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.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, **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
- 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.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.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.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, **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
- 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