I ran into almost every issue in installing and I wrote a troubleshooting guide (both CUDA 11 & 12 supported)

[CiSong10]

I've installed MinkowskiEngine more times than I can count this week because I hate my life, running into just about every issue imaginable. I put together some notes for myself, but I figured they might be helpful to others as well. These steps should work for building with both CUDA 11 and CUDA 12.

MinkowskiEngine is a powerful library, and I’m genuinely grateful to the developers for their work. That said, last commit was 2 years ago, which makes me wonder if the developers are still maintaining it. I really hope it gets some much-needed upgrades.

Here is the link:

https://www.youtube.com/watch?v=Q4SWxWIOVBM

Sorry, wrong link:

https://github.com/CiSong10/MinkowskiEngine/blob/cuda12-installation/installation_note.md

[QuteSaltyFish]

I write a docker file with the necessary fix you could merge into your repo if you like @CiSong10

ARG PYTORCH="2.7.0"
ARG CUDA="12.8"
ARG CUDNN="9"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"

ARG CUDA_SM_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6 8.9 9.0 12.0"
# 2) Export it for CMake (>=3.18) and PyTorch builds:
ENV CMAKE_CUDA_ARCHITECTURES=${CUDA_SM_LIST} \
    TORCH_CUDA_ARCH_LIST=${CUDA_SM_LIST}


# Install dependencies
RUN apt-get update
RUN apt-get install -y git ninja-build cmake build-essential libopenblas-dev \
    xterm xauth openssh-server tmux wget mate-desktop-environment-core

WORKDIR /workspace
RUN git clone https://github.com/CiSong10/MinkowskiEngine.git && \
    cd MinkowskiEngine && \
    git checkout cuda12-installation 

WORKDIR ./MinkowskiEngine  
# Apply the fix for error: NVTX3 header collisions (https://github.com/NVIDIA/MinkowskiEngine/issues/614#issuecomment-2886009673)
RUN sed -i '/sources=.*BIND_FILES.*/a\        define_macros=[('"'"'NVTX_DISABLE'"'"', None)],' setup.py 
# Apply the fix for error: Compilation: compiler finds both std::to_address and cuda::std::to_address (https://github.com/NVIDIA/MinkowskiEngine/issues/596)
RUN sed -i 's/\bauto __raw = __to_address(__r.get());/auto __raw = std::__to_address(__r.get());/' /usr/include/c++/11/bits/shared_ptr_base.h
RUN python setup.py install --force_cuda --blas=openblas

[Lans1ot]

Awesome solution.
but sth wrong with my CUDA 12.5 so I just get back to CUDA 12.2.

[CiSong10]

@QuteSaltyFish Thank you. I have pushed this change into my branch.
I also pushed the fix for NVTX3 header collisions in my branch as well, so there's no need for this step now:

# Apply the fix for error: NVTX3 header collisions (https://github.com/NVIDIA/MinkowskiEngine/issues/614#issuecomment-2886009673)
RUN sed -i '/sources=.*BIND_FILES.*/a\        define_macros=[('"'"'NVTX_DISABLE'"'"', None)],' setup.py 

-- shorter Dockerfile ,yay

[WiGig11]

I write a docker file with the necessary fix you could merge into your repo if you like @CiSong10

ARG PYTORCH="2.7.0"
ARG CUDA="12.8"
ARG CUDNN="9"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"

ARG CUDA_SM_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6 8.9 9.0 12.0"
# 2) Export it for CMake (>=3.18) and PyTorch builds:
ENV CMAKE_CUDA_ARCHITECTURES=${CUDA_SM_LIST} \
    TORCH_CUDA_ARCH_LIST=${CUDA_SM_LIST}


# Install dependencies
RUN apt-get update
RUN apt-get install -y git ninja-build cmake build-essential libopenblas-dev \
    xterm xauth openssh-server tmux wget mate-desktop-environment-core

WORKDIR /workspace
RUN git clone https://github.com/CiSong10/MinkowskiEngine.git && \
    cd MinkowskiEngine && \
    git checkout cuda12-installation 

WORKDIR ./MinkowskiEngine  
# Apply the fix for error: NVTX3 header collisions (https://github.com/NVIDIA/MinkowskiEngine/issues/614#issuecomment-2886009673)
RUN sed -i '/sources=.*BIND_FILES.*/a\        define_macros=[('"'"'NVTX_DISABLE'"'"', None)],' setup.py 
# Apply the fix for error: Compilation: compiler finds both std::to_address and cuda::std::to_address (https://github.com/NVIDIA/MinkowskiEngine/issues/596)
RUN sed -i 's/\bauto __raw = __to_address(__r.get());/auto __raw = std::__to_address(__r.get());/' /usr/include/c++/11/bits/shared_ptr_base.h
RUN python setup.py install --force_cuda --blas=openblas

Hello ! thank u for your work , however, due to specific issues, i am wondering if you could provide a illustration of install ME on host machine? I followd you instruction wihout mamba but used conda and pip instead. Since i am using a 5090, i can only use the torch 2.7.0 or higher versions.

[QuteSaltyFish]

I write a docker file with the necessary fix you could merge into your repo if you like @CiSong10

ARG PYTORCH="2.7.0"
ARG CUDA="12.8"
ARG CUDNN="9"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"

ARG CUDA_SM_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6 8.9 9.0 12.0"
# 2) Export it for CMake (>=3.18) and PyTorch builds:
ENV CMAKE_CUDA_ARCHITECTURES=${CUDA_SM_LIST} \
    TORCH_CUDA_ARCH_LIST=${CUDA_SM_LIST}


# Install dependencies
RUN apt-get update
RUN apt-get install -y git ninja-build cmake build-essential libopenblas-dev \
    xterm xauth openssh-server tmux wget mate-desktop-environment-core

WORKDIR /workspace
RUN git clone https://github.com/CiSong10/MinkowskiEngine.git && \
    cd MinkowskiEngine && \
    git checkout cuda12-installation 

WORKDIR ./MinkowskiEngine  
# Apply the fix for error: NVTX3 header collisions (https://github.com/NVIDIA/MinkowskiEngine/issues/614#issuecomment-2886009673)
RUN sed -i '/sources=.*BIND_FILES.*/a\        define_macros=[('"'"'NVTX_DISABLE'"'"', None)],' setup.py 
# Apply the fix for error: Compilation: compiler finds both std::to_address and cuda::std::to_address (https://github.com/NVIDIA/MinkowskiEngine/issues/596)
RUN sed -i 's/\bauto __raw = __to_address(__r.get());/auto __raw = std::__to_address(__r.get());/' /usr/include/c++/11/bits/shared_ptr_base.h
RUN python setup.py install --force_cuda --blas=openblas

Hello ! thank u for your work , however, due to specific issues, i am wondering if you could provide a illustration of install ME on host machine? I followd you instruction wihout mamba but used conda and pip instead. Since i am using a 5090, i can only use the torch 2.7.0 or higher versions.

You can do the same setup using the RUN command. If you're using conda, you can first conda install nvidia/label/cuda-12.8.1::cuda and then install the pytorch 2.7. Then you could run the command I write in the dockerfile to build ME.

[WiGig11]

I write a docker file with the necessary fix you could merge into your repo if you like @CiSong10

ARG PYTORCH="2.7.0"
ARG CUDA="12.8"
ARG CUDNN="9"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"

ARG CUDA_SM_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6 8.9 9.0 12.0"
# 2) Export it for CMake (>=3.18) and PyTorch builds:
ENV CMAKE_CUDA_ARCHITECTURES=${CUDA_SM_LIST} \
    TORCH_CUDA_ARCH_LIST=${CUDA_SM_LIST}


# Install dependencies
RUN apt-get update
RUN apt-get install -y git ninja-build cmake build-essential libopenblas-dev \
    xterm xauth openssh-server tmux wget mate-desktop-environment-core

WORKDIR /workspace
RUN git clone https://github.com/CiSong10/MinkowskiEngine.git && \
    cd MinkowskiEngine && \
    git checkout cuda12-installation 

WORKDIR ./MinkowskiEngine  
# Apply the fix for error: NVTX3 header collisions (https://github.com/NVIDIA/MinkowskiEngine/issues/614#issuecomment-2886009673)
RUN sed -i '/sources=.*BIND_FILES.*/a\        define_macros=[('"'"'NVTX_DISABLE'"'"', None)],' setup.py 
# Apply the fix for error: Compilation: compiler finds both std::to_address and cuda::std::to_address (https://github.com/NVIDIA/MinkowskiEngine/issues/596)
RUN sed -i 's/\bauto __raw = __to_address(__r.get());/auto __raw = std::__to_address(__r.get());/' /usr/include/c++/11/bits/shared_ptr_base.h
RUN python setup.py install --force_cuda --blas=openblas

Hello ! thank u for your work , however, due to specific issues, i am wondering if you could provide a illustration of install ME on host machine? I followd you instruction wihout mamba but used conda and pip instead. Since i am using a 5090, i can only use the torch 2.7.0 or higher versions.

You can do the same setup using the RUN command. If you're using conda, you can first conda install nvidia/label/cuda-12.8.1::cuda and then install the pytorch 2.7. Then you could run the command I write in the dockerfile to build ME.

Hello! so sorry to bother you once more. However, as requested, i write a script based on the suggestions of GPT-5 and you. Here is my script and the response i get:

 ```bash #!/usr/bin/env bash ######################################## # Customizable parameters ######################################## ENV_NAME="mink" PYTHON_VERSION="3.10" PYTORCH_VERSION="2.7.0" CUDA_CONDA_VERSION="12.4.0" # CUDA toolkit available in conda (does not rely on system CUDA) WORKDIR="${HOME}/workspace" # CUDA architecture list matching Dockerfile CUDA_SM_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6 8.9 9.0 12.0" ######################################## # 0) Prepare conda ######################################## if ! command -v conda >/dev/null 2>&1; then echo "[ERROR] Conda not detected, please install Miniconda/Anaconda first." exit 1 fi eval "$(conda shell.bash hook)" conda deactivate || true if ! conda env list | awk '{print $1}' | grep -qx "${ENV_NAME}"; then conda create -y -n "${ENV_NAME}" "python=${PYTHON_VERSION}" fi conda activate "${ENV_NAME}" ######################################## # 1) Install build tools and dependencies only in this environment (conda-forge) # No system package installations (no apt) ######################################## conda install -y -c conda-forge \ cmake ninja pkg-config \ openblas openblas-devel \ gcc_linux-64=12.* gxx_linux-64=12.* \ wget unzip ######################################## # 2) Install CUDA toolkit (including nvcc) only in this environment # Completely rely on the CUDA toolkit from conda ######################################## conda install -y -c "nvidia/label/cuda-${CUDA_CONDA_VERSION}" cuda-toolkit ######################################## # 3) Create activation script for this environment (only affects this environment) # Does not modify ~/.bashrc or affect global environment ######################################## mkdir -p "$CONDA_PREFIX/etc/conda/activate.d" cat > "$CONDA_PREFIX/etc/conda/activate.d/99_mink_env.sh" <<'EOF' # Only affects this environment when activated export CUDA_HOME="$CONDA_PREFIX" # Use CUDA from this environment export PATH="$CUDA_HOME/bin:$PATH" export LD_LIBRARY_PATH="$CUDA_HOME/lib64:${LD_LIBRARY_PATH:-}" # Use gcc/g++-12 from conda-forge (for this environment) export CC="$CONDA_PREFIX/bin/x86_64-conda-linux-gnu-gcc" export CXX="$CONDA_PREFIX/bin/x86_64-conda-linux-gnu-g++" # CUDA compilation flags matching Dockerfile export TORCH_NVCC_FLAGS="-Xfatbin -compress-all" export CMAKE_CUDA_ARCHITECTURES="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6 8.9 9.0 12.0" export TORCH_CUDA_ARCH_LIST="$CMAKE_CUDA_ARCHITECTURES" EOF # Re-activate the environment to load environment variables (still only affects this environment) conda deactivate && conda activate "${ENV_NAME}" ######################################## # 4) Install PyTorch (preferably matching CUDA 12.4) ######################################## # Try to install the specified version; if unavailable, fall back to a version available in the channels conda install -y -c pytorch -c nvidia \ "pytorch=${PYTORCH_VERSION}" torchvision torchaudio "pytorch-cuda=${CUDA_CONDA_VERSION%.*}" || \ conda install -y -c pytorch -c nvidia \ pytorch torchvision torchaudio "pytorch-cuda=${CUDA_CONDA_VERSION%.*}" python - <<'PY' import torch print("torch =", torch.__version__, "| torch.cuda =", torch.version.cuda, "| CUDA available:", torch.cuda.is_available()) PY ######################################## # 5) Download MinkowskiEngine source code (no git) ######################################## mkdir -p "${WORKDIR}" && cd "${WORKDIR}" rm -rf MinkowskiEngine-cuda12-installation cuda12-installation.zip 2>/dev/null || true wget -q https://github.com/CiSong10/MinkowskiEngine/archive/refs/heads/cuda12-installation.zip unzip -q cuda12-installation.zip cd MinkowskiEngine-cuda12-installation ######################################## # 6) Apply patches (only affects this environment/project, no system changes) ######################################## # 6.1 NVTX3 conflict: Insert define_macros in setup.py (same as Dockerfile) sed -i "/sources=.*BIND_FILES.*/a\ define_macros=[('NVTX_DISABLE', None)]," setup.py # 6.2 to_address conflict: Only patch the header files in the conda sysroot (no system modification) HEADER_CONDA=$(python - <<'PY' import glob, os p = os.environ.get("CONDA_PREFIX","") c = glob.glob(p + "/x86_64-conda-linux-gnu/sysroot/usr/include/c++/*/bits/shared_ptr_base.h") print(c[0] if c else "") PY ) if [ -n "${HEADER_CONDA}" ] && grep -q "__to_address(__r.get());" "${HEADER_CONDA}"; then sed -i "s/\bauto __raw = __to_address(__r.get());/auto __raw = std::__to_address(__r.get());/" "${HEADER_CONDA}" else echo "[WARN] No need to patch shared_ptr_base.h in conda sysroot, or the line has changed, skipping." fi ######################################## # 7) Build and install MinkowskiEngine (only install in this environment) ######################################## python setup.py clean python setup.py install --force_cuda --blas=openblas --blas_include_dirs="${CONDA_PREFIX}/include" ######################################## # 8) Final verification (still only in this environment) ######################################## python - <<'PY' import MinkowskiEngine as ME, torch print("ME version:", getattr(ME, "__version__", "unknown")) print("CUDA available:", torch.cuda.is_available(), "| torch.cuda =", torch.version.cuda) PY echo echo "[DONE] Installation complete. Only affects Conda environment '${ENV_NAME}', no system files modified." echo "Use: conda activate ${ENV_NAME}" ``` 

response:
Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/data/test/MinkowskiEngine-cuda12-installation/MinkowskiEngine/__init__.py", line 53, in <module> from MinkowskiEngineBackend._C import ( ModuleNotFoundError: No module named 'MinkowskiEngineBackend'