Dockerfile-gpu

# Define the CUDA SDK version you need
ARG CUDA_IMAGE="12.1.1-devel-ubuntu22.04"
FROM nvidia/cuda:${CUDA_IMAGE}

ENV DEBIAN_FRONTEND=noninteractive

WORKDIR /app

RUN apt-get update && apt-get upgrade -y \
    && apt-get install -y git build-essential \
    python3 python3-pip python3.10-venv  libpq-dev gcc wget \
    ocl-icd-opencl-dev opencl-headers clinfo \
    libclblast-dev libopenblas-dev \
    && mkdir -p /etc/OpenCL/vendors && echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd

# Create a virtual environment and activate it
RUN python3 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"

# Install Python dependencies from requirements.txt
COPY requirements.txt .
RUN pip install --upgrade pip && \
    pip install --no-cache-dir -r requirements.txt

# Running nltk setup as you mentioned
RUN python3.10 -c "import nltk; nltk.download('punkt')" && \
    python3.10 -c "import nltk; nltk.download('averaged_perceptron_tagger')"

# Copy the application code
COPY . .

ENV CUDA_DOCKER_ARCH=all
ENV LLAMA_CUBLAS=1

RUN CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python==0.2.7 --force-reinstall --upgrade --no-cache-dir

# Make necessary scripts executable
RUN chmod +x ./entrypoint.sh ./wait-for-it.sh ./install_tool_dependencies.sh ./entrypoint_celery.sh

# Set environment variable to point to the custom libllama.so
# ENV LLAMA_CPP_LIB=/app/llama.cpp/libllama.so

EXPOSE 8001

CMD ["./entrypoint.sh"]