Dockerfile

# Use the official PyTorch image as the base
FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive \
    MKL_THREADING_LAYER=GNU \
    HOME=/home/user \
    PYTHONPATH=/home/user/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 \
    CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda \
    CUDA_HOME=/usr/local/cuda \
    LD_LIBRARY_PATH=/usr/local/cuda/lib64 \
    TORCH_CUDA_ARCH_LIST="8.6" \
    CMAKE_PREFIX_PATH="/usr/local/cuda:/usr/lib/x86_64-linux-gnu/"

# Override PATH so that system Python 3.9 takes precedence over Conda’s Python 3.10
ENV PATH="/usr/local/cuda/bin:/usr/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/sbin"

# Install Python 3.9 and build dependencies (including those needed for NumPy)
RUN apt-get update && \
    apt-get install -y --no-install-recommends \
      python3.9 python3.9-dev python3.9-venv \
      git wget libgl1-mesa-glx libglib2.0-0 ffmpeg libx264-dev pkg-config \
      build-essential gfortran libopenblas-dev liblapack-dev \
      cmake g++ software-properties-common gnupg2 && \
    rm -rf /var/lib/apt/lists/*

# Set Python 3.9 as default for python3, install pip (using wget instead of curl), and create symlinks
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1 && \
    update-alternatives --set python3 /usr/bin/python3.9 && \
    wget -qO- https://bootstrap.pypa.io/get-pip.py | python3.9 && \
    ln -sf /usr/bin/python3.9 /usr/bin/python && \
    ln -sf /usr/bin/pip3 /usr/bin/pip

# Upgrade pip, setuptools, and wheel
RUN python3.9 -m pip install --upgrade pip setuptools wheel

# Add NVIDIA package repositories and install CUDA Toolkit and cuDNN
RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb && \
    dpkg -i cuda-keyring_1.0-1_all.deb && \
    apt-get update && \
    apt-get install -y --no-install-recommends \
      cuda-toolkit-11-7 libcudnn8 libcudnn8-dev && \
    rm -rf /var/lib/apt/lists/*

# Create a non-root user and switch to it
RUN useradd -m -u 1000 user
USER user

# Set working directory
WORKDIR /home/user/app

# Clone the repository
RUN git clone -b dev https://github.com/fffiloni/dreamtalk .

# Download model checkpoints
RUN mkdir -p checkpoints && \
    wget https://huggingface.co/camenduru/dreamtalk/resolve/main/damo/dreamtalk/checkpoints/denoising_network.pth -O checkpoints/denoising_network.pth && \
    wget https://huggingface.co/camenduru/dreamtalk/resolve/main/damo/dreamtalk/checkpoints/renderer.pt -O checkpoints/renderer.pt

# Pre-install NumPy (version as specified in requirements_HF.txt) to avoid build issues
RUN python3.9 -m pip install numpy==1.26.4

# Copy and install remaining Python dependencies
COPY requirements_HF.txt .
RUN python3.9 -m pip install --no-cache-dir -r requirements_HF.txt && \
    python3.9 -m pip install --no-cache-dir "moviepy<2" gradio

# Copy the application file
COPY app.py .

# Set additional CUDA environment variables if needed
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID \
    CUDA_VISIBLE_DEVICES=0

# Run the application explicitly with Python 3.9
CMD ["python3.9", "app.py"]