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
- input
str
frozen model file to compress
- output
str
compressed model filename
- extrapolate
int
scale of model extrapolation
- step
float
uniform step size of the tabulation’s first table
- frequency
str
frequency of tabulation overflow check
- checkpoint_folder
str
trining checkpoint folder for freezing
- training_script
str
training script of the input frozen model
- mpi_log
str
mpi logging mode for training
- log_path
Optional
[str
] if speccified log will be written to this file
- log_level
int
logging level
- input
- 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.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.
- 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.neighbor_stat(*, system: str, rcut: float, type_map: List[str], **kwargs)[source]
Calculate neighbor statistics.
Examples
>>> neighbor_stat(system='.', rcut=6., 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]
- 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
- model
str
path where model is stored
- system
str
system directory
- set_prefix
str
string prefix of set
- numb_test
int
munber of tests to do
- rand_seed
Optional
[int
] seed for random generator
- shuffle_testbool
whether to shuffle tests
- detail_file
Optional
[str
] file where test details will be output
- atomicbool
whether per atom quantities should be computed
- model
- 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
- INPUT
str
json/yaml control file
- init_model
Optional
[str
] path to checkpoint folder or None
- restart
Optional
[str
] path to checkpoint folder or None
- output
str
path for dump file with arguments
- init_frz_model
str
path to frozen model or None
- mpi_log
str
mpi logging mode
- log_level
int
logging level defined by int 0-3
- log_path
Optional
[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
- INPUT
- 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.
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
- input
str
frozen model file to compress
- output
str
compressed model filename
- extrapolate
int
scale of model extrapolation
- step
float
uniform step size of the tabulation’s first table
- frequency
str
frequency of tabulation overflow check
- checkpoint_folder
str
trining checkpoint folder for freezing
- training_script
str
training script of the input frozen model
- mpi_log
str
mpi logging mode for training
- log_path
Optional
[str
] if speccified log will be written to this file
- log_level
int
logging level
- input
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.doc module
Module that prints train input arguments docstrings.
deepmd.entrypoints.freeze module
Script for freezing TF trained graph so it can be used with LAMMPS and i-PI.
References
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.
- deepmd.entrypoints.main.main()[source]
DeePMD-Kit entry point.
- Raises
RuntimeError
if no command was input
- deepmd.entrypoints.main.main_parser() argparse.ArgumentParser [source]
DeePMD-Kit commandline options argument parser.
- Returns
argparse.ArgumentParser
main parser of DeePMD-kit
- deepmd.entrypoints.main.parse_args(args: Optional[List[str]] = None) argparse.Namespace [source]
Parse arguments and convert argument strings to objects.
- Parameters
- args: List[str]
list of command line arguments, main purpose is testing default option None takes arguments from sys.argv
- Returns
argparse.Namespace
the populated namespace
deepmd.entrypoints.neighbor_stat module
- deepmd.entrypoints.neighbor_stat.neighbor_stat(*, system: str, rcut: float, type_map: List[str], **kwargs)[source]
Calculate neighbor statistics.
Examples
>>> neighbor_stat(system='.', rcut=6., 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]
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
- model
str
path where model is stored
- system
str
system directory
- set_prefix
str
string prefix of set
- numb_test
int
munber of tests to do
- rand_seed
Optional
[int
] seed for random generator
- shuffle_testbool
whether to shuffle tests
- detail_file
Optional
[str
] file where test details will be output
- atomicbool
whether per atom quantities should be computed
- model
- 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
- INPUT
str
json/yaml control file
- init_model
Optional
[str
] path to checkpoint folder or None
- restart
Optional
[str
] path to checkpoint folder or None
- output
str
path for dump file with arguments
- init_frz_model
str
path to frozen model or None
- mpi_log
str
mpi logging mode
- log_level
int
logging level defined by int 0-3
- log_path
Optional
[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
- INPUT
- Raises
RuntimeError
if distributed training job nem is wrong
deepmd.entrypoints.transfer module
Module used for transfering parameters between models.