deepmd.pt.train.training

deepmd.pt.train.training#

Attributes#

log

Classes#

Trainer

Functions#

get_additional_data_requirement(...)

whether_hessian(→ bool)

get_loss(→ deepmd.pt.loss.TaskLoss)

get_single_model(→ Any)

get_model_for_wrapper(→ Any)

get_case_embd_config(→ tuple[bool, dict[str, int]])

model_change_out_bias(→ Any)

Module Contents#

deepmd.pt.train.training.log[source]#
class deepmd.pt.train.training.Trainer(config: dict[str, Any], training_data: deepmd.pt.utils.dataloader.DpLoaderSet, stat_file_path: str | None = None, validation_data: deepmd.pt.utils.dataloader.DpLoaderSet | None = None, init_model: str | None = None, restart_model: str | None = None, finetune_model: str | None = None, force_load: bool = False, shared_links: dict[str, str] | None = None, finetune_links: dict[str, str] | None = None, init_frz_model: str | None = None)[source]#
restart_training[source]#
multi_task[source]#
finetune_update_stat = False[source]#
model_keys[source]#
rank[source]#
world_size[source]#
num_model[source]#
num_steps[source]#
disp_file[source]#
disp_freq[source]#
disp_avg[source]#
save_ckpt[source]#
save_freq[source]#
max_ckpt_keep[source]#
display_in_training[source]#
timing_in_training[source]#
change_bias_after_training[source]#
lcurve_should_print_header = True[source]#
model[source]#
warmup_steps[source]#
gradient_max_norm[source]#
wrapper[source]#
start_step = 0[source]#
enable_tensorboard[source]#
tensorboard_log_dir[source]#
tensorboard_freq[source]#
enable_profiler[source]#
profiling[source]#
profiling_file[source]#
run() → None[source]#
save_model(save_path: str, lr: float = 0.0, step: int = 0) → None[source]#
get_data(is_train: bool = True, task_key: str = 'Default') → tuple[dict[str, Any], dict[str, Any], dict[str, Any]][source]#
print_header(fout: Any, train_results: dict[str, Any], valid_results: dict[str, Any]) → None[source]#
print_on_training(fout: Any, step_id: int, cur_lr: float, train_results: dict, valid_results: dict) → None[source]#
deepmd.pt.train.training.get_additional_data_requirement(_model: Any) → list[deepmd.utils.data.DataRequirementItem][source]#
deepmd.pt.train.training.whether_hessian(loss_params: dict[str, Any]) → bool[source]#
deepmd.pt.train.training.get_loss(loss_params: dict[str, Any], start_lr: float, _ntypes: int, _model: Any) → deepmd.pt.loss.TaskLoss[source]#
deepmd.pt.train.training.get_single_model(_model_params: dict[str, Any]) → Any[source]#
deepmd.pt.train.training.get_model_for_wrapper(_model_params: dict[str, Any], resuming: bool = False, _loss_params: dict[str, Any] | None = None) → Any[source]#
deepmd.pt.train.training.get_case_embd_config(_model_params: dict[str, Any]) → tuple[bool, dict[str, int]][source]#
deepmd.pt.train.training.model_change_out_bias(_model: Any, _sample_func: Callable[[], Any], _bias_adjust_mode: str = 'change-by-statistic') → Any[source]#
Contents