funkwhale/docs/administrator_documentation/configuration_docs/optimize.md

Optimize memory usage

Funkwhale has a reasonable memory footprint. If you're running Funkwhale on a limited device, you can use these tweaks to reduce the footprint.

Reduce workers concurrency

Funkwhale uses Celery to handle asynchronous tasks. By default, Celery spawns a worker per CPU core. This can lead to higher memory usage.

You can set the number of workers using the CELERYD_CONCURRENCY variable in your .env file. For example, a value of CELERYD_CONCURRENCY=1 spawns a single worker.

Reducing the number of celery workers slows down the handling of asynchronous tasks. On larger instances, this can cause performance problems.

Switch to solo pool execution

Celery uses a prefork pool by default. This enables the server to process many tasks at the same time. You can switch to a solo pool which handles tasks one at a time. This reduces memory overhead but removes the ability to process tasks concurrently.

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:::{tab-item} Debian :sync: debian

1. Open your funkwhale-worker unit file in an editor.

   sudo nano /etc/systemd/system/funkwhale-worker.service
   

2. Add the --pool=solo flag to the ExecStart line of your unit file.

   ExecStart=/srv/funkwhale/.local/bin/poetry run celery -A --pool=solo funkwhale_api.taskapp worker -l INFO --concurrency=${CELERYD_CONCURRENCY}
   

3. Restart the Celery service.

   sudo systemctl restart funkwhale-worker.service
   

:::

:::{tab-item} Docker :sync: docker

1. Add the --pool=solo flag to the celerybeat command in docker-compose.yml.

   celerybeat:
   …
   command: celery -A --pool=solo funkwhale_api.taskapp beat --pidfile= -l INFO
   

2. Restart Celery.

   docker-compose restart celerybeat
   

::: ::::