Dockerfile

FROM pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    cmake \
    build-essential \
    libgl1-mesa-glx \
    libglib2.0-0 \
    ffmpeg \
    python3.8 \
    python3-pip \
    python3.8-dev \
    && rm -rf /var/lib/apt/lists/*

# Create a symlink for python
RUN ln -s /usr/bin/python3 /usr/bin/python

RUN useradd -m -u 1000 user

USER user

ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH \
    PYTHONPATH=$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 CUDA_HOME environment variable
ENV CUDA_HOME=/usr/local/cuda-11.8
ENV TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0;7.5;8.0;8.6+PTX;8.9;9.0"
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}

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

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

# Clone the repository (adjust the URL if needed)
RUN git clone --recursive https://github.com/jnjaby/KEEP.git .

# Copy the app.py script and requirements file into the container
COPY --chown=user:user app.py .
COPY --chown=user:user requirements_HF.txt .

# Install Python dependencies
RUN pip install --upgrade pip && \
    pip install -r requirements_HF.txt && \
    pip install gradio && \
    pip install "numpy<1.25,>=1.18" && \
    pip install cupy-cuda11x && \
    pip install ffmpeg-python dlib

# Install basicsr
RUN cd basicsr && python setup.py develop

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