mace/setup.cfg at develop · ACEsuit/mace · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
[metadata]
name = mace-torch
version = attr: mace.__version__
short_description = MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/ACEsuit/mace
classifiers =
    Programming Language :: Python :: 3
    Programming Language :: Python :: 3.10
    Programming Language :: Python :: 3.11
    Programming Language :: Python :: 3.12
    Programming Language :: Python :: 3.13
    Operating System :: OS Independent
    License :: OSI Approved :: MIT License

[options]
packages = find:
python_requires = >=3.10
install_requires =
    torch>=1.12
    e3nn==0.4.4
    numpy
    opt_einsum
    ase
    torch-ema
    prettytable
    matscipy
    h5py
    torchmetrics
    python-hostlist
    configargparse
    GitPython
    pyYAML
    tqdm
    lmdb
    orjson
    # for plotting:
    matplotlib
    pandas

[options.entry_points]
console_scripts =
    mace_active_learning_md  =  mace.cli.active_learning_md:main
    mace_create_lammps_model  =  mace.cli.create_lammps_model:main
    mace_eval_configs  =  mace.cli.eval_configs:main
    mace_plot_train  =  mace.cli.plot_train:main
    mace_run_train  =  mace.cli.run_train:main
    mace_prepare_data  =  mace.cli.preprocess_data:main
    mace_finetuning_select = mace.cli.fine_tuning_select:main
    mace_convert_device  =  mace.cli.convert_device:main
    mace_select_head =  mace.cli.select_head:main
    mace_e3nn_cueq = mace.cli.convert_e3nn_cueq:main
    mace_cueq_to_e3nn = mace.cli.convert_cueq_e3nn:main
    mace_polar_density_cube = mace.cli.polar_density_cube:main

[options.extras_require]
wandb = wandb
fpsample = fpsample
test =
    pytest
    pytest-benchmark
    pytest-cov
    pytest-split
    pytest-timeout
    pytest-xdist
dev =
    black
    isort
    mypy
    pre-commit
    pytest
    pytest-benchmark
    pytest-cov
    pytest-split
    pytest-timeout
    pytest-xdist
    pylint==4.0.6
schedulefree = schedulefree
magnetic =
    sphericart-torch==1.0.9
    torch-geometric
torchsim =
    torch-sim-atomistic; python_version >= "3.12"
# The cueq ops wheels are capped below 0.11. The 0.11 ops binaries link a newer
# cuBLAS than they declare (metadata says nvidia-cublas>=12.5.0), so pip can
# satisfy them with the cuBLAS that torch pins -- 13.1.1.3 via cuda-toolkit --
# and the extension then fails to import. cuequivariance falls back to
# SegmentedPolynomialNaive on that failure with only a warning, which is why an
# ops release surfaced as an AttributeError in every cueq test at once.
# Upper bounds rather than == so patch releases still flow and pip keeps room to
# resolve. Revisit when the ops metadata is fixed or torch moves its cuBLAS pin.
cueq = cuequivariance-torch>=0.2.0,<0.12
oeq = openequivariance
# No cueq-cuda-11. Its ops stream ended at 0.5.1 in June 2025, it publishes no
# wheel past cp312, and nothing pins the frontend to match: `[cueq,cueq-cuda-11]`
# resolved a 0.11 cuequivariance-torch against 0.5.1 kernels, six minor versions
# apart, and offered that as the supported install. cueq-cuda-12 is the route for
# a CUDA 11 host, since CUDA 12 builds run on those drivers.
cueq-cuda-12 = cuequivariance-ops-torch-cu12>=0.2.0,<0.11
# cu13 ops start at 0.7.0; the cu12 floor above predates that split.
cueq-cuda-13 = cuequivariance-ops-torch-cu13>=0.7.0,<0.11