docker-compose.gpu.yml

# ─────────────────────────────────────────────────────────────────────────────
# ChessEcon — GPU Override (docker-compose.gpu.yml)
#
# Usage:
#   docker compose -f docker-compose.yml -f docker-compose.gpu.yml up
#
# Requirements:
#   - NVIDIA GPU with CUDA 12.1+ support
#   - nvidia-container-toolkit installed on the host
#   - Run: sudo nvidia-ctk runtime configure --runtime=docker
# ─────────────────────────────────────────────────────────────────────────────

services:

  chessecon:
    build:
      target: backend-gpu
    image: chessecon:gpu
    environment:
      CUDA_VISIBLE_DEVICES: "${CUDA_VISIBLE_DEVICES:-0}"
      TORCH_DTYPE: "${TORCH_DTYPE:-bfloat16}"
      USE_FLASH_ATTENTION: "${USE_FLASH_ATTENTION:-true}"
      DEVICE: "cuda"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  trainer:
    build:
      target: backend-gpu
    image: chessecon:gpu
    environment:
      CUDA_VISIBLE_DEVICES: "${CUDA_VISIBLE_DEVICES:-all}"
      TORCH_DTYPE: "${TORCH_DTYPE:-bfloat16}"
      USE_FLASH_ATTENTION: "${USE_FLASH_ATTENTION:-true}"
      DEVICE: "cuda"
      # Multi-GPU training
      NPROC_PER_NODE: "${NPROC_PER_NODE:-1}"
      # Larger batches on GPU
      GAMES_PER_BATCH: "${GAMES_PER_BATCH:-16}"
      BATCH_SIZE: "${BATCH_SIZE:-8}"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]