deepmd.pt_expt.entrypoints.main

deepmd.pt_expt.entrypoints.main#

Training entrypoint for the pt_expt backend.

Attributes#

log

Functions#

`_detect_lmdb_path`(→ str \| None)	Return the LMDB path when `systems_raw` is a scalar LMDB string.
`_get_neighbor_stat_data`(→ Any)	Return a data proxy suitable for `BaseModel.update_sel` (neighbor stat).
`_build_data_system`(...)	Build a data system from dataset config, routing LMDB paths to LmdbDataSystem.
`get_trainer`(→ deepmd.pt_expt.train.training.Trainer)	Build a `training.Trainer` from a normalised config.
`train`(→ None)	Run training with the pt_expt backend.
`freeze`(→ None)	Freeze a pt_expt checkpoint into a .pte exported model.
`change_bias`(→ None)	Change the output bias of a pt_expt model.
`main`(→ None)	Entry point for the pt_expt backend CLI.

Module Contents#

deepmd.pt_expt.entrypoints.main.log[source]#

deepmd.pt_expt.entrypoints.main._detect_lmdb_path(systems_raw: Any) → str | None[source]#

Return the LMDB path when systems_raw is a scalar LMDB string.

Returns None for non-LMDB inputs. Raises ValueError if systems_raw is a list containing any LMDB path, so both _get_neighbor_stat_data and _build_data_system fail with the same clear message instead of the opaque error from process_systems() / DeepmdData.

deepmd.pt_expt.entrypoints.main._get_neighbor_stat_data(dataset_params: dict[str, Any], type_map: list[str] | None) → Any[source]#

Return a data proxy suitable for BaseModel.update_sel (neighbor stat).

Routes a scalar LMDB systems path through dpmodel’s make_neighbor_stat_data; falls back to the legacy get_data for npy/HDF5 directories.

deepmd.pt_expt.entrypoints.main._build_data_system(dataset_params: dict[str, Any], type_map: list[str], seed: int | None = None) → deepmd.utils.data_system.DeepmdDataSystem | deepmd.pt_expt.utils.lmdb_dataset.LmdbDataSystem[source]#

Build a data system from dataset config, routing LMDB paths to LmdbDataSystem.

A scalar systems value pointing at an LMDB directory triggers the LMDB adapter; otherwise we fall through to the legacy DeepmdDataSystem path with system expansion.

deepmd.pt_expt.entrypoints.main.get_trainer(config: dict[str, Any], init_model: str | None = None, restart_model: str | None = None, finetune_model: str | None = None, finetune_links: dict | None = None, shared_links: dict | None = None) → deepmd.pt_expt.train.training.Trainer[source]#: Build a training.Trainer from a normalised config.

deepmd.pt_expt.entrypoints.main.train(input_file: str, init_model: str | None = None, restart: str | None = None, finetune: str | None = None, model_branch: str = '', use_pretrain_script: bool = False, skip_neighbor_stat: bool = False, output: str = 'out.json') → None[source]#

Run training with the pt_expt backend.

Parameters:

input_filestr: Path to the JSON configuration file.
init_modelstr or None: Path to a checkpoint to initialise weights from.
restartstr or None: Path to a checkpoint to restart training from.
finetunestr or None: Path to a pretrained checkpoint to fine-tune from.
model_branchstr: Branch to select from a multi-task pretrained model.
use_pretrain_scriptbool: If True, copy descriptor/fitting params from the pretrained model.
skip_neighbor_statbool: Skip neighbour statistics calculation.
outputstr: Where to dump the normalised config.

deepmd.pt_expt.entrypoints.main.freeze(model: str, output: str = 'frozen_model.pte', head: str | None = None) → None[source]#

Freeze a pt_expt checkpoint into a .pte exported model.

Parameters:

modelstr: Path to the checkpoint file (.pt).
outputstr: Path for the output .pte file.
headstr or None: Head to freeze in multi-task mode.

deepmd.pt_expt.entrypoints.main.change_bias(input_file: str, mode: str = 'change', bias_value: list | None = None, datafile: str | None = None, system: str = '.', numb_batch: int = 0, model_branch: str | None = None, output: str | None = None) → None[source]#

Change the output bias of a pt_expt model.

Parameters:

input_filestr: Path to the model file (.pt checkpoint or .pte frozen model).
modestr: "change" or "set".
bias_valuelist or None: User-defined bias values (one per type).
datafilestr or None: File listing data system paths.
systemstr: Data system path (used when datafile is None).
numb_batchint: Number of batches for statistics (0 = all).
model_branchstr or None: Branch name for multi-task models.
outputstr or None: Output file path.

deepmd.pt_expt.entrypoints.main.main(args: list[str] | argparse.Namespace | None = None) → None[source]#

Entry point for the pt_expt backend CLI.

Parameters:

argslist[str] | argparse.Namespace | None: Command-line arguments or pre-parsed namespace.