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File size: 1,793 Bytes
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# ChessEcon — OpenEnv 0.1 compliant multi-agent chess economy
#
# White: Qwen/Qwen2.5-0.5B-Instruct (GRPO training target)
# Black: meta-llama/Llama-3.2-1B-Instruct (fixed opponent)
#
# Quick start:
# docker compose up --build
services:
backend:
build:
context: ./backend
dockerfile: Dockerfile
image: chessecon-backend:latest
container_name: chessecon-backend
restart: unless-stopped
ports:
- "8008:8000"
env_file:
- ./backend/.env
environment:
- DEVICE=cuda # GPU inference
- HOST=0.0.0.0
- PORT=8000
- WHITE_MODEL=/models/Qwen_Qwen2.5-0.5B-Instruct
- BLACK_MODEL=/models/meta-llama_Llama-3.2-1B-Instruct
- HF_HUB_OFFLINE=1
- CUDA_VISIBLE_DEVICES=0 # use first GPU
volumes:
- ./training/models:/models:ro # model weights
- /home/minasm/.cache/huggingface:/root/.cache/huggingface:ro # HF cache
- checkpoints:/app/checkpoints # LoRA checkpoints
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 5
start_period: 180s
dashboard:
image: nginx:alpine
container_name: chessecon-dashboard
restart: unless-stopped
ports:
- "3006:80"
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- ./frontend/dist/public:/usr/share/nginx/html:ro
- ./nginx.conf:/etc/nginx/conf.d/default.conf:ro
volumes:
checkpoints:
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