Dockerfile

# 1) Base image with CUDA + cuDNN *devel* so nvcc is available to compile gsplat CUDA extensions
FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04

# 2) Basic system tools and Python
RUN apt-get update && apt-get install -y python3 python3-pip git wget libgl1 ninja-build && rm -rf /var/lib/apt/lists/*

RUN python3 -m pip install --upgrade pip

# 3) PyTorch with CUDA 12.1 (adjust if needed)
RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# 4) Nerfstudio, auto_LiRPA, and other Python deps used by this repo
RUN pip install "nerfstudio[full]"

RUN pip install numpy scipy pillow matplotlib tqdm pyyaml torchvision graphviz && \
    pip uninstall -y opencv-python opencv-python-headless || true && \
    pip install opencv-python-headless==4.10.0.84

# 5) Copy the Abstract-Rendering repo into the image
WORKDIR /workspace
COPY . /workspace/Abstract-Rendering
WORKDIR /workspace/Abstract-Rendering

# 6) Default command: start a shell; you run scripts manually
CMD ["/bin/bash"]