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INSTALLING.md

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Installing TLDR

  1. Install docker & docker-compose
  2. Download the docker-compose.yml and .env files
  3. Fill the .env file with your configuration options
  4. Look at Hardware Acceleration section if you need it
  5. Run docker compose up -d and see kyoo at http://localhost:8901

Installing

To install Kyoo, you need docker and docker-compose. Those can be installed from here for Linux Mac or Windows. Docker is used to run each services of Kyoo in an isolated environment with all the dependencies they need.

Kyoo also needs 2 files to work properly. The first should be downloaded from the latest release artifact, the other needs to be filled in with your configurations. Those files can be put in any directory of your choice.

Those files are:

  • A docker-compose.yml (simply download docker-compose.yml from the latest release).
  • A .env file that you will need to fill. Look at the example .env.example

If you want an explanation of what are those files, you can read the following: The docker-compose.yml file describes the different services of Kyoo, where they should be downloaded and their start order.
The .env file contains all the configuration options that the services in docker-compose.yml will read.

If you need hardware acceleration, look at Hardware Acceleration section if you need it

The next and last step is actually starting Kyoo. To do that, open a terminal in the same directory as the 3 configurations files and run docker-compose up -d.

Congratulation, everything is now ready to use Kyoo. You can navigate to http://localhost:8901 on a web browser to see your instance of Kyoo.

Updating

Updating Kyoo is exactly the same as installing it. Get an updated version of the docker-compose.yml file and unsure that your .env contains all the options specified in the updated .env.example file.

After that, you will need to update Kyoo's services. For that, open a terminal in the configuration's directory and run the command docker-compose pull. You are now ready to restart Kyoo, you can run docker-compose up -d.

Uninstalling

To uninstall Kyoo, you need to open a terminal in the configuration's directory and run docker-compose down. This will stop Kyoo's services. You can then remove the configuration files.

Hardware Acceleration

VA-API (intel, amd)

First install necessary drivers on your system, when running vainfo you should have something like this:

libva info: VA-API version 1.20.0
libva info: Trying to open /run/opengl-driver/lib/dri/iHD_drv_video.so
libva info: Found init function __vaDriverInit_1_20
libva info: va_openDriver() returns 0
vainfo: VA-API version: 1.20 (libva 2.20.1)
vainfo: Driver version: Intel iHD driver for Intel(R) Gen Graphics - 23.3.5 ()
vainfo: Supported profile and entrypoints
      VAProfileH264Main               :	VAEntrypointVLD
      VAProfileH264Main               :	VAEntrypointEncSlice
      ...Truncated...
      VAProfileHEVCSccMain444_10      :	VAEntrypointVLD
      VAProfileHEVCSccMain444_10      :	VAEntrypointEncSliceLP

Kyoo will default to use your primary card (located at /dev/dri/renderD128). If you need to specify a secondary one, you can use the GOCODER_VAAPI_RENDERER env-var to specify /dev/dri/renderD129 or another one.

Then you can simply run kyoo using docker compose --profile vaapi up -d (notice the --profile vaapi added) You can also add COMPOSE_PROFILES=vaapi to your .env instead of adding the --profile flag.

Nvidia

To enable nvidia hardware acceleration, first install necessary drivers on your system.

Then, install the nvidia-container-toolkit, you can simply follow the instructions on the official webpage or your distribution wiki.

To test if everything works, you can run sudo docker run --rm --gpus all ubuntu nvidia-smi. If your version of docker is older, you might need to add --runtime nvidia like so: sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

After that, you can now use docker compose --profile nvidia up -d to start kyoo with nvidia hardware acceleration (notice the --profile nvidia added). You can also add COMPOSE_PROFILES=nvidia to your .env instead of adding the --profile flag.

Note that most nvidia cards have an artificial limit on the number of encodes. You can confirm your card limit here. This limit can also be removed by applying an unofficial patch to you driver.