Dockerfile

FROM nvidia/cuda:12.2.0-devel-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive

# Install dependencies
RUN apt update && \
    apt install --no-install-recommends -y build-essential python3 python3-pip wget curl git cmake zlib1g-dev libblas-dev && \
    apt clean && rm -rf /var/lib/apt/lists/*

RUN wget -qO- "https://cmake.org/files/v3.18/cmake-3.18.0-Linux-x86_64.tar.gz" | tar --strip-components=1 -xz -C /usr/local

WORKDIR /app

ENV PATH=/usr/local/cuda/bin:${PATH}
ENV CUDA_HOME=/usr/local/cuda \
    LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

RUN echo 'alias nvcc="nvcc -Xcompiler \"-fPIC\" -std=c++17"' >> ~/.bashrc

# Download ggml and mmproj models from HuggingFace
RUN wget https://huggingface.co/mys/ggml_llava-v1.5-13b/raw/main/ggml-model-q4_k.gguf && \
    wget https://huggingface.co/mys/ggml_llava-v1.5-13b/raw/main/mmproj-model-f16.gguf 

# Clone and build llava-server with CUDA support
RUN git clone https://github.com/matthoffner/llava-cpp-server.git && \
    cd llava-cpp-server && \
    git submodule init && \
    git submodule update && \
    LLAMA_CUBLAS=1 make

# Create a non-root user for security reasons
RUN useradd -m -u 1000 user && \
    mkdir -p /home/user/app && \
    cp /app/ggml-model-q4_k.gguf /home/user/app && \
    cp /app/mmproj-model-f16.gguf /home/user/app

USER user
ENV HOME=/home/user

WORKDIR $HOME/app

# Expose the port
EXPOSE 8080

RUN ls -al

# Start the llava-server with models
CMD ["/app/llava-cpp-server/bin/llava-server", "-m", "/home/user/app/ggml-model-q4_k.gguf", "--mmproj", "/home/user/app/mmproj-model-f16.gguf", "--host", "0.0.0.0"]