Implementation Catalog

This catalog is a collection of repositories for various Machine Learning techniques and algorithms implemented at Vector Institute.

🌐 Web Interface

The catalog is available as an interactive website featuring:

• Fast Search: Instant client-side search powered by Pagefind
• Filtering: Browse by type (applied-research, bootcamp, tool)
• Responsive Design: Works on all devices
• Static Deployment: Zero backend, hosted on GitHub Pages

Local Development

# Navigate to the catalog directory
cd catalog

# Install dependencies
npm install

# Run development server
npm run dev

📊 Analytics Dashboard

Executive dashboard for catalog performance metrics:

• Key Metrics: Aggregate stats across all repositories
• Top Performers: Highest starred, most visited, and most cloned repos
• Complete Overview: Sortable table of all repositories
• Auto-Update: Weekly collection via GitHub Actions (Mondays at 00:00 UTC)

GitHub Metrics

Manual Collection: python scripts/collect_github_metrics.py (requires gh CLI)

Note: Traffic data (views/clones) requires a Personal Access Token with repo permissions. Add as METRICS_GITHUB_TOKEN secret in repository settings. Without it, traffic metrics will show "—" but basic metrics (stars, forks) still work.

PyPI Metrics

Collects download statistics and package metadata for tools published on PyPI:

• Download counts (last day, week, month, and total)
• Package version and release date
• Historical tracking for trend analysis

Manual Collection: python scripts/collect_pypi_metrics.py

📋 Repository Information

Each repository in the catalog is defined by a YAML file in the repositories/ directory with the following fields:

Required Fields

• name: Repository name
• repo_id: GitHub repository identifier (org/repo)
• description: Brief introduction to the implementation
• implementations: List of ML algorithms and techniques with optional reference URLs
• type: Classification - tool, bootcamp, or applied-research
• year: Publication or release year

Optional Fields

• public_datasets: Links to publicly available datasets used
• github_url: Custom GitHub URL (overrides repo_id)
• paper_url: Link to associated research paper
• bibtex: Citation reference for the paper
• platform_url: URL for deployment platform (e.g., Coder)
• package_name: PyPI package name for published tools