Trainer

class deeplay.trainer.Trainer(*, accelerator: str | Accelerator = 'auto', strategy: str | Strategy = 'auto', devices: list[int] | str | int = 'auto', num_nodes: int = 1, precision: Literal[64, 32, 16] | Literal['transformer-engine', 'transformer-engine-float16', '16-true', '16-mixed', 'bf16-true', 'bf16-mixed', '32-true', '64-true'] | Literal['64', '32', '16', 'bf16'] | None = None, logger: Logger | Iterable[Logger] | bool | None = None, callbacks: list[Callback] | Callback | None = None, fast_dev_run: int | bool = False, max_epochs: int | None = None, min_epochs: int | None = None, max_steps: int = -1, min_steps: int | None = None, max_time: str | timedelta | dict[str, int] | None = None, limit_train_batches: int | float | None = None, limit_val_batches: int | float | None = None, limit_test_batches: int | float | None = None, limit_predict_batches: int | float | None = None, overfit_batches: int | float = 0.0, val_check_interval: int | float | None = None, check_val_every_n_epoch: int | None = 1, num_sanity_val_steps: int | None = None, log_every_n_steps: int | None = None, enable_checkpointing: bool | None = None, enable_progress_bar: bool | None = None, enable_model_summary: bool | None = None, accumulate_grad_batches: int = 1, gradient_clip_val: int | float | None = None, gradient_clip_algorithm: str | None = None, deterministic: bool | Literal['warn'] | None = None, benchmark: bool | None = None, inference_mode: bool = True, use_distributed_sampler: bool = True, profiler: Profiler | str | None = None, detect_anomaly: bool = False, barebones: bool = False, plugins: Precision | ClusterEnvironment | CheckpointIO | LayerSync | list[Precision | ClusterEnvironment | CheckpointIO | LayerSync] | None = None, sync_batchnorm: bool = False, reload_dataloaders_every_n_epochs: int = 0, default_root_dir: str | Path | None = None, model_registry: str | None = None)

Bases: Trainer

Attributes Summary

history

Returns the history of the training process.

Methods Summary

disable_history()	Disables the history callback.
disable_progress_bar()	Disables the progress bar.
rich_progress_bar([refresh_rate, leave])	Enables the rich progress bar.
tqdm_progress_bar([refresh_rate])	Enables the TQDM progress bar.

Attributes Documentation

history

Returns the history of the training process.

Returns

LogHistory

The log history callback object.

Raises

ValueError

If the history callback is not found.

Methods Documentation

disable_history() → None

Disables the history callback.

Raises

ValueError

If the history callback is not found.

disable_progress_bar() → None

Disables the progress bar.

rich_progress_bar(refresh_rate: int = 1, leave: bool = False) → None

Enables the rich progress bar.

Parameters

refresh_rateint, optional

The refresh rate of the progress bar, by default 1.

leavebool, optional

Whether to leave the progress bar after completion, by default False.

tqdm_progress_bar(refresh_rate: int = 1) → None

Enables the TQDM progress bar.

Parameters

refresh_rateint, optional

The refresh rate of the progress bar, by detault 1.