory configurations
- Updated documentation to reflect mapped node architecture
- Added comparison between mapped and unmapped configurations in examples

🤖 Generated with [Claude Code](https://claude.ai/code)

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

2025-08-11 10:17:55 -05:00

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Tdarr Node Container Configurations

Overview

Complete examples for running Tdarr transcoding nodes in containers, covering both CPU-only and GPU-accelerated setups.

CPU-Only Configuration (Docker Compose)

For systems without GPU or when GPU isn't needed:

version: "3.4"
services:
  tdarr-node:
    container_name: tdarr-node-cpu
    image: ghcr.io/haveagitgat/tdarr_node:latest
    restart: unless-stopped
    environment:
      - TZ=America/Chicago
      - UMASK_SET=002
      - nodeName=local-workstation-cpu
      - serverIP=YOUR_TDARR_SERVER_IP  # Replace with your tdarr server IP
      - serverPort=8266
      - inContainer=true
      - ffmpegVersion=6
    volumes:
      # Mount your media from the same NAS share as the server
      - /path/to/your/media:/media  # Replace with your local media mount
      # Temp directory for transcoding cache
      - ./temp:/temp

Use case:

CPU-only transcoding
Testing Tdarr functionality
Systems without dedicated GPU
When GPU drivers aren't available

GPU-Accelerated Configuration (Podman)

Recommended for Fedora/RHEL/CentOS/Nobara systems:

Mapped Node (Direct Media Access)

podman run -d --name tdarr-node-gpu-mapped \
    --gpus all \
    --restart unless-stopped \
    -e TZ=America/Chicago \
    -e UMASK_SET=002 \
    -e nodeName=local-workstation-gpu-mapped \
    -e serverIP=10.10.0.43 \
    -e serverPort=8266 \
    -e inContainer=true \
    -e ffmpegVersion=6 \
    -e NVIDIA_DRIVER_CAPABILITIES=all \
    -e NVIDIA_VISIBLE_DEVICES=all \
    -v /mnt/NV2/tdarr-cache:/cache \
    -v /mnt/media/TV:/media/TV \
    -v /mnt/media/Movies:/media/Movies \
    -v /mnt/media/tdarr/tdarr-cache-clean:/temp \
    ghcr.io/haveagitgat/tdarr_node:latest

Unmapped Node (Downloads Files)

podman run -d --name tdarr-node-gpu-unmapped \
    --gpus all \
    --restart unless-stopped \
    -e TZ=America/Chicago \
    -e UMASK_SET=002 \
    -e nodeName=local-workstation-gpu-unmapped \
    -e serverIP=10.10.0.43 \
    -e serverPort=8266 \
    -e inContainer=true \
    -e ffmpegVersion=6 \
    -e NVIDIA_DRIVER_CAPABILITIES=all \
    -e NVIDIA_VISIBLE_DEVICES=all \
    -v /mnt/NV2/tdarr-cache:/cache \
    -v /mnt/media:/media \
    -v /mnt/media/tdarr/tdarr-cache-clean:/temp \
    ghcr.io/haveagitgat/tdarr_node:latest

Use cases:

Mapped: Direct media access, faster processing, no file downloads
Unmapped: Works when network shares aren't available locally
Hardware video encoding/decoding (NVENC/NVDEC)
High-performance transcoding with NVMe cache
Multiple concurrent streams
Fedora-based systems where Podman works better than Docker

GPU-Accelerated Configuration (Docker)

For Ubuntu/Debian systems where Docker GPU support works:

version: "3.4"
services:
  tdarr-node:
    container_name: tdarr-node-gpu
    image: ghcr.io/haveagitgat/tdarr_node:latest
    restart: unless-stopped
    environment:
      - TZ=America/Chicago
      - UMASK_SET=002
      - nodeName=local-workstation-gpu
      - serverIP=YOUR_TDARR_SERVER_IP
      - serverPort=8266
      - inContainer=true
      - ffmpegVersion=6
      - NVIDIA_DRIVER_CAPABILITIES=all
      - NVIDIA_VISIBLE_DEVICES=all
    volumes:
      - /path/to/your/media:/media
      - ./temp:/temp
    deploy:
      resources:
        reservations:
          devices:
          - driver: nvidia
            count: all
            capabilities: [gpu]

Configuration Parameters

Required Environment Variables

TZ: Timezone (e.g., America/Chicago)
nodeName: Unique identifier for this node
serverIP: IP address of Tdarr server
serverPort: Tdarr server port (typically 8266)
inContainer: Set to true for containerized deployments
ffmpegVersion: FFmpeg version to use (6 recommended)

GPU-Specific Variables

NVIDIA_DRIVER_CAPABILITIES: Set to all for full GPU access
NVIDIA_VISIBLE_DEVICES: all for all GPUs, or specific GPU IDs

Volume Mounts

/media: Mount point for media files (must match server configuration)
/temp: Temporary directory for transcoding cache

Platform-Specific Recommendations

Fedora/RHEL/CentOS/Nobara

GPU: Use Podman (Docker Desktop has GPU issues)
CPU: Docker or Podman both work fine

Ubuntu/Debian

GPU: Use Docker with nvidia-container-toolkit
CPU: Docker recommended

Testing GPU Functionality

Verify GPU access inside container:

# For Podman
podman exec tdarr-node-gpu nvidia-smi

# For Docker  
docker exec tdarr-node-gpu nvidia-smi

Test NVENC encoding:

# For Podman
podman exec tdarr-node-gpu /usr/local/bin/tdarr-ffmpeg -f lavfi -i testsrc2=duration=5:size=1920x1080:rate=30 -c:v h264_nvenc -t 5 /tmp/test.mp4

# For Docker
docker exec tdarr-node-gpu /usr/local/bin/tdarr-ffmpeg -f lavfi -i testsrc2=duration=5:size=1920x1080:rate=30 -c:v h264_nvenc -t 5 /tmp/test.mp4

Troubleshooting

GPU not detected: See reference/docker/nvidia-gpu-troubleshooting.md
Permission issues: Ensure proper UMASK_SET and volume permissions
Connection issues: Verify serverIP and firewall settings
Performance issues: Monitor CPU/GPU utilization during transcoding

patterns/docker/gpu-acceleration.md - GPU acceleration patterns
reference/docker/nvidia-gpu-troubleshooting.md - Detailed GPU troubleshooting
start-tdarr-gpu-podman.sh - Ready-to-use Podman startup script

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