Running PhotoPrism on a Synology NAS¶

Before setting up PhotoPrism on your NAS, we recommend that you check the Synology Knowledge Base for the CPU and memory configuration of your device.

For a good user experience, it should be a 64-bit system with at least 2 cores and 3 GB of RAM. Indexing large photo and video collections also benefits greatly from using SSD storage, especially for the database and cache files.

Should you experience problems with the installation, we recommend that you ask the Synology community for advice, as we cannot provide support for third-party software and services. Also note that RAW image conversion and TensorFlow are disabled on devices with 1 GB or less memory, and that high-resolution panoramic images may require additional swap space and/or physical memory above the recommended minimum.

Will my device be fast enough?¶

This largely depends on your expectations and the number of files you have. Most users report that PhotoPrism runs well on their Synology NAS. However, you should keep in mind:

initial indexing may take longer than on standard desktop computers
the hardware has no video transcoding support and software transcoding is generally slow

Setup¶

Setup using Portainer¶

A step-by-step guide to install PhotoPrism with Portainer can be found here.

Setup using Synology Container Manager¶

To install PhotoPrism with the Synology Container Manager, we recommend following this beginner-friendly tutorial on LinuxLinks.

First Steps¶

Our First Steps 👣 tutorial guides you through the user interface and settings to ensure your library is indexed according to your individual preferences.

Troubleshooting¶

If your device runs out of memory, the index is frequently locked, or other system resources are running low:

Try reducing the number of workers by setting PHOTOPRISM_WORKERS to a reasonably small value in docker-compose.yml, depending on the performance of your device
Make sure your device has at least 4 GB of swap space so that indexing doesn't cause restarts when memory usage spikes; RAW image conversion and video transcoding are especially demanding
If you are using SQLite, switch to MariaDB, which is better optimized for high concurrency
As a last measure, you can disable the use of TensorFlow for image classification and facial recognition

Other issues? Our troubleshooting checklists help you quickly diagnose and solve them.