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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.

  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/venv/bin/celery \
       --app funkwhale_api.taskapp \
       --pool solo \
       worker \
       --loglevel INFO \
  3. Restart the Celery service.

    sudo systemctl restart funkwhale-worker.service
  1. Add the --pool=solo flag to the celerybeat command in docker-compose.yml.

    command: celery -A --pool=solo funkwhale_api.taskapp beat --pidfile= -l INFO
  2. Restart Celery.

    sudo docker compose restart celerybeat