feat: L1 server with persistent run storage
- FastAPI server on port 8100
- POST /runs to start rendering jobs
- GET /runs/{id} to check status
- Cache and run persistence in Redis
- Auto-generated API docs at /docs
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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gilesb
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server.py
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server.py
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#!/usr/bin/env python3
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"""
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Art DAG L1 Server
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Manages rendering runs and provides access to the cache.
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- POST /runs - start a run (recipe + inputs)
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- GET /runs/{run_id} - get run status/result
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- GET /cache/{content_hash} - get cached content
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"""
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import hashlib
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import json
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import os
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import uuid
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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import redis
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from celery_app import app as celery_app
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from tasks import render_effect
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# Cache directory
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CACHE_DIR = Path.home() / ".artdag" / "cache"
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CACHE_DIR.mkdir(parents=True, exist_ok=True)
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# Redis for persistent run storage
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redis_client = redis.Redis(host='localhost', port=6379, db=5)
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RUNS_KEY_PREFIX = "artdag:run:"
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def save_run(run: "RunStatus"):
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"""Save run to Redis."""
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redis_client.set(f"{RUNS_KEY_PREFIX}{run.run_id}", run.model_dump_json())
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def load_run(run_id: str) -> Optional["RunStatus"]:
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"""Load run from Redis."""
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data = redis_client.get(f"{RUNS_KEY_PREFIX}{run_id}")
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if data:
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return RunStatus.model_validate_json(data)
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return None
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def list_all_runs() -> list["RunStatus"]:
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"""List all runs from Redis."""
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runs = []
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for key in redis_client.scan_iter(f"{RUNS_KEY_PREFIX}*"):
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data = redis_client.get(key)
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if data:
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runs.append(RunStatus.model_validate_json(data))
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return sorted(runs, key=lambda r: r.created_at, reverse=True)
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app = FastAPI(
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title="Art DAG L1 Server",
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description="Distributed rendering server for Art DAG",
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version="0.1.0"
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)
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class RunRequest(BaseModel):
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"""Request to start a run."""
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recipe: str # Recipe name (e.g., "dog", "identity")
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inputs: list[str] # List of content hashes
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output_name: Optional[str] = None
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class RunStatus(BaseModel):
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"""Status of a run."""
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run_id: str
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status: str # pending, running, completed, failed
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recipe: str
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inputs: list[str]
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output_name: str
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created_at: str
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completed_at: Optional[str] = None
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output_hash: Optional[str] = None
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error: Optional[str] = None
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celery_task_id: Optional[str] = None
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def file_hash(path: Path) -> str:
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"""Compute SHA3-256 hash of a file."""
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hasher = hashlib.sha3_256()
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with open(path, "rb") as f:
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for chunk in iter(lambda: f.read(65536), b""):
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hasher.update(chunk)
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return hasher.hexdigest()
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def cache_file(source: Path) -> str:
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"""Copy file to cache, return content hash."""
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content_hash = file_hash(source)
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cache_path = CACHE_DIR / content_hash
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if not cache_path.exists():
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import shutil
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shutil.copy2(source, cache_path)
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return content_hash
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@app.get("/")
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async def root():
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"""Server info."""
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return {
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"name": "Art DAG L1 Server",
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"version": "0.1.0",
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"cache_dir": str(CACHE_DIR),
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"runs_count": len(list_all_runs())
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}
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@app.post("/runs", response_model=RunStatus)
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async def create_run(request: RunRequest):
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"""Start a new rendering run."""
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run_id = str(uuid.uuid4())
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# Generate output name if not provided
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output_name = request.output_name or f"{request.recipe}-{run_id[:8]}"
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# Create run record
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run = RunStatus(
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run_id=run_id,
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status="pending",
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recipe=request.recipe,
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inputs=request.inputs,
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output_name=output_name,
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created_at=datetime.now(timezone.utc).isoformat()
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)
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# Submit to Celery
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# For now, we only support single-input recipes
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if len(request.inputs) != 1:
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raise HTTPException(400, "Currently only single-input recipes supported")
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input_hash = request.inputs[0]
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task = render_effect.delay(input_hash, request.recipe, output_name)
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run.celery_task_id = task.id
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run.status = "running"
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save_run(run)
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return run
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@app.get("/runs/{run_id}", response_model=RunStatus)
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async def get_run(run_id: str):
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"""Get status of a run."""
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run = load_run(run_id)
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if not run:
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raise HTTPException(404, f"Run {run_id} not found")
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# Check Celery task status if running
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if run.status == "running" and run.celery_task_id:
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task = celery_app.AsyncResult(run.celery_task_id)
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if task.ready():
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if task.successful():
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result = task.result
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run.status = "completed"
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run.completed_at = datetime.now(timezone.utc).isoformat()
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run.output_hash = result.get("output", {}).get("content_hash")
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# Cache the output
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output_path = Path(result.get("output", {}).get("local_path", ""))
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if output_path.exists():
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cache_file(output_path)
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else:
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run.status = "failed"
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run.error = str(task.result)
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# Save updated status
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save_run(run)
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return run
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@app.get("/runs")
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async def list_runs():
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"""List all runs."""
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return list_all_runs()
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@app.get("/cache/{content_hash}")
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async def get_cached(content_hash: str):
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"""Get cached content by hash."""
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cache_path = CACHE_DIR / content_hash
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if not cache_path.exists():
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raise HTTPException(404, f"Content {content_hash} not in cache")
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return FileResponse(cache_path)
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@app.get("/cache")
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async def list_cache():
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"""List cached content hashes."""
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return [f.name for f in CACHE_DIR.iterdir() if f.is_file()]
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# Known assets (bootstrap data)
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KNOWN_ASSETS = {
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"cat": "33268b6e167deaf018cc538de12dbe562612b33e89a749391cef855b320a269b",
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}
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@app.get("/assets")
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async def list_assets():
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"""List known assets."""
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return KNOWN_ASSETS
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@app.post("/cache/import")
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async def import_to_cache(path: str):
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"""Import a local file to cache."""
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source = Path(path)
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if not source.exists():
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raise HTTPException(404, f"File not found: {path}")
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content_hash = cache_file(source)
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return {"content_hash": content_hash, "cached": True}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8100)
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