Production deployment

Pre-built images from GHCR (recommended)

The fastest deployment path: no source code required. Images are published automatically on every release to GitHub Container Registry.

curl -O https://raw.githubusercontent.com/lad-sapienza/BraDypUS/v5/bradypus.yml
docker compose -f bradypus.yml pull
docker compose -f bradypus.yml up -d

To pin a specific version:

BDUS_VERSION=5.0.3 docker compose -f bradypus.yml up -d

See Deploy with pre-built images for the full guide.

Build from source

If you have cloned the repository and want to build the images locally:

docker compose -f docker-compose.prod.yml up -d --build

In production, the node container (Vite dev server) is replaced by a static Nginx container serving the pre-built dist/ files.

Environment variables

Set these in docker-compose.prod.yml or via a .env file:

Variable	Default	Description
BRADYPUS_DEBUG	0	Set to 1 only for debugging
BRADYPUS_ALLOW_NEW_APP	0	Set to 1 temporarily to create the first app
BRADYPUS_CORS_ORIGIN	—	Space-separated allowed origins for cross-origin API access

Shared hosting (PHP-only)

For hosts that support PHP but not Docker, follow the Manual installation guide and upload the files via SFTP/SSH.

SQLite on shared hosting

SQLite databases are stored as files in projects/{app}/db/. They can be downloaded and uploaded like any other file, making backups and migrations trivial.

HTTPS

Always run BraDypUS behind HTTPS in production. JWT tokens are transmitted in headers — without HTTPS they are exposed in transit.

When using a reverse proxy (Nginx, Caddy, Traefik), set the X-Forwarded-Proto header so BraDypUS knows the connection is over HTTPS.

Data persistence

All application data (SQLite databases, uploaded files, configuration, backups) lives in the projects/ directory inside the bdus-api container. Both Compose files map this directory to a persistent Docker named volume called projects_data, so data survives container restarts and image updates.

To find where Docker stores the volume on disk:

docker volume inspect $(docker volume ls -q | grep projects_data)

For backup and restore commands, see the Data persistence section of the pre-built images guide.

WARNING

On Docker Desktop (Mac / Windows) the volume lives inside the Docker VM and is not directly accessible from the host filesystem. Use docker run --rm -v projects_data:/data alpine ... to read or write files inside the volume.