Accelerators#

CuEquivariance and flashTP provide acceleration for both SevenNet training and inference. For Benchmark results, follow here

CuEquivariance#

CuEquivariance is an NVIDIA Python library designed to facilitate the construction of high-performance geometric neural networks using segmented polynomials and triangular operations. For more information, refer to cuEquivariance.

Requirements#

  • Python >= 3.10

  • cuEquivariance >= 0.6.1

Install via:

pip install sevenn[cueq12]  # cueq11 for CUDA version 11.*

Note

Some GeForce GPUs do not support pynvml, causing pynvml.NVMLError_NotSupported: Not Supported. Then try a lower cuEquivariance version, such as 0.6.1.

FlashTP#

FlashTP is a high-performance Tensor-Product library for Machine Learning Interatomic Potentials (MLIPs). For more information and the installation guide, refer to flashTP.

Requirements#

  • Python >= 3.10

  • flashTP >= 0.1.0

Note

During installation of flashTP, subprocess.CalledProcessError: ninja ... exit status 137 typically indicates out-of-memory during compilation. Try reducing the build parallelism:

export MAX_JOBS=1