Command line interface#

SevenNet provides five commands for preprocessing, training, and deployment: sevenn preset, sevenn graph_build, sevenn, sevenn inference, and sevenn get_model.

`sevenn preset`#

With the sevenn preset command, the input file setting the training parameters is generated automatically.

sevenn preset {preset keyword} > input.yaml

Available preset keywords are: base, fine_tune, multi_modal, sevennet-0, and sevennet-l3i5. Check comments in the preset YAML files for explanations. For fine-tuning, be aware that most model hyperparameters cannot be modified unless explicitly indicated. To reuse a preprocessed training set, you can specify sevenn_data/${dataset_name}.pt for the load_trainset_path: in the input.yaml.

`sevenn graph_build`#

To obtain the preprocessed data, sevenn_data/graph.pt, sevenn graph_build command can be used. The output files can be used for training (sevenn) or inference (sevenn inference) to skip the graph build stage.

sevenn graph_build {dataset path} {cutoff radius}

The output sevenn_data/graph.yaml contains statistics and meta information about the dataset. These files must be located in the sevenn_data directory. If you move the dataset, move the entire sevenn_data directory without changing the contents.

See sevenn graph_build --help for more information.

`sevenn train`#

Given that input.yaml and sevenn_data/graph.pt are prepared, SevenNet can be trained by the following command:

sevenn train input.yaml -s

We support multi-GPU training using PyTorch DDP (distributed data parallel) with a single process (or a CPU core) per GPU.

torchrun --standalone --nnodes {number of nodes} --nproc_per_node {number of GPUs} --no_python sevenn input.yaml -d

Enable cuEquivariance, flashTP, or OpenEquivariance while training by adding the –enable_cueq, –enable_flash, or –enable_oeq.

sevenn train input.yaml -s --enable_cueq # or --enable_flash or --enable_oeq

Please note that batch_size in input.yaml refers to the per-GPU batch size.

`sevenn inference`#

Using the checkpoint after training, the properties such as energy, force, and stress can be inferred directly.

sevenn inference checkpoint_best.pth path_to_my_structures/*

This will create the inference_results directory, where CSV files contain predicted energy, force, stress, and their references (if available). See sevenn inference --help for more information.

`sevenn get_model`#

The command is for LAMMPS integration of SevenNet. It deploys a model into LAMMPS-readable file(s).

See LAMMPS: PyTorch or LAMMPS: ML-IAP for installation.

See Accelerators for installation of accelerators.

Basic usage#

sevenn get_model \
    {pretrained_name or checkpoint_path} \
    {--use_mliap} \  # For LAMMPS ML-IAP use.
    {--enable_flash | --enable_cueq | --enable_oeq} \  # For accelerators.
    {--modal {task_name}} \  # Required when using multi-fidelity model
    {--get_parallel}  # For parallel MD simulations

If --use_mliap is not set (TorchScript version), it will create deployed_serial.pt or a directory containing several deployed_parallel_*.pt files for parallel execution. They can be used as a LAMMPS potential with the e3gnn or e3gnn/parallel pair_style in LAMMPS. In this case, --enable_flash and --enable_oeq are available. Check LAMMPS: PyTorch for installation and lammps script in this use case.

If --use_mliap is set, it will create deployed_serial_mliap.pt. The file can be used with the mliap pair_style in LAMMPS. In this case, --enable_cueq, --enable_flash, and --enable_oeq are available, but you cannot specify multiple at the same time. Parallel execution is not tested in this case. Check LAMMPS: ML-IAP for installation and lammps script in this use case.

Examples#

# deyploy 7net-0 with flashTP enabled for LAMMPS PyTorch (TorchScript) interface
sevenn get_model 7net-0 --enable_flash

# deyploy 7net-0 with flashTP enabled for LAMMPS ML-IAP interface
sevenn get_model 7net-0 --use_mliap --enable_flash

# deyploy 7net-omni with cuEq enabled for LAMMPS ML-IAP interface, mpa modality
sevenn get_model 7net-Omni --use_mliap --enable_cueq --modal mpa

# deyploy 7net-mf-ompa with parallel for LAMMPS PyTorch interface omat24 modality
sevenn get_model 7net-mf-ompa --get_parallel --modal omat24

`sevenn cp`#

This is an utility command. You can check model’s complexity, metadata, and its modalities (or tasks) at glance.

sevenn cp 7net-mf-ompa

Sevennet version                                0.11.0
When                                  2025-03-10 23:32
Hash                  ce5b3f90adf546499e528174fbc72fce
Cutoff                                             6.0
Channel                                            128
Lmax                                                 3
Group (parity)                                      O3
Interaction layers                                   5
Self connection type                            nequip
Last epoch                                        None
Elements                                           119
cuEquivariance used                              False
FlashTP used                                     False
Modality                                   omat24, mpa

Command line interface#

sevenn preset#

sevenn graph_build#

sevenn train#

sevenn inference#

sevenn get_model#