PanSou Web is a Dockerized web application that provides a unified interface for searching cloud storage resources across multiple platforms including Telegram channels and various cloud storage plugins (Baidu, Alibaba Cloud, etc.). The application features a Vue.js frontend with a Go-based backend service, Nginx reverse proxy, and specialized management interfaces for accounts on services like QQ频道, 观影 (media), 盘链, and 微博. It includes advanced search filtering, link health checking with visibility-based triggering, result export functionality, and real-time system health monitoring. The architecture follows a traditional web application pattern with clear separation between frontend UI and backend API services.
HydroShare is a Django-based web application for sharing, managing, and publishing hydrologic data and models. It provides a rich resource management system with support for various file types (NetCDF, GeoTIFF, timeseries, etc.), fine-grained access control (groups, communities, privileges), metadata extraction, and discovery via a Vue.js frontend. The system uses PostgreSQL, Redis, Celery, MinIO (S3-compatible storage), and MongoDB for indexing. It exposes REST APIs for programmatic access and includes background tasks for bag creation, metadata extraction, and DOI registration. While primarily an application, it incorporates data engineering patterns for scientific metadata handling and file type aggregation.
Dioptra is a NIST-developed open-source platform for designing, managing, and executing reproducible machine learning experiments with a strong emphasis on adversarial robustness evaluation. The system provides a full-stack application including a REST API (Flask-RESTx with dependency injection, SQLAlchemy ORM, and Alembic migrations), a Vue.js frontend (Quasar framework), a Python client library, a plugin-based task execution engine, and a declarative YAML workflow system. It supports experiment tracking via MLflow integration, artifact versioning, user and group management, and job queuing via Redis RQ. The platform is designed to be deployed using Docker and Cookiecutter templates, with support for custom worker containers and GPU acceleration. While primarily an application for experiment lifecycle management, Dioptra also incorporates research-oriented components such as object detection modules (YOLOv1 architecture with TensorFlow backends), adversarial attack and defense plugins (using ART), and a custom type system for plugin parameter validation. The codebase is well-structured with clear separation of concerns (controllers, services, repositories), generic endpoint generators to reduce boilerplate, and a comprehensive test suite. Notable architectural patterns include unit of work, dependency injection, and a custom pyplugs decorator-based plugin registration system. However, the codebase shows signs of technical drift with some deprecated syntax, monkey-patching of third-party libraries, and complex draft/resource locking logic that may be brittle. Overall, Dioptra is a solid, professional-grade platform that effectively addresses the challenge of reproducible ML experimentation in adversarial contexts.