Tdarr 2.17.01

[FunkyBuddha]

What is Tdarr?

Tdarr is a popular conditional transcoding application for processing large (or small) media libraries. The application comes in the form of a click-to-run web-app, which you run on your own device and access through a web browser.

Tdarr uses two popular transcoding applications under the hood: FFmpeg and HandBrake (which itself is built on top of FFmpeg).

 

Why choose Tdarr?

Distributed

Tdarr works in a distributed manner where you can use multiple devices to process your library together. It does this using 'Tdarr Nodes' which connect with a central server and pick up tasks so you can put all your spare devices to use.

Each Node can run multiple 'Tdarr Workers' in parallel to maximize the hardware usage % on that Node. For example, a single FFmpeg worker running on a 64 core CPU may only hit ~30% utilization. Running multiple Workers in parallel allows the CPU to hit 100% utilization, allowing you to process your library more quickly.

 

Info:

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Docker Compose:

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version: "3.4"
services:
  tdarr:
    container_name: tdarr
    image: ghcr.io/haveagitgat/tdarr:latest
    restart: unless-stopped
    network_mode: bridge
    ports:
      - 8265:8265 # webUI port
      - 8266:8266 # server port
    environment:
      - TZ=Europe/London
      - PUID=${PUID}
      - PGID=${PGID}
      - UMASK_SET=002
      - serverIP=0.0.0.0
      - serverPort=8266
      - webUIPort=8265
      - internalNode=true
      - inContainer=true
      - ffmpegVersion=6
      - nodeName=MyInternalNode
      - NVIDIA_DRIVER_CAPABILITIES=all
      - NVIDIA_VISIBLE_DEVICES=all
    volumes:
      - /docker/tdarr/server:/app/server
      - /docker/tdarr/configs:/app/configs
      - /docker/tdarr/logs:/app/logs
      - /media:/media
      - /transcode_cache:/temp

    devices:
      - /dev/dri:/dev/dri
    deploy:
      resources:
        reservations:
          devices:
          - driver: nvidia
            count: all
            capabilities: [gpu]