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- Tdarr
Tdarr Transcode Automation Home Download Docs Samples Tools Account Help
- Windows, Linux and macOS - Tdarr
Tdarr_Updater linux_arm64 Tdarr_Updater darwin_x64 (macOS) Tdarr_Updater darwin_arm64 (macOS) info Instead of using the Updater, you can download Tdarr_Server and Tdarr_Node directly here Unzip it If on Linux macOS it's best to run the packages from a terminal so you can see the output Windows will run them from a terminal automatically
- Tdarr
Tdarr Transcode Automation • Tdarr Server • Tdarr Nodes (5) • Plugin stacks + flows • CPU and GPU workers • Video health checks • Tdarr samples (2000+) • Community plugins (140+) • Basic stats • Library node schedulers • Queue management, sorting and filtering • File search • + More
- What is Tdarr? | 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
- Getting Started - Tdarr
Getting Started Tdarr V2 is a cross-platform, distributed transcoding system that is broken up into multiple modules Getting multiple machines working together across a local network requires some configuration You can run Tdarr using only a single machine Using extra Tdarr Nodes on multiple machines to increase transcoding resources is
- Tutorial Videos - Tdarr
By Spaceinvader One Special thanks to Spaceinvader One and Makin and Fixin for allowing use of their videos here Please check out their channels!
- Run and Compose - Tdarr
Run and Compose Run If using Docker, please still read the previous page of instructions as the configurations will be the same except you'll be setting the variables through env vars instead of the Config json files (except for the pathTranslators as those can't be set through env vars)
- Why choose Tdarr?
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
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