OpenAudio/docker-compose.gpu.yml at main · davidamacey/OpenAudio · GitHub

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# docker-compose.gpu.yml
# Optional overlay for GPU runtime acceleration (NVIDIA)
#
# The ML worker images include CUDA libraries via PyTorch (works on CPU too).
# This overlay enables GPU RUNTIME access for ML workers on systems with NVIDIA GPUs.
# Without this overlay, PyTorch falls back to CPU inference (still functional, just slower).
#
# Usage:
#   ./openaudio.sh start dev              # Auto-detects GPU and applies if available
#   OR manually:
#   docker compose -f docker-compose.yml -f docker-compose.override.yml -f docker-compose.gpu.yml up
#
# Note: This file is automatically included by openaudio.sh when NVIDIA GPU is detected.
# On macOS or systems without NVIDIA GPU, this file is not loaded.
#
# Configuration:
#   GPU_DEVICE_ID - GPU device to use (default: 0)
#   Set in .env file: GPU_DEVICE_ID=1

services:
  celery-torch-worker:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]
              device_ids: ['${GPU_DEVICE_ID:-0}']
    environment:
      - NVIDIA_VISIBLE_DEVICES=${GPU_DEVICE_ID:-0}
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
      - CUDA_VISIBLE_DEVICES=${GPU_DEVICE_ID:-0}
      - TORCH_DEVICE=cuda
      - USE_GPU=true

  celery-sam-worker:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]
              device_ids: ['${GPU_DEVICE_ID:-0}']
    environment:
      - NVIDIA_VISIBLE_DEVICES=${GPU_DEVICE_ID:-0}
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
      - CUDA_VISIBLE_DEVICES=${GPU_DEVICE_ID:-0}
      - TORCH_DEVICE=cuda
      - USE_GPU=true