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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 1000 user
# Switch to the "user" user
USER user
# Set home to the user's home directory
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 \
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"] |