If the app you're looking for isn't available in the App Store or a community app store, you can run it yourself using Portainer. Portainer is a Docker management tool that lets you deploy and manage any Docker container or Docker Compose stack directly on your DeviceFreedom.
Install Portainer from the App Store like any other app. Once installed, open it and log in with the default credentials shown after installation. You'll be prompted to change your password on first login.
From there you can deploy containers, create stacks (Docker Compose), manage volumes, and monitor everything that's running.
Portainer is a power user tool. Follow these practices to avoid issues:
- Use named Docker volumes only. Data stored in bind-mounted volumes will be lost when the Portainer app is restarted or updated. Named volumes are persisted safely.
- Watch for port conflicts. Your custom containers may conflict with ports used by other devicefreedom-os apps. Check which ports are already in use before assigning one.
- Set a restart policy. Use
unless-stopped or always as your container's restart policy so it starts back up automatically when Portainer or your DeviceFreedom restarts.
- Access containers in your browser. You can reach any container with a web interface at
devicefreedom.local:PORT — for example, if your container runs on port 4545, go to devicefreedom.local:4545.
- Custom containers don't appear on your DeviceFreedom home screen. They're managed entirely within Portainer and won't show up alongside your other apps in devicefreedom-os.
- Uninstalling Portainer removes all your custom containers and their data. Everything runs inside Portainer, so if you uninstall it, it's all gone. Keep backups of your Compose files and any important data.
- You can access custom containers remotely the same way you access your DeviceFreedom — for example, over Tailscale. You'll just need to use the container's port number.
- GPU access is available. If your device has a GPU, Portainer can pass it through to your containers — useful for AI, machine learning, or media transcoding workloads.