Dockerfile

# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
# you will also find guides on how best to write your Dockerfile

FROM nvidia/cuda:12.1.0-devel-ubuntu22.04

# Set compute capability for nerfacc and tiny-cuda-nn
# See https://developer.nvidia.com/cuda-gpus and limit number to speed-up build
ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6 8.9 9.0+PTX"
ENV TCNN_CUDA_ARCHITECTURES=90;89;86;80;75;70;61;60
# Speed-up build for RTX 30xx
# ENV TORCH_CUDA_ARCH_LIST="8.6"
# ENV TCNN_CUDA_ARCHITECTURES=86
# Speed-up build for RTX 40xx
# ENV TORCH_CUDA_ARCH_LIST="8.9"
# ENV TCNN_CUDA_ARCHITECTURES=89

# apt install by root user
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    cmake \
    git \
    git-lfs \
    ffmpeg \
    libegl1-mesa-dev \
    libgl1-mesa-dev \
    libgles2-mesa-dev \
    libglib2.0-0 \
    libgl1-mesa-glx \
    libsm6 \
    libxext6 \
    libxrender1 \
    python-is-python3 \
    python3.10-dev \
    python3-pip \
    rsync \
    wget \
    && rm -rf /var/lib/apt/lists/*

RUN useradd -m -u 1000 user
USER user

ENV CUDA_HOME=/usr/local/cuda
ENV PATH=${CUDA_HOME}/bin:/home/user/.local/bin:${PATH}
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
ENV LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH}

WORKDIR /app

RUN pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121
RUN pip install --no-cache-dir datasets "huggingface-hub>=0.19" "hf-transfer>=0.1.4" "protobuf<4" "click<8.1" "pydantic~=1.0"
RUN pip install --no-cache-dir gradio[oauth]==4.44.1 "uvicorn>=0.14.0" spaces

COPY --chown=user ./requirements.txt requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt

COPY --chown=user . /app
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]