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
- **kwargs
additional arguments
- input
- 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, nvnmd_weight: Optional[str] = None, united_model: bool = False, **kwargs)[source]
Freeze the graph in supplied folder.
- Parameters
- checkpoint_folder
str
location of either the folder with checkpoint or the checkpoint prefix
- output
str
output file name
- node_names
Optional
[str
],optional
names of nodes to output, by default None
- nvnmd_weight
Optional
[str
],optional
nvnmd weight file
- united_modelbool
when in multi-task mode, freeze all nodes into one unit model
- **kwargs
other arguments
- checkpoint_folder
- deepmd.entrypoints.make_model_devi(*, models: list, system: str, set_prefix: str, output: str, frequency: int, real_error: bool = False, atomic: bool = False, relative: Optional[float] = None, relative_v: Optional[float] = None, **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.
- real_errorbool, default:
False
If True, calculate the RMS real error instead of model deviation.
- atomicbool, default:
False
If True, calculate the force model deviation of each atom.
- relative
float
, default:None
If given, calculate the relative model deviation of force. The value is the level parameter for computing the relative model deviation of the force.
- relative_v
float
, default:None
If given, calculate the relative model deviation of virial. The value is the level parameter for computing the relative model deviation of the virial.
- **kwargs
Arbitrary keyword arguments.
- models
- deepmd.entrypoints.neighbor_stat(*, system: str, rcut: float, type_map: List[str], one_type: bool = False, **kwargs)[source]
Calculate neighbor statistics.
- Parameters
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.start_dpgui(*, port: int, bind_all: bool, **kwargs)[source]
Host DP-GUI server.
- Parameters
- Raises
ModuleNotFoundError
The dpgui package is not installed
- deepmd.entrypoints.test(*, model: str, system: str, datafile: 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
- datafile
str
the path to the list of systems to test
- set_prefix
str
string prefix of set
- numb_test
int
munber of tests to do. 0 means all data.
- 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
- **kwargs
additional arguments
- 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, finetune: Optional[str] = None, **kwargs)
Run DeePMD model training.
- Parameters
- INPUT
str
json/yaml control file
- init_model
Optional
[str
] path prefix of checkpoint files or None
- restart
Optional
[str
] path prefix of checkpoint files 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_compressbool
indicates whether in the model compress mode
- skip_neighbor_statbool, default=False
skip checking neighbor statistics
- finetune
Optional
[str
] path to pretrained model or None
- **kwargs
additional arguments
- INPUT
- Raises
RuntimeError
if distributed training job name 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
- **kwargs
additional arguments
- input
deepmd.entrypoints.convert module
deepmd.entrypoints.doc module
deepmd.entrypoints.freeze module
Script for freezing TF trained graph so it can be used with LAMMPS and i-PI.
References
- deepmd.entrypoints.freeze.freeze(*, checkpoint_folder: str, output: str, node_names: Optional[str] = None, nvnmd_weight: Optional[str] = None, united_model: bool = False, **kwargs)[source]
Freeze the graph in supplied folder.
- Parameters
- checkpoint_folder
str
location of either the folder with checkpoint or the checkpoint prefix
- output
str
output file name
- node_names
Optional
[str
],optional
names of nodes to output, by default None
- nvnmd_weight
Optional
[str
],optional
nvnmd weight file
- united_modelbool
when in multi-task mode, freeze all nodes into one unit model
- **kwargs
other arguments
- checkpoint_folder
deepmd.entrypoints.gui module
- deepmd.entrypoints.gui.start_dpgui(*, port: int, bind_all: bool, **kwargs)[source]
Host DP-GUI server.
- Parameters
- Raises
ModuleNotFoundError
The dpgui package is not installed
deepmd.entrypoints.ipi module
Use dp_ipi inside the Python package.
deepmd.entrypoints.main module
DeePMD-Kit entry point module.
- deepmd.entrypoints.main.main(args: Optional[Union[List[str], Namespace]] = None)[source]
DeePMD-Kit entry point.
- Parameters
- args
List
[str
]or
argparse.Namespace
,optional
list of command line arguments, used to avoid calling from the subprocess, as it is quite slow to import tensorflow; if Namespace is given, it will be used directly
- args
- Raises
RuntimeError
if no command was input
- deepmd.entrypoints.main.main_parser() ArgumentParser [source]
DeePMD-Kit commandline options argument parser.
- Returns
argparse.ArgumentParser
main parser of DeePMD-kit
deepmd.entrypoints.neighbor_stat module
- deepmd.entrypoints.neighbor_stat.neighbor_stat(*, system: str, rcut: float, type_map: List[str], one_type: bool = False, **kwargs)[source]
Calculate neighbor statistics.
- Parameters
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, datafile: 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
- datafile
str
the path to the list of systems to test
- set_prefix
str
string prefix of set
- numb_test
int
munber of tests to do. 0 means all data.
- 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
- **kwargs
additional arguments
- 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, finetune: Optional[str] = None, **kwargs)[source]
Run DeePMD model training.
- Parameters
- INPUT
str
json/yaml control file
- init_model
Optional
[str
] path prefix of checkpoint files or None
- restart
Optional
[str
] path prefix of checkpoint files 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_compressbool
indicates whether in the model compress mode
- skip_neighbor_statbool, default=False
skip checking neighbor statistics
- finetune
Optional
[str
] path to pretrained model or None
- **kwargs
additional arguments
- INPUT
- Raises
RuntimeError
if distributed training job name is wrong
deepmd.entrypoints.transfer module
Module used for transfering parameters between models.