Command-line interface

sGDML provides a fully featured command-line interface (CLI) for all tasks related to force field reconstruction. You can get help for any command with the -h flag:

$ sgdml <command> -h

List of commands

Task	Command
Reconstruct a force field from beginning to end	sgdml all <dataset_file> <n_train> <n_valid> [<n_test>]
Create training tasks	sgdml create <dataset_file> <n_train> <n_valid>
Train models from training tasks	sgdml train <task_dir_or_file> <valid_dataset_file>
Validate models	sgdml validate <model_dir_or_file> <valid_dataset_file>
Select best performing model	sgdml select <model_dir>
Test a model	sgdml test <model_dir_or_file> <test_dataset_file> [<n_test>]
Show details for dataset, task or model file	sgdml show <file>
Purge all caches	sgdml reset

Tip

Dataset files can also be referenced by their fingerprint instead of their file name, e.g. ./d_ethanol.npz is equivalent to ./f03a68c944d70bd7083c951e7f77aaac within the CLI. Since fingerprints are guaranteed to be unique, this practice is less prone to user input error when dealing with multiple similar datasets.

List of optional arguments

Some commands have optional arguments to configure model/training parameters, file handling or to manage compute resources. Please use the -h flag to see which commands support what arguments.

Generic arguments

Option	Argument
Print a description of all command line options	-h, --help
Print the sGDML version number and exit	--version

Model/training configuration

Option	Argument
Path to a separate validation dataset file	-v <valid_dataset_file>, --validation_dataset <valid_dataset_file>
Path to separate test dataset file	-t <test_dataset_file>, --test_dataset <test_dataset_file>
Integer list and/or range <start>:[<step>:]<stop> to search for the option kernel width sigma	-s <s1> [<s2> ...], --sig <s1> [<s2> ...]
Ignore symmetries in the model (as in the orginal GDML variant)	--gdml
Take permutations from existing file (e.g. other dataset or model files; key: perms)	--perms_from <file>
Reconstruct force field w/o its corresponding potential energy surface	--no_E
Include the energy constraints in the kernel (CPU-implementation only)	--E_cstr
Give up on unfinished tasks (e.g. due to timeouts, crashes, etc.), if multiple are trained	--lazy

File management

Option	Argument
Specify a custom training directory	--task_dir <task_dir>
Specify a custom model output file	--model_file <model_file>
Overwrite outputs/allow changing existing files	-o, --overwrite

Compute resource management

Option	Argument
Limit memory usage (whenever possible) [GB]	-m <max_memory>, --max_memory <max_memory>
Limit number of processes	-p <max_processes>, --max_processes <max_processes>
Use CPU implementation (no PyTorch dependency)	--cpu