File size: 2,060 Bytes
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services:
stack-2.9:
build:
context: .
dockerfile: Dockerfile
args:
- PYTHON_VERSION=3.10
- VLLM_VERSION=0.6.3
- CUDA_VERSION=12.1.0
container_name: stack-2.9-server
restart: unless-stopped
ports:
- "${STACK_PORT:-8000}:8000"
environment:
# Model configuration
- MODEL_ID=${MODEL_ID:-TheBloke/Llama-2-7B-Chat-AWQ}
- HUGGING_FACE_TOKEN=${HUGGING_FACE_TOKEN:-}
- QUANTIZATION=${QUANTIZATION:-awq}
# vLLM engine parameters
- TENSOR_PARALLEL_SIZE=${TENSOR_PARALLEL_SIZE:-1}
- GPU_MEMORY_UTILIZATION=${GPU_MEMORY_UTILIZATION:-0.9}
- MAX_MODEL_LEN=${MAX_MODEL_LEN:-4096}
- MAX_NUM_SEQS=${MAX_NUM_SEQS:-64}
- MAX_NUM_BATCHED_TOKENS=${MAX_NUM_BATCHED_TOKENS:-4096}
- ENFORCE_EAGER=${ENFORCE_EAGER:-false}
- DISABLE_LOG_STATS=${DISABLE_LOG_STATS:-false}
# Server configuration
- HOST=${HOST:-0.0.0.0}
- PORT=${PORT:-8000}
- MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-/home/vllm/.cache/huggingface}
# Performance tuning
- OMP_NUM_THREADS=${OMP_NUM_THREADS:-4}
- CUDA_LAUNCH_BLOCKING=${CUDA_LAUNCH_BLOCKING:-0}
- CUDNN_LOGINFO_DBG=1
volumes:
# Model cache persistence
- model_cache:/home/vllm/.cache/huggingface:rw
# Optional: mount custom models
- ./models:/app/models:ro
networks:
- stack-network
# GPU configuration - uncomment for GPU support
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
# Runtime configuration
runtime: nvidia
# Health check
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 60s
# Resource limits
# mem_limit: ${MEM_LIMIT:-8g}
# mem_reservation: ${MEM_RESERVATION:-4g}
volumes:
model_cache:
driver: local
networks:
stack-network:
driver: bridge
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