Hello,
I am trying to use the MACE ML-IAP interface with LAMMPS following the documentation:
https://mace-docs.readthedocs.io/en/latest/guide/lammps_mliap.html
My environment is:
- OS: CentOS 7
- glibc: 2.17
- Python: 3.10.18
- PyTorch: 2.3.1+cu121
- CUDA runtime reported by PyTorch: 12.1
- mace-torch: 0.3.16
- cuequivariance: 0.10.0
- cuequivariance-torch: 0.10.0
- e3nn: 0.4.4
- ASE: 3.29.0
- matscipy: 1.1.1
- NumPy: 2.2.6
- SciPy: 1.15.2
- pandas: 2.3.3
The base cuEquivariance packages install successfully:
cuequivariance
cuequivariance-torch
However, following the MACE documentation, I cannot install the CUDA 12 kernel package using:
pip install cuequivariance-ops-torch-cu12
I get Error like:
ERROR: Could not find a version that satisfies the requirement cuequivariance-ops-torch-cu12 (from versions: none)
ERROR: No matching distribution found for cuequivariance-ops-torch-cu12
As a test, I tried the CUDA 11 package:
pip install cuequivariance-ops-torch-cu11
This package installs successfully, but importing it with my existing PyTorch 2.3.1+cu121 environment fails with:
AttributeError: module 'torch.library' has no attribute 'custom_op'
Could you please advise what combination of PyTorch/cuEquivariance/CUDA packages is recommended for a CentOS 7 system with glibc 2.17 and CUDA 12.1?
Thank you
Hello,
I am trying to use the MACE ML-IAP interface with LAMMPS following the documentation:
https://mace-docs.readthedocs.io/en/latest/guide/lammps_mliap.html
My environment is:
The base cuEquivariance packages install successfully:
cuequivariance
cuequivariance-torch
However, following the MACE documentation, I cannot install the CUDA 12 kernel package using:
pip install cuequivariance-ops-torch-cu12
I get Error like:
ERROR: Could not find a version that satisfies the requirement cuequivariance-ops-torch-cu12 (from versions: none)
ERROR: No matching distribution found for cuequivariance-ops-torch-cu12
As a test, I tried the CUDA 11 package:
pip install cuequivariance-ops-torch-cu11
This package installs successfully, but importing it with my existing PyTorch 2.3.1+cu121 environment fails with:
AttributeError: module 'torch.library' has no attribute 'custom_op'
Could you please advise what combination of PyTorch/cuEquivariance/CUDA packages is recommended for a CentOS 7 system with glibc 2.17 and CUDA 12.1?
Thank you