Dockerfile

# Use an official PyTorch image with CUDA support as the base image
FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel

# Add NVIDIA CUDA GPG key
# Add NVIDIA CUDA GPG key using a different key server
RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key A4B469963BF863CC

# 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

RUN apt-get update && apt-get install -y ninja-build

# Install necessary dependencies, including CMake, a C++ compiler, and others
RUN apt-get update && apt-get install -y unzip ffmpeg cmake g++ build-essential aria2

# 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

# Clone your repository or add your code to the container
RUN git clone -b main https://github.com/fffiloni/video-retalking $HOME/app

# Install specific versions of PyTorch and TorchVision
RUN pip install torch==2.0.0+cu117 torchvision==0.15.0+cu117 -f https://download.pytorch.org/whl/torch_stable.html

# Install dependencies
#COPY requirements.txt $HOME/app/requirements.txt
RUN pip install --no-cache-dir -r requirements.txt gradio imageio[ffmpeg]

# Download checkpoint files using aria2
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/30_net_gen.pth -d $HOME/app/checkpoints -o 30_net_gen.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/BFM.zip -d $HOME/app/checkpoints -o BFM.zip
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/DNet.pt -d $HOME/app/checkpoints -o DNet.pt
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/ENet.pth -d $HOME/app/checkpoints -o ENet.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/GFPGANv1.3.pth -d $HOME/app/checkpoints -o GFPGANv1.3.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/GPEN-BFR-512.pth -d $HOME/app/checkpoints -o GPEN-BFR-512.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/LNet.pth -d $HOME/app/checkpoints -o LNet.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/ParseNet-latest.pth -d $HOME/app/checkpoints -o ParseNet-latest.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/RetinaFace-R50.pth -d $HOME/app/checkpoints -o RetinaFace-R50.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/expression.mat -d $HOME/app/checkpoints -o expression.mat
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/face3d_pretrain_epoch_20.pth -d $HOME/app/checkpoints -o face3d_pretrain_epoch_20.pth
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/shape_predictor_68_face_landmarks.dat -d $HOME/app/checkpoints -o shape_predictor_68_face_landmarks.dat
RUN unzip -d $HOME/app/checkpoints/BFM $HOME/app/checkpoints/BFM.zip


# Ensure the compiled CUDA code can be found
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}

# 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"]