# Tdarr — Distributed Media Transcoding Automation

> Automate video and audio library transcoding across multiple nodes using FFmpeg and HandBrake, with health checking and a visual flow-based plugin system.

## Install

Save as a script file and run:

# Tdarr — Distributed Media Transcoding Automation

## Quick Use
```bash
# Run Tdarr server + node with Docker Compose
# docker-compose.yml
version: "3"
services:
  tdarr:
    image: ghcr.io/haveagitgat/tdarr:latest
    ports:
      - 8265:8265   # Web UI
      - 8266:8266   # Server
    environment:
      - serverIP=0.0.0.0
      - serverPort=8266
      - webUIPort=8265
      - internalNode=true
      - nodeName=LocalNode
    volumes:
      - /opt/tdarr/server:/app/server
      - /opt/tdarr/configs:/app/configs
      - /opt/tdarr/logs:/app/logs
      - /media:/media
      - /tmp/tdarr:/temp
```

## Introduction
Tdarr is a distributed transcoding application that processes your media library in bulk. It scans video and audio files, applies user-defined rules through a flow-based plugin system, and distributes transcode jobs across multiple worker nodes using FFmpeg or HandBrake.

## What Tdarr Does
- Scans media libraries for files that do not match your desired codec, container, or bitrate
- Distributes transcode and health-check jobs across CPU and GPU worker nodes
- Provides a visual flow editor for building complex conditional processing pipelines
- Reports library-wide codec statistics and storage savings after transcoding
- Runs automated health checks to detect corrupt or truncated media files

## Architecture Overview
Tdarr uses a server-node model. The server coordinates the job queue and hosts the web UI on port 8265. Worker nodes connect to the server and pull transcode or health-check tasks. Each node can run multiple CPU and GPU workers in parallel. Processing uses FFmpeg or HandBrake under the hood, selected per flow step. The internal node option runs a worker inside the server container for single-machine setups.

## Self-Hosting & Configuration
- Deploy the server with Docker and optionally add remote worker nodes on other machines
- Mount your media library and a temporary transcode directory into the container
- Create libraries in the web UI and point them at your media folders
- Build processing flows using the visual drag-and-drop editor or use community presets
- Enable GPU transcoding by passing through NVIDIA or Intel Quick Sync devices

## Key Features
- Distributed architecture scales from one machine to a cluster of worker nodes
- Visual flow editor for building conditional transcode and remux pipelines
- GPU-accelerated transcoding with NVENC, QSV, and VAAPI support
- Library analytics showing codec breakdown, resolution distribution, and space savings
- Community plugin repository with hundreds of ready-made processing flows

## Comparison with Similar Tools
- **HandBrake** — standalone GUI/CLI transcoder, no library scanning or distributed workers
- **FFmpeg** — command-line only, no queue management or web UI
- **Unmanic** — similar library optimizer, single-node only, smaller community
- **FileFlows** — flow-based file processing, supports more file types beyond media
- **Bazarr** — subtitle manager for media, does not handle transcoding

## FAQ
**Q: Does Tdarr re-encode files automatically?**
A: Only files that match your flow conditions are processed. You define which codecs, containers, or quality levels trigger a transcode.

**Q: Can I use GPU transcoding?**
A: Yes. Pass through your NVIDIA GPU or Intel iGPU to the container and configure GPU workers in the node settings. NVENC and Quick Sync are both supported.

**Q: Will Tdarr damage my original files?**
A: Tdarr transcodes to a temporary directory first and only replaces the original after a successful health check. You can also enable a cache or backup directory for extra safety.

**Q: How many nodes can I run?**
A: There is no hard limit. Each node registers with the server and pulls jobs independently. Add more nodes to increase throughput.

## Sources
- https://github.com/HaveAGitGat/Tdarr
- https://docs.tdarr.io/

---
Source: https://tokrepo.com/en/workflows/17e6e513-3f98-11f1-9bc6-00163e2b0d79
Author: Script Depot