# Use an official PyTorch image with CUDA support as the base image FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime RUN apt-get update && apt-get install -y build-essential # Install a specific version of pip (e.g., 20.2.4) #RUN pip install pip==20.2.4 # Install Git and system libraries required for OpenGL without interactive prompts ENV DEBIAN_FRONTEND=noninteractive # Install Git, OpenGL libraries, and libglib2.0 RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0 # Set the pip resolver to "legacy" RUN pip config set global.resolver legacy #RUN apt-get update && apt-get install -y ninja-build # 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 environment variables 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 COPY . . # Install dependencies # Use the new pip resolver and always take the latest version if not specified RUN pip install --use-feature=fast-deps -r requirements.txt gradio # Update package lists and install other dependencies as needed # Ensure that CUDA components are correctly installed and configured # Install any other required packages # Set the environment variable to specify the GPU device ENV CUDA_DEVICE_ORDER=PCI_BUS_ID ENV CUDA_VISIBLE_DEVICES=0 # Run your app.py script CMD ["python", "app.py"]