# Use an official PyTorch image with CUDA support as the base image FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime # Install Git and system libraries required for OpenGL without interactive prompts ENV DEBIAN_FRONTEND=noninteractive # Install Git and OpenGL libraries, and libglib2.0 RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0 # Set up a new user named "user" with user ID 1000 RUN useradd -m -u 1000 user # Switch to the "user" 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 the working directory to the user's home directory WORKDIR $HOME/app # Clone your repository or add your code to the container RUN git clone -b main https://github.com/fffiloni/daclip-uir $HOME/app # Install dependencies RUN pip install --no-cache-dir -r requirements.txt gradio # Copy the pretrained file and folder into the working directory COPY pretrained_daclip_uir/ $HOME/app/universal-image-restoration/config/daclip-sde/pretrained/ RUN find $HOME/app # Set the working directory to the app.py file's location #WORKDIR $HOME/app/universal-image-restoration/config/daclip-sde/ # 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/universal-image-restoration/config/daclip-sde/ # Run your app.py script CMD ["python", "app.py"]