Archipelago Docker Deployment

Updated: July 1st 2024

Previously Updated: January 30th 2024

This repository serves as bootstrap for a Archipelago 1.4.0 deployment on a localhost for development/testing/customizing via Docker and provides a more unified experience this time:

• minio.io (latest)for local S3 with Console.
• Apache Solr 9.2.1 with the wizardly Solr OCR Highlight library v0.8.4 built by the Developement Team at the Bavarian State Library. Thanks Johannes Baiter and team.
• MySQL 8.x (amd64/x86)/MariaDB 10.6.x(Arm64/M1/M2/M3)
• NGINX 11
• Custom PHP-FPM 8.1 multi architecture, fine-tuned for Drupal 10 , WARC to WACZ processing, Tesseract 5 with JP2 support, PDFAlto and Composer 2.x, Drush 12, etc
• Natural Language Processing via NLPWEB64 multi architecture with FastText Language detection (Thanks Mike Bennet!) or alternatively new ML (Image similarity: YOLO,MobileNet,Insightface and Text transformer: SBERT)
• Cantaloupe 6.0.1 Snapshot multi architecture as IIIF2/3 Server with Video Frame extraction and PDF support (with custom fix for tiled PDF)
• A Skeleton Project setup to run latest Version of Drupal (10.2.x), our new Bootstrap 5 theme and Strawberry Field modules on 1.4.0 & friends on 0.8.0
• Complete support for Apple Silicon M1 Machines and in general arm64 architecture Chips like Raspberry Pi 4, with specially built arm64 docker containers. The only differences now between deployment strategies is the DB. Blazing fast OCR.

The skeleton project contains all the pieces needed to run a local deployment of a vanilla Archipelago including (YES!) content provided as an optional feature from archipelago-recyclables

Starting from ZERO

This is the recommended, simplest way for this release. There are a too many, tons of fun new features, Metadata Displays, Webforms, New formatters and Twig extensions, improved viewers, new and improved JS libraries, OpenCV/Face Detection, smarter NLP, File composting, better HUGE import/update capabilities, bug fixes (yes so many) so please try them out. The team has also updated the DEMO AMI set (Content) to showcase metadata/display improvements.

macOS Intel or Apple Silicon M1/M2/M3:

Step by Step deployment on macOS

Ubuntu 18.04 or 20.04:

Step by Step deployment on Ubuntu

Windows 10 or 11:

Step by Step deployment on Windows

More fun if you add content:

One-Step Demo content ingest

If you like it (or not), want new features, or want to be part of making this better (documenting, coding and planning) let us know. Make your voice and opinion be heard, this is a community effort.

Caring & Coding + Fixing + Testing

• Diego Pino
• Allison Sherrick
• Giancarlo Birello

Acknowledgments

This software is a Metropolitan New York Library Council Open-Source initiative and part of the Archipelago Commons project.

License

GPLv3