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# 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 --upgrade pip
# 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"] |