Installation Issues

See the README for information about the various ways to install HBDesigner.

These installation methods assume a system that is running CUDA 12.8 or greater, but have options for CUDA 12.4. If you are running on a system that has a version of CUDA below 12.4 or a CUDA version too recent to be compatible with CUDA 12.8, continue reading for advice for how to adapt the environment files for your system.

Adjusting for the CUDA version available on your system

If your system has a more recent version of CUDA (e.g. 13.0) then these installation files will likely still work correctly. See the Common Issues section below if you are having trouble.

If your system has an older version of CUDA, then there are several dependencies you may need to change:

  • The PyTorch source: For example, if you have CUDA 12.4,

    --extra-index-url https://download.pytorch.org/whl/cu128
--find-links https://data.pyg.org/whl/torch-2.8.0+cu128.html

    will become

    --extra-index-url https://download.pytorch.org/whl/cu124
--find-links https://data.pyg.org/whl/torch-2.6.0+cu124.html

    You can find the correct link to use for your torch/CUDA combination here.

  • The torch version will need to be changed:

    torch==2.8.0+cu128

    would be come

    torch==2.6.0+cu124

    (As depicted in the example, you may have to change the torch version number depending on what is available for your CUDA version.)

  • You should then be able to remove the version numbers from the following dependencies:

    • nvidia-cublas-cu12

    • nvidia-cuda-cupti-cu12

    • nvidia-cuda-nvrtc-cu12

    • nvidia-cuda-runtime-cu12

    • nvidia-cudnn-cu12

    • nvidia-cufft-cu12

    • nvidia-cufile-cu12

    • nvidia-curand-cu12

    • nvidia-cusolver-cu12

    • nvidia-cusparse-cu12

    • nvidia-cusparselt-cu12

    • nvidia-nccl-cu12

    • nvidia-nvjitlink-cu12

    • nvidia-nvtx-cu12

    • sympy

    • torch-cluster

    • torch-scatter

  • You may need to find a version of triton that works with your PyTorch/CUDA combination, or you might be able to remove the version requirement from the triton listing. For CUDA 12.4, triton==3.2.0 works.

Common Issues

ImportError: /lib64/libm.so.6: version GLIBC_2.27’ not found`

If you are seeing this, it is most likely because your CUDA version and CUDA version the HBDesigner dependencies are expecting do not match. See the previous section to modify your file for the CUDA version you have access to.

Segmentation Fault (core dumped)

This can be caused by a variety of different things, including a CUDA version mismatch. If you haven’t already modified your environment file to customize it for your particular system, see the previous section. If you have updated these files, try adding the dev requirements to your installation.