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services:
backend:
container_name: mindsearch-backend
build:
context: .
dockerfile: backend.dockerfile
image: mindsearch/backend:latest
restart: unless-stopped
# Uncomment the following line to force using local build
# pull: never
ports:
- "8002:8002"
environment:
- PYTHONUNBUFFERED=1
# - OPENAI_API_KEY=${OPENAI_API_KEY:-}
- OPENAI_API_BASE=${OPENAI_API_BASE:-https://api.openai.com/v1}
# - QWEN_API_KEY=${QWEN_API_KEY:-}
# - SILICON_API_KEY=${SILICON_API_KEY:-}
command: python -m mindsearch.app --lang ${LANG:-cn} --model_format ${MODEL_FORMAT:-internlm_server}
volumes:
- /root/.cache:/root/.cache
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
# GPU support explanation:
# The current configuration has been tested with NVIDIA GPUs. If you use other types of GPUs, you may need to adjust the configuration.
# For AMD GPUs, you can try using the ROCm driver by modifying the configuration as follows:
# deploy:
# resources:
# reservations:
# devices:
# - driver: amd
# count: 1
# capabilities: [gpu]
#
# For other GPU types, you may need to consult the respective Docker GPU support documentation.
# In theory, any GPU supported by PyTorch should be configurable here.
# If you encounter issues, try the following steps:
# 1. Ensure the correct GPU drivers are installed on the host
# 2. Check if your Docker version supports your GPU type
# 3. Install necessary GPU-related libraries in the Dockerfile
# 4. Adjust the deploy configuration here to match your GPU type
#
# Note: After changing GPU configuration, you may need to rebuild the image.
frontend:
container_name: mindsearch-frontend
build:
context: .
dockerfile: frontend.dockerfile
image: mindsearch/frontend:latest
restart: unless-stopped
# Uncomment the following line to force using local build
# pull: never
ports:
- "8080:8080"
depends_on:
- backend
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