Dockerfile

FROM nvidia/cuda:12.5.1-cudnn-devel-ubuntu22.04

ARG DEBIAN_FRONTEND=noninteractive

ENV PYTHONUNBUFFERED=1

RUN apt-get update && apt-get install -y \
  build-essential \
  python3.9 \
  python3-pip \
  git \
  ffmpeg \
  libcudnn8 \
  libcudnn8-dev \
  sudo

RUN useradd -m docker && echo "docker:docker" | chpasswd && adduser docker sudo

 
WORKDIR /code

COPY ./requirements.txt /code/requirements.txt

# make sure cudnn errors go away
#RUN export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:`python3 -c 'import os; import torch; print(os.path.dirname(torch.__file__) +"/lib")'`
RUN export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; import torch; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__) + ":" + os.path.dirname(torch.__file__) +"/lib")'`:$LD_LIBRARY_PATH

# Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1099 user
# Switch to the "user" user
#USER user
# Set home to the user's home directory
ENV PYTHONUNBUFFERED=1 \
	GRADIO_ALLOW_FLAGGING=never \
	GRADIO_NUM_PORTS=1 \
	GRADIO_SERVER_NAME=0.0.0.0 \
	GRADIO_THEME=huggingface \
	SYSTEM=spaces

RUN export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; import torch; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__) + ":" + os.path.dirname(torch.__file__) +"/lib")'`:$LD_LIBRARY_PATH
RUN pip3 install --no-cache-dir --upgrade -r /code/requirements.txt

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

# Copy the current directory contents into the container at $HOME/app setting the owner to the user
COPY --chown=user . $HOME/app

CMD ["python3", "app.py"]