feat: Docker support for L1 server
- Dockerfile for L1 server/worker - docker-compose.yml with Redis - Environment variables for Redis URL and cache dir 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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18
Dockerfile
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18
Dockerfile
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FROM python:3.11-slim
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WORKDIR /app
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# Install dependencies
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application
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COPY . .
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# Create cache directory
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RUN mkdir -p /data/cache
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ENV PYTHONUNBUFFERED=1
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# Default command runs the server
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CMD ["python", "server.py"]
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@@ -5,12 +5,15 @@ Distributed rendering for the Art DAG system.
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Uses the foundational artdag language from GitHub.
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"""
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import os
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from celery import Celery
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REDIS_URL = os.environ.get('REDIS_URL', 'redis://localhost:6379/5')
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app = Celery(
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'art_celery',
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broker='redis://localhost:6379/5',
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backend='redis://localhost:6379/5',
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broker=REDIS_URL,
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backend=REDIS_URL,
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include=['tasks']
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)
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58
docker-compose.yml
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58
docker-compose.yml
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version: "3.8"
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services:
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redis:
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image: redis:7-alpine
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volumes:
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- redis_data:/data
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networks:
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- artdag
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deploy:
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replicas: 1
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restart_policy:
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condition: on-failure
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l1-server:
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build: .
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image: git.rose-ash.com/art-dag/l1-server:latest
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ports:
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- "8100:8100"
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environment:
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- REDIS_URL=redis://redis:6379/5
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volumes:
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- l1_cache:/data/cache
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depends_on:
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- redis
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networks:
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- artdag
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deploy:
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replicas: 1
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restart_policy:
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condition: on-failure
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l1-worker:
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build: .
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image: git.rose-ash.com/art-dag/l1-server:latest
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command: celery -A celery_app worker --loglevel=info
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environment:
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- REDIS_URL=redis://redis:6379/5
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volumes:
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- l1_cache:/data/cache
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- effects:/app/effects
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depends_on:
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- redis
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networks:
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- artdag
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deploy:
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replicas: 2
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restart_policy:
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condition: on-failure
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volumes:
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redis_data:
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l1_cache:
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effects:
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networks:
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artdag:
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driver: overlay
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13
server.py
13
server.py
@@ -21,16 +21,23 @@ from fastapi.responses import FileResponse
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from pydantic import BaseModel
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import redis
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from urllib.parse import urlparse
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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 directory (use /data/cache in Docker, ~/.artdag/cache locally)
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CACHE_DIR = Path(os.environ.get("CACHE_DIR", str(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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REDIS_URL = os.environ.get('REDIS_URL', 'redis://localhost:6379/5')
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parsed = urlparse(REDIS_URL)
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redis_client = redis.Redis(
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host=parsed.hostname or 'localhost',
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port=parsed.port or 6379,
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db=int(parsed.path.lstrip('/') or 0)
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)
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RUNS_KEY_PREFIX = "artdag:run:"
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