# Python reference environment for parity dumps.
#
# Deliberately stock-PyTorch only: no FlexGEMM / flash-attn / o-voxel / CuMesh
# CUDA extensions. Sparse attention and sparse conv are monkeypatched to dense
# equivalents by scripts/ref_common.py (batch=1 makes them equal), so reference
# activations can be produced on GPU or CPU without custom kernels — that also
# keeps the RTX 5070 Ti (sm_120) out of extension-build trouble.
#
# Build:  docker build -f docker/Dockerfile.ref -t trellis2-ref docker
# Run :   docker run --rm --device nvidia.com/gpu=all \
#             -v "$PWD":/work -v "$PWD/../python/TRELLIS.2":/trellis2 \
#             -e PYTHONPATH=/trellis2 -w /work trellis2-ref python scripts/...
FROM pytorch/pytorch:2.7.1-cuda12.8-cudnn9-devel

RUN pip install --no-cache-dir \
    "transformers==4.57.1" \
    safetensors \
    pillow \
    numpy \
    easydict \
    opencv-python-headless \
    trimesh \
    tqdm \
    imageio \
    gguf