dockerfile

[iernstig]

In case anybody has issue with dependencies. I created a dockerfile for your convenience.

FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu18.04

ENV DEBIAN_FRONTEND noninteractive

# Install basic dependencies
RUN apt-get update && apt-get install -y \
    curl \
    build-essential \
    git \
    software-properties-common \
    ffmpeg \
    libsm6 \
    libxext6 \
    libgl1 \ 
    ninja-build \
    unzip

# This step must be completed before setting a different python (3.8) as system default
RUN add-apt-repository ppa:ubuntu-toolchain-r/test
RUN apt-get update && apt-get install -y gcc-11 g++-11
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 90 \
    --slave /usr/bin/g++ g++ /usr/bin/g++-11
RUN update-alternatives --config gcc


# Add deadsnakes PPA and install Python 3.8
RUN add-apt-repository ppa:deadsnakes/ppa && \
    apt-get update && \
    apt-get install -y python3.8 python3.8-dev python3.8-venv python3.8-distutils 

# Make Python 3.8 the default
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1

# Install UV
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/

# Install PyTorch with CUDA support using Python 3.8
RUN uv pip install --python python3.8 torch==2.1.0 --index-url https://download.pytorch.org/whl/cu118 --system

# Install other dependencies
RUN uv pip install --python python3.8 setuptools wheel --system
RUN uv pip install --python python3.8 \
    opencv-python==4.8.1.78 \
    trimesh==3.23.5 \
    open3d==0.17 \
    pyvista==0.42.3 \
    scipy==1.10.1 \
    scikit-image==0.21.0 \
    pyhocon==0.3.59 \
    pyexr==0.3.10 \
    tensorboard==2.14.0 \
    icecream==2.1.3 \
    PyMCubes==0.1.4 \
    pyembree==0.2.11 \
    --system

# install tcnn, set TCNN_CUDA_ARCHITECTURES to your compute platform. 
# Your compute arch can be obtained by the following command
# unfortunately - I have not found a way to automatically find this during the docker build stage
# nvidia-smi --query-gpu=compute_cap --format=csv | tail -1 | sed "s#\.##g"

RUN PATH="/usr/local/cuda/bin:${PATH}" \
    LIBRARY_PATH="/usr/local/cuda/lib64/stubs:${LIBRARY_PATH}" \
    TCNN_CUDA_ARCHITECTURES="86" \
    CXXFLAGS="-std=c++17" \
    uv pip install --no-build-isolation --python python3.8 git+https://github.com/NVlabs/tiny-cuda-nn/@2ec562e853e6f482b5d09168705205f46358fb39#subdirectory=bindings/torch  --system

# Install nerfacc and other dependencies
# Note: This assumes the directory will be available in the Docker context
RUN git clone https://github.com/CyberAgentAILab/SuperNormal.git /supernormal
WORKDIR /supernormal

RUN uv pip install --upgrade pip setuptools wheel --system
RUN PATH="/usr/local/cuda/bin:${PATH}" \
    LIBRARY_PATH="/usr/local/cuda/lib64/stubs:${LIBRARY_PATH}" \
    uv pip install --python python3.8 \
    # --config-settings build-backend=setuptools.build_meta \
    --no-build-isolation -e ./third_parties/nerfacc-0.3.5/nerfacc-0.3.5/ --system

now, since there is no cuda runtime by default when building this docker container. You need to enable that according to https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime - like so (on ubuntu): Install nvidia-container-runtime:

sudo apt-get install nvidia-container-runtime

Edit/create the /etc/docker/daemon.json with content:

{
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
         } 
    },
    "default-runtime": "nvidia" 
}

Restart docker daemon:

sudo systemctl restart docker

Finally, you can build the container using: DOCKER_BUILDKIT=0 docker build . --tag supernormal

In case you want, you could connect to the container in vscode with dev-containers-extension using the following devcontainer.json

{
  "name": "CUDA",
  "image": "supernormal",

  "runArgs": [
    "--gpus=all"
  ],
  "customizations": {
    "vscode": {
      "extensions": [
        "ms-python.python",
        "ms-vscode.cpptools",
        "ms-python.vscode-pylance"
      ]
    }
  },
  "workspaceMount": "source=${localWorkspaceFolder},target=/workspace,type=bind",
  "workspaceFolder": "/workspace"

}