title: SimpleAudit Studio emoji: 🔍 colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false

SimpleAudit Studio — AI Model Audit Platform

A production-quality platform for running reproducible AI model audits using the SimpleAudit engine (Target → Auditor → Judge).

Demo Credentials

• Username: admin
• Password: admin123

What You Can Do

• Browse the scenario library and create new audit scenarios
• Register model endpoints (OpenAI-compatible APIs)
• Launch audits and watch live progress
• Compare results across runs
• Explore the visualizer for detailed analysis

Quick Start (Minimal Config)

Run SimpleAudit Studio locally in seconds — no Docker, no Postgres, no infrastructure:

uvx --from "git+https://github.com/SushantGautam/SimpleAuditStudio.git" simpleaudit-studio

The package is not published to PyPI, so uvx builds it from the pinned git ref on first run (cached afterwards). This boots a full local stack in one process:

• Django web UI at http://localhost:8000 (login: admin / admin12345)
• Hatchet workflow engine (embedded mode — real persistent queue + worker, no Docker)
• SQLite database (auto-created, persists across restarts)
• Mock model server (pre-seeded so you can try an audit immediately)

First run downloads a ~53 MB Hatchet sidecar binary (cached in ~/.hatchet/). Subsequent starts are faster. Press Ctrl+C to stop.

Using Real Models

The pre-seeded model connections point at a built-in mock server so you can explore the UI without any setup. To run real audits, update your model connections in the UI (Models page) to point at any OpenAI-compatible endpoint:

Set the API key in the connection form (or leave blank for local servers that don't require auth). Then launch an audit from the Scenario Library.

When to Use What

The minimal config uses the same code paths as the full deployment — same worker, same engine, same queue semantics. The only differences are SQLite instead of Postgres and embedded Hatchet instead of a standalone server.

Architecture

This Space runs the full stack in a single container:

Browser → Django/Gunicorn (:7860)
              ↓
         PostgreSQL 16 (domain + queue DBs)
              ↓
         Hatchet Server (durable job queue)
              ↓
         Worker (CPU pool) → SimpleAudit Engine
              Target → Auditor → Judge

Notes

• Ephemeral storage: All data is lost when the Space restarts or is redeployed. This is a demo, not a persistent deployment.
• CPU-only: Audits run on CPU. For GPU-backed model inference, point your model endpoint at an external API.
• Cold start: The first request after idle may take 30–60 seconds while all services initialize.

Self-Hosting

Quick start (single container, no clone needed)

Build and run directly from GitHub — no local checkout required:

docker build \
  https://github.com/SushantGautam/SimpleAuditStudio.git#main \
  -t simpleaudit-studio

docker run -d -p 7860:7860 --name simpleaudit simpleaudit-studio

Open http://localhost:7860 — log in with admin / admin123.

Note: This single-container mode uses ephemeral storage. Data is lost on restart. For persistent deployments with separate Postgres/Hatchet/worker containers, use Docker Compose below.

Full stack (Docker Compose, persistent)

git clone https://github.com/SushantGautam/SimpleAuditStudio
cd SimpleAuditStudio
cp .env.example .env
# edit POSTGRES_PASSWORD and BOOTSTRAP_ADMIN_PASSWORD at minimum
docker compose up -d

Services: Web UI (:8000), PostgreSQL, Hatchet queue (:8888), Worker. Optional profiles: --profile storage (MinIO), --profile mock (mock model API).

See docs/deployment.md for production hardening, GPU workers, backups, and upgrade procedures.