espnet2.train.trainer.TrainerOptions

source

class espnet2.train.trainer.TrainerOptions(ngpu: int, resume: bool, use_amp: bool, train_dtype: str, grad_noise: bool, accum_grad: int, grad_clip: float, grad_clip_type: float, log_interval: int | None, no_forward_run: bool, use_matplotlib: bool, use_tensorboard: bool, use_wandb: bool, adapter: str, use_adapter: bool, save_strategy: str, output_dir: pathlib.Path | str, max_epoch: int, seed: int, sharded_ddp: bool, patience: int | None, keep_nbest_models: int | List[int], nbest_averaging_interval: int, early_stopping_criterion: Sequence[str], best_model_criterion: Sequence[Sequence[str]], val_scheduler_criterion: Sequence[str], unused_parameters: bool, wandb_model_log_interval: int, create_graph_in_tensorboard: bool)

Bases: object

accum_grad : int

adapter : str

best_model_criterion : Sequence[Sequence[str]]

create_graph_in_tensorboard : bool

early_stopping_criterion : Sequence[str]

grad_clip : float

grad_clip_type : float

grad_noise : bool

keep_nbest_models : int | List[int]

log_interval : int | None

max_epoch : int

nbest_averaging_interval : int

ngpu : int

no_forward_run : bool

output_dir : Path | str

patience : int | None

resume : bool

save_strategy : str

seed : int

sharded_ddp : bool

train_dtype : str

unused_parameters : bool

use_adapter : bool

use_amp : bool

use_matplotlib : bool

use_tensorboard : bool

use_wandb : bool

val_scheduler_criterion : Sequence[str]

wandb_model_log_interval : int