e4fd5eb010
- 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>
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": "..."}
}
}