artifacts/oci/base/cuda/vLLM/Dockerfile

34 lines
1.2 KiB
Docker

# CUDA 12.1 + cuDNN8 runtime base — tested with recent PyTorch wheels
FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
ARG DEBIAN_FRONTEND=noninteractive
# System deps + Python
RUN apt-get update && apt-get install -y --no-install-recommends \
python3 python3-venv python3-pip git curl ca-certificates \
&& rm -rf /var/lib/apt/lists/*
ENV PIP_NO_CACHE_DIR=1 \
PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1
# Install CUDA-enabled PyTorch + vLLM
RUN pip3 install --upgrade pip \
&& pip3 install --extra-index-url https://download.pytorch.org/whl/cu121 \
torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 \
&& pip3 install vllm==0.5.2 uvicorn fastapi
EXPOSE 8000
ENV MODEL_PATH="meta-llama/Meta-Llama-3-8B-Instruct" \
VLLM_ARGS="--max-model-len 8192 --gpu-memory-utilization 0.9" \
HF_HOME=/models/.cache \
VLLM_WORKER_USE_GRAPH_EXECUTOR=1
RUN useradd -m -u 10001 app && mkdir -p /models && chown -R app:app /models
USER app
HEALTHCHECK --interval=30s --timeout=5s --start-period=30s CMD curl -fsS http://127.0.0.1:8000/v1/models || exit 1
ENTRYPOINT ["bash","-lc","vllm serve \"$MODEL_PATH\" --port 8000 --api-key dummy $VLLM_ARGS"]