Unmanic

intermediate

Library optimizer for video file conversion.

Overview

Unmanic is an open-source library optimizer designed to automate video file conversion and transcoding workflows. Developed by Josh Sunnex, Unmanic continuously monitors media libraries and applies configurable conversion rules to optimize video files for storage efficiency, streaming compatibility, or quality standardization. The application features a plugin-based architecture that supports various video encoders, audio processors, and subtitle handlers, making it adaptable to diverse media processing requirements. This Docker deployment creates a self-contained transcoding environment where Unmanic operates as a background service, automatically discovering and processing media files based on user-defined criteria. The container includes a web-based management interface for configuring conversion profiles, monitoring job queues, and installing additional plugins. The system utilizes temporary storage for processing operations while maintaining organized library structures and preserving original files when configured to do so. Media enthusiasts, content creators, and system administrators managing large video libraries will find this setup particularly valuable for automating repetitive transcoding tasks. The configuration supports hardware acceleration through GPU passthrough, enabling efficient processing of high-resolution content while reducing CPU load and processing times.

Key Features

Plugin-based transcoding engine supporting multiple video codecs including H.264, H.265, VP9, and AV1
Automatic library scanning with configurable file monitoring and detection rules
Multi-worker processing queue with parallel transcoding job execution
Hardware acceleration support for Intel Quick Sync, AMD VCE, and NVIDIA NVENC encoders
Web-based management interface for real-time job monitoring and configuration
Flexible conversion profiles with customizable quality settings and format targeting
Post-processing hooks for file organization, notification systems, and metadata handling
Statistics dashboard tracking processing times, file size reductions, and queue performance

Common Use Cases

1Plex and Jellyfin server optimization by converting media files to streaming-friendly formats
2Storage space reduction through automated compression of large video libraries
3Format standardization for mixed media collections with inconsistent codecs and containers
4Automated subtitle extraction and audio track management for multilingual content
5Batch processing of recorded TV content with commercial detection and removal plugins
6Content preparation for mobile devices requiring specific resolution and bitrate constraints
7Archive conversion from legacy formats to modern codecs for long-term preservation

Prerequisites

Minimum 4GB RAM recommended for processing 1080p content, 8GB+ for 4K transcoding
Sufficient temporary storage space equal to 2-3x the size of largest video files being processed
GPU with hardware encoding support if utilizing hardware acceleration features
Media library accessible via mounted volumes with read/write permissions
Network connectivity for plugin downloads and optional notification integrations
Basic understanding of video codecs, containers, and transcoding concepts

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  unmanic: 
3    image: josh5/unmanic:latest
4    container_name: unmanic
5    restart: unless-stopped
6    ports: 
7      - "8888:8888"
8    environment: 
9      PUID: 1000
10      PGID: 1000
11    volumes: 
12      - unmanic_config:/config
13      - /path/to/library:/library
14      - /tmp/unmanic:/tmp/unmanic
15
16volumes: 
17  unmanic_config:

.env Template

.env

1# Configure your library path

Usage Notes

1Docs: https://docs.unmanic.app/
2Access at http://localhost:8888 - setup wizard on first run
3Install plugins: Library > Plugins (video encoder, audio, subtitles)
4Library scanning monitors folder for new files automatically
5Worker queue processes files with configurable parallel workers
6Hardware transcoding: mount /dev/dri for Intel/AMD acceleration

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  unmanic:
5    image: josh5/unmanic:latest
6    container_name: unmanic
7    restart: unless-stopped
8    ports:
9      - "8888:8888"
10    environment:
11      PUID: 1000
12      PGID: 1000
13    volumes:
14      - unmanic_config:/config
15      - /path/to/library:/library
16      - /tmp/unmanic:/tmp/unmanic
17
18volumes:
19  unmanic_config:
20EOF
21
22# 2. Create the .env file
23cat > .env << 'EOF'
24# Configure your library path
25EOF
26
27# 3. Start the services
28docker compose up -d
29
30# 4. View logs
31docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/unmanic/run | bash

Troubleshooting

FFmpeg not found or codec errors: Verify the container has access to required encoders and install additional codec plugins
Permission denied accessing library files: Check PUID/PGID values match the host filesystem ownership and verify volume mount permissions
Hardware acceleration not working: Ensure /dev/dri device is properly mounted and GPU drivers are compatible with container runtime
Jobs failing with out of memory errors: Increase container memory limits or reduce parallel worker count in settings
Web interface inaccessible: Verify port 8888 is not blocked by firewall and check container logs for binding errors
Processing stuck or extremely slow: Monitor temp directory disk space and ensure adequate storage for temporary transcoding files

Community Notes

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Includes docker-compose.yml, .env template, README, and license

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