Dockerfile

# Use the NVIDIA CUDA runtime as a base image
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive

RUN useradd -m -u 1000 user

USER user

ENV HOME=/home/user \
    CUDA_HOME=/usr/local/cuda \
    PATH=/home/user/.local/bin:$PATH \
    LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} \
    LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH} \
    PYTHONPATH=$HOME/app \
    PYTHONUNBUFFERED=1 \
    GRADIO_ALLOW_FLAGGING=never \
    GRADIO_NUM_PORTS=1 \
    GRADIO_SERVER_NAME=0.0.0.0 \
    GRADIO_THEME=huggingface \
    GRADIO_SHARE=False \
    SYSTEM=spaces

# Set the working directory to the user's home directory
WORKDIR $HOME/app

# Install system dependencies as root
USER root
# Install basic utilities and Python 3.9
RUN apt-get update && apt-get install -y \
    wget \
    curl \
    git \
    software-properties-common \
    && add-apt-repository ppa:deadsnakes/ppa -y && \
    apt-get update && apt-get install -y \
    python3.9 \
    python3.9-distutils \
    python3.9-dev \
    && update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1 \
    && update-alternatives --install /usr/bin/pip pip /usr/bin/pip3.9 1 \
    && rm -rf /var/lib/apt/lists/*

# Verify Python and pip versions
RUN python --version && pip --version

USER user
# Copy dependencies and install them via pip
COPY . .
RUN pip install --upgrade pip
RUN pip install -r requirements.txt
RUN pip install gradio

USER root
# Configure git to trust the directory
RUN git config --global --add safe.directory /home/user/app
RUN chown -R user:user /home/user/app

USER user


# Set the environment variable to specify the GPU device
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
ENV CUDA_VISIBLE_DEVICES=0

# Command to run your application
CMD ["python", "app.py"]