gilesb e4fd5eb010 Integrate artdag cache with deletion rules
- Add cache_manager.py with L1CacheManager wrapping artdag Cache
- Add L2SharedChecker for checking published status via L2 API
- Update server.py to use cache_manager for storage
- Update DELETE /cache/{content_hash} to enforce deletion rules
- Add DELETE /runs/{run_id} endpoint for discarding runs
- Record activities when runs complete for deletion tracking
- Add comprehensive tests for cache manager

Deletion rules enforced:
- Cannot delete items published to L2
- Cannot delete inputs/outputs of runs
- Can delete orphaned items
- Runs can only be discarded if no items are shared

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-08 00:51:18 +00:00
2026-01-07 17:16:43 +00:00

Art Celery

L1 rendering server for the Art DAG system. Manages distributed rendering jobs via Celery workers.

Dependencies

  • artdag (GitHub): Core DAG execution engine
  • artdag-effects (rose-ash): Effect implementations
  • Redis: Message broker, result backend, and run persistence

Setup

# Install Redis
sudo apt install redis-server

# Install Python dependencies
pip install -r requirements.txt

# Start a worker
celery -A celery_app worker --loglevel=info

# Start the L1 server
python server.py

L1 Server API

Interactive docs: http://localhost:8100/docs

Endpoints

Method Path Description
GET / Server info
POST /runs Start a rendering run
GET /runs List all runs
GET /runs/{run_id} Get run status
GET /cache List cached content hashes
GET /cache/{hash} Download cached content
POST /cache/import?path= Import local file to cache
GET /assets List known assets

Start a run

curl -X POST http://localhost:8100/runs \
  -H "Content-Type: application/json" \
  -d '{"recipe": "dog", "inputs": ["33268b6e..."], "output_name": "my-output"}'

Check run status

curl http://localhost:8100/runs/{run_id}

Storage

  • Cache: ~/.artdag/cache/ (content-addressed files)
  • Runs: Redis db 5, keys artdag:run:* (persists across restarts)

CLI Usage

# Render cat through dog effect
python render.py dog cat --sync

# Render cat through identity effect
python render.py identity cat --sync

# Submit async (don't wait)
python render.py dog cat

Architecture

server.py (L1 Server - FastAPI)
    │
    ├── POST /runs → Submit job
    │       │
    │       ▼
    │   celery_app.py (Celery broker)
    │       │
    │       ▼
    │   tasks.py (render_effect task)
    │       │
    │       ├── artdag (GitHub) - DAG execution
    │       └── artdag-effects (rose-ash) - Effects
    │               │
    │               ▼
    │           Output + Provenance
    │
    └── GET /cache/{hash} → Retrieve output

Provenance

Every render produces a provenance record:

{
  "task_id": "celery-task-uuid",
  "rendered_at": "2026-01-07T...",
  "rendered_by": "@giles@artdag.rose-ash.com",
  "output": {"name": "...", "content_hash": "..."},
  "inputs": [...],
  "effects": [...],
  "infrastructure": {
    "software": {"name": "infra:artdag", "content_hash": "..."},
    "hardware": {"name": "infra:giles-hp", "content_hash": "..."}
  }
}
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