deepmd.tf.entrypoints
Submodule that contains all the DeePMD-Kit entry point scripts. Make model deviation calculation. Compress a model, which including tabulating the embedding-net. Print out trining input arguments to console. Freeze the graph in supplied folder. Host DP-GUI server. Calculate neighbor statistics. Test model predictions. Run DeePMD model training. Transfer operation from old fron graph to new prepared raw graph. Make model deviation calculation. A list of paths of models to use for making model deviation The path of system to make model deviation calculation The output file for model deviation results The number of steps that elapse between writing coordinates in a trajectory by a MD engine (such as Gromacs / Lammps). This paramter is used to determine the index in the output file. If True, calculate the RMS real error instead of model deviation. If True, calculate the force model deviation of each atom. If given, calculate the relative model deviation of force. The value is the level parameter for computing the relative model deviation of the force. If given, calculate the relative model deviation of virial. The value is the level parameter for computing the relative model deviation of the virial. Arbitrary keyword arguments. Compress model. The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the step parameter as the domain’s uniform step size, while the second table takes 10 * step as it’s uniform step size. The range of the first table is automatically detected by the code, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper. frozen model file to compress compressed model filename scale of model extrapolation uniform step size of the tabulation’s first table frequency of tabulation overflow check trining checkpoint folder for freezing training script of the input frozen model mpi logging mode for training if speccified log will be written to this file logging level additional arguments Print out trining input arguments to console. Freeze the graph in supplied folder. Host DP-GUI server. The dpgui package is not installed Calculate neighbor statistics. Examples Test model predictions. path where model is stored system directory the path to the list of systems to test munber of tests to do. 0 means all data. seed for random generator whether to shuffle tests file where test details will be output whether per atom quantities should be computed (Supported backend: PyTorch) Task head to test if in multi-task mode. additional arguments if no valid system was found Run DeePMD model training. json/yaml control file path prefix of checkpoint files or None path prefix of checkpoint files or None path for dump file with arguments path to frozen model or None mpi logging mode logging level defined by int 0-3 logging file path or None if logs are to be output only to stdout indicates whether in the model compress mode skip checking neighbor statistics path to pretrained model or None additional arguments if distributed training job name is wrongSubmodules
deepmd.tf.entrypoints.compress
deepmd.tf.entrypoints.convert
deepmd.tf.entrypoints.doc
deepmd.tf.entrypoints.freeze
deepmd.tf.entrypoints.gui
deepmd.tf.entrypoints.ipi
deepmd.tf.entrypoints.main
deepmd.tf.entrypoints.neighbor_stat
deepmd.tf.entrypoints.test
deepmd.tf.entrypoints.train
deepmd.tf.entrypoints.transfer
Package Contents
Functions
make_model_devi
(*, models, system, output, frequency)doc_train_input
(*[, out_type])freeze
(*, checkpoint_folder, output[, node_names, ...])start_dpgui
(*, port, bind_all, **kwargs)neighbor_stat
(*, system, rcut, type_map[, mixed_type, ...])test
(*, model, system, datafile, numb_test, rand_seed, ...)train_dp
(*, INPUT, init_model, restart, output, ...[, ...])transfer
(*, old_model, raw_model, output, **kwargs)list
str
str
int
False
False
float
, default: None
float
, default: None
str
str
int
float
str
str
str
str
Optional
[str
]int
ModuleNotFoundError
>>> neighbor_stat(
... system=".",
... rcut=6.0,
... type_map=[
... "C",
... "H",
... "O",
... "N",
... "P",
... "S",
... "Mg",
... "Na",
... "HW",
... "OW",
... "mNa",
... "mCl",
... "mC",
... "mH",
... "mMg",
... "mN",
... "mO",
... "mP",
... ],
... )
min_nbor_dist: 0.6599510670195264
max_nbor_size: [23, 26, 19, 16, 2, 2, 1, 1, 72, 37, 5, 0, 31, 29, 1, 21, 20, 5]
str
str
str
int
Optional
[int
]Optional
[str
]Optional
[str
], optional
RuntimeError
str
Optional
[str
]Optional
[str
]str
str
str
int
Optional
[str
]Optional
[str
]RuntimeError