deepmd.entrypoints package

Submodule that contains all the DeePMD-Kit entry point scripts.

deepmd.entrypoints.compress(*, input: str, output: str, extrapolate: int, step: float, frequency: str, checkpoint_folder: str, training_script: str, mpi_log: str, log_path: Optional[str], log_level: int, **kwargs)[source]

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.

Parameters
inputstr

frozen model file to compress

outputstr

compressed model filename

extrapolateint

scale of model extrapolation

stepfloat

uniform step size of the tabulation’s first table

frequencystr

frequency of tabulation overflow check

checkpoint_folderstr

trining checkpoint folder for freezing

training_scriptstr

training script of the input frozen model

mpi_logstr

mpi logging mode for training

log_pathOptional[str]

if speccified log will be written to this file

log_levelint

logging level

deepmd.entrypoints.config(*, output: str, **kwargs)[source]

Auto config file generator.

Parameters
output: str

file to write config file

Raises
RuntimeError

if user does not input any systems

ValueError

if output file is of wrong type

deepmd.entrypoints.convert(*, FROM: str, input_model: str, output_model: str, **kwargs)[source]
deepmd.entrypoints.doc_train_input(*, out_type: str = 'rst', **kwargs)[source]

Print out trining input arguments to console.

deepmd.entrypoints.freeze(*, checkpoint_folder: str, output: str, node_names: Optional[str] = None, **kwargs)[source]

Freeze the graph in supplied folder.

Parameters
checkpoint_folderstr

location of the folder with model

outputstr

output file name

node_namesOptional[str], optional

names of nodes to output, by default None

deepmd.entrypoints.make_model_devi(*, models: list, system: str, set_prefix: str, output: str, frequency: int, **kwargs)[source]

Make model deviation calculation

Parameters
models: list

A list of paths of models to use for making model deviation

system: str

The path of system to make model deviation calculation

set_prefix: str

The set prefix of the system

output: str

The output file for model deviation results

frequency: int

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.

deepmd.entrypoints.test(*, model: str, system: str, set_prefix: str, numb_test: int, rand_seed: Optional[int], shuffle_test: bool, detail_file: str, atomic: bool, **kwargs)[source]

Test model predictions.

Parameters
modelstr

path where model is stored

systemstr

system directory

set_prefixstr

string prefix of set

numb_testint

munber of tests to do

rand_seedOptional[int]

seed for random generator

shuffle_testbool

whether to shuffle tests

detail_fileOptional[str]

file where test details will be output

atomicbool

whether per atom quantities should be computed

Raises
RuntimeError

if no valid system was found

deepmd.entrypoints.train_dp(*, INPUT: str, init_model: Optional[str], restart: Optional[str], output: str, init_frz_model: str, mpi_log: str, log_level: int, log_path: Optional[str], is_compress: bool = False, skip_neighbor_stat: bool = False, **kwargs)

Run DeePMD model training.

Parameters
INPUTstr

json/yaml control file

init_modelOptional[str]

path to checkpoint folder or None

restartOptional[str]

path to checkpoint folder or None

outputstr

path for dump file with arguments

init_frz_modelstr

path to frozen model or None

mpi_logstr

mpi logging mode

log_levelint

logging level defined by int 0-3

log_pathOptional[str]

logging file path or None if logs are to be output only to stdout

is_compress: bool

indicates whether in the model compress mode

skip_neighbor_statbool, default=False

skip checking neighbor statistics

Raises
RuntimeError

if distributed training job nem is wrong

deepmd.entrypoints.transfer(*, old_model: str, raw_model: str, output: str, **kwargs)[source]

Transfer operation from old fron graph to new prepared raw graph.

Parameters
old_modelstr

frozen old graph model

raw_modelstr

new model that will accept ops from old model

outputstr

new model with transfered parameters will be saved to this location

Submodules

deepmd.entrypoints.compress module

Compress a model, which including tabulating the embedding-net.

deepmd.entrypoints.compress.compress(*, input: str, output: str, extrapolate: int, step: float, frequency: str, checkpoint_folder: str, training_script: str, mpi_log: str, log_path: Optional[str], log_level: int, **kwargs)[source]

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.

Parameters
inputstr

frozen model file to compress

outputstr

compressed model filename

extrapolateint

scale of model extrapolation

stepfloat

uniform step size of the tabulation’s first table

frequencystr

frequency of tabulation overflow check

checkpoint_folderstr

trining checkpoint folder for freezing

training_scriptstr

training script of the input frozen model

mpi_logstr

mpi logging mode for training

log_pathOptional[str]

if speccified log will be written to this file

log_levelint

logging level

deepmd.entrypoints.config module

Quickly create a configuration file for smooth model.

deepmd.entrypoints.config.config(*, output: str, **kwargs)[source]

Auto config file generator.

Parameters
output: str

file to write config file

Raises
RuntimeError

if user does not input any systems

ValueError

if output file is of wrong type

deepmd.entrypoints.convert module

deepmd.entrypoints.convert.convert(*, FROM: str, input_model: str, output_model: str, **kwargs)[source]

deepmd.entrypoints.doc module

Module that prints train input arguments docstrings.

deepmd.entrypoints.doc.doc_train_input(*, out_type: str = 'rst', **kwargs)[source]

Print out trining input arguments to console.

deepmd.entrypoints.freeze module

Script for freezing TF trained graph so it can be used with LAMMPS and i-PI.

References

https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc

deepmd.entrypoints.freeze.freeze(*, checkpoint_folder: str, output: str, node_names: Optional[str] = None, **kwargs)[source]

Freeze the graph in supplied folder.

Parameters
checkpoint_folderstr

location of the folder with model

outputstr

output file name

node_namesOptional[str], optional

names of nodes to output, by default None

deepmd.entrypoints.main module

DeePMD-Kit entry point module.

deepmd.entrypoints.main.get_ll(log_level: str) int[source]

Convert string to python logging level.

Parameters
log_levelstr

allowed input values are: DEBUG, INFO, WARNING, ERROR, 3, 2, 1, 0

Returns
int

one of python logging module log levels - 10, 20, 30 or 40

deepmd.entrypoints.main.main()[source]

DeePMD-Kit entry point.

Raises
RuntimeError

if no command was input

deepmd.entrypoints.main.parse_args(args: Optional[List[str]] = None)[source]

DeePMD-Kit commandline options argument parser.

Parameters
args: List[str]

list of command line arguments, main purpose is testing default option None takes arguments from sys.argv

deepmd.entrypoints.test module

Test trained DeePMD model.

deepmd.entrypoints.test.test(*, model: str, system: str, set_prefix: str, numb_test: int, rand_seed: Optional[int], shuffle_test: bool, detail_file: str, atomic: bool, **kwargs)[source]

Test model predictions.

Parameters
modelstr

path where model is stored

systemstr

system directory

set_prefixstr

string prefix of set

numb_testint

munber of tests to do

rand_seedOptional[int]

seed for random generator

shuffle_testbool

whether to shuffle tests

detail_fileOptional[str]

file where test details will be output

atomicbool

whether per atom quantities should be computed

Raises
RuntimeError

if no valid system was found

deepmd.entrypoints.train module

DeePMD training entrypoint script.

Can handle local or distributed training.

deepmd.entrypoints.train.train(*, INPUT: str, init_model: Optional[str], restart: Optional[str], output: str, init_frz_model: str, mpi_log: str, log_level: int, log_path: Optional[str], is_compress: bool = False, skip_neighbor_stat: bool = False, **kwargs)[source]

Run DeePMD model training.

Parameters
INPUTstr

json/yaml control file

init_modelOptional[str]

path to checkpoint folder or None

restartOptional[str]

path to checkpoint folder or None

outputstr

path for dump file with arguments

init_frz_modelstr

path to frozen model or None

mpi_logstr

mpi logging mode

log_levelint

logging level defined by int 0-3

log_pathOptional[str]

logging file path or None if logs are to be output only to stdout

is_compress: bool

indicates whether in the model compress mode

skip_neighbor_statbool, default=False

skip checking neighbor statistics

Raises
RuntimeError

if distributed training job nem is wrong

deepmd.entrypoints.transfer module

Module used for transfering parameters between models.

deepmd.entrypoints.transfer.transfer(*, old_model: str, raw_model: str, output: str, **kwargs)[source]

Transfer operation from old fron graph to new prepared raw graph.

Parameters
old_modelstr

frozen old graph model

raw_modelstr

new model that will accept ops from old model

outputstr

new model with transfered parameters will be saved to this location