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Trainer

class pytorch_lightning.trainer.trainer.Trainer(logger=True, enable_checkpointing=True, callbacks=None, default_root_dir=None, gradient_clip_val=None, gradient_clip_algorithm=None, num_nodes=1, num_processes=None, devices=None, gpus=None, auto_select_gpus=None, tpu_cores=None, ipus=None, enable_progress_bar=True, overfit_batches=0.0, track_grad_norm=- 1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=None, max_epochs=None, min_epochs=None, max_steps=- 1, min_steps=None, max_time=None, limit_train_batches=None, limit_val_batches=None, limit_test_batches=None, limit_predict_batches=None, val_check_interval=None, log_every_n_steps=50, accelerator=None, strategy=None, sync_batchnorm=False, precision=32, enable_model_summary=True, num_sanity_val_steps=2, resume_from_checkpoint=None, profiler=None, benchmark=None, deterministic=None, reload_dataloaders_every_n_epochs=0, auto_lr_find=False, replace_sampler_ddp=True, detect_anomaly=False, auto_scale_batch_size=False, plugins=None, amp_backend=None, amp_level=None, move_metrics_to_cpu=False, multiple_trainloader_mode='max_size_cycle', inference_mode=True)[source]

Bases: object

Customize every aspect of training via flags.

Parameters

  • accelerator (Union[str, Accelerator, None]) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps”, “auto”) as well as custom accelerator instances.

  • accumulate_grad_batches (Union[int, Dict[int, int], None]) – Accumulates grads every k batches or as set up in the dict. Default: None.

  • amp_backend (Optional[str]) –

    The mixed precision backend to use (“native” or “apex”). Default: 'native''.

    Deprecated since version v1.9: Setting amp_backend inside the Trainer is deprecated in v1.8.0 and will be removed in v2.0.0. This argument was only relevant for apex which is being removed.

  • amp_level (Optional[str]) –

    The optimization level to use (O1, O2, etc…). By default it will be set to “O2” if amp_backend is set to “apex”.

    Deprecated since version v1.8: Setting amp_level inside the Trainer is deprecated in v1.8.0 and will be removed in v2.0.0.

  • auto_lr_find (Union[bool, str]) – If set to True, will make trainer.tune() run a learning rate finder, trying to optimize initial learning for faster convergence. trainer.tune() method will set the suggested learning rate in self.lr or self.learning_rate in the LightningModule. To use a different key set a string instead of True with the key name. Default: False.

  • auto_scale_batch_size (Union[str, bool]) – If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory. The result will be stored in self.batch_size in the LightningModule or LightningDataModule depending on your setup. Additionally, can be set to either power that estimates the batch size through a power search or binsearch that estimates the batch size through a binary search. Default: False.

  • auto_select_gpus (Optional[bool]) –

    If enabled and gpus or devices is an integer, pick available gpus automatically. This is especially useful when GPUs are configured to be in “exclusive mode”, such that only one process at a time can access them. Default: False.

    Deprecated since version v1.9: auto_select_gpus has been deprecated in v1.9.0 and will be removed in v2.0.0. Please use the function find_usable_cuda_devices() instead.

  • benchmark (Optional[bool]) – The value (True or False) to set torch.backends.cudnn.benchmark to. The value for torch.backends.cudnn.benchmark set in the current session will be used (False if not manually set). If deterministic is set to True, this will default to False. Override to manually set a different value. Default: None.

  • callbacks (Union[List[Callback], Callback, None]) – Add a callback or list of callbacks. Default: None.

  • enable_checkpointing (bool) – If True, enable checkpointing. It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in callbacks. Default: True.

  • check_val_every_n_epoch (Optional[int]) – Perform a validation loop every after every N training epochs. If None, validation will be done solely based on the number of training batches, requiring val_check_interval to be an integer value. Default: 1.

  • default_root_dir (Union[str, Path, None]) – Default path for logs and weights when no logger/ckpt_callback passed. Default: os.getcwd(). Can be remote file paths such as s3://mybucket/path or ‘hdfs://path/’

  • detect_anomaly (bool) – Enable anomaly detection for the autograd engine. Default: False.

  • deterministic (Union[bool, Literal[‘warn’], None]) – If True, sets whether PyTorch operations must use deterministic algorithms. Set to "warn" to use deterministic algorithms whenever possible, throwing warnings on operations that don’t support deterministic mode (requires PyTorch 1.11+). If not set, defaults to False. Default: None.

  • devices (Union[List[int], str, int, None]) – Will be mapped to either gpus, tpu_cores, num_processes or ipus, based on the accelerator type.

  • fast_dev_run (Union[int, bool]) – Runs n if set to n (int) else 1 if set to True batch(es) of train, val and test to find any bugs (ie: a sort of unit test). Default: False.

  • gpus (Union[List[int], str, int, None]) –

    Number of GPUs to train on (int) or which GPUs to train on (list or str) applied per node Default: None.

    Deprecated since version v1.7: gpus has been deprecated in v1.7 and will be removed in v2.0. Please use accelerator='gpu' and devices=x instead.

  • gradient_clip_val (Union[int, float, None]) – The value at which to clip gradients. Passing gradient_clip_val=None disables gradient clipping. If using Automatic Mixed Precision (AMP), the gradients will be unscaled before. Default: None.

  • gradient_clip_algorithm (Optional[str]) – The gradient clipping algorithm to use. Pass gradient_clip_algorithm="value" to clip by value, and gradient_clip_algorithm="norm" to clip by norm. By default it will be set to "norm".

  • limit_train_batches (Union[int, float, None]) – How much of training dataset to check (float = fraction, int = num_batches). Default: 1.0.

  • limit_val_batches (Union[int, float, None]) – How much of validation dataset to check (float = fraction, int = num_batches). Default: 1.0.

  • limit_test_batches (Union[int, float, None]) – How much of test dataset to check (float = fraction, int = num_batches). Default: 1.0.

  • limit_predict_batches (Union[int, float, None]) – How much of prediction dataset to check (float = fraction, int = num_batches). Default: 1.0.

  • logger (Union[Logger, Iterable[Logger], bool]) – Logger (or iterable collection of loggers) for experiment tracking. A True value uses the default TensorBoardLogger if it is installed, otherwise CSVLogger. False will disable logging. If multiple loggers are provided, local files (checkpoints, profiler traces, etc.) are saved in the log_dir of the first logger. Default: True.

  • log_every_n_steps (int) – How often to log within steps. Default: 50.

  • enable_progress_bar (bool) – Whether to enable to progress bar by default. Default: True.

  • profiler (Union[Profiler, str, None]) – To profile individual steps during training and assist in identifying bottlenecks. Default: None.

  • overfit_batches (Union[int, float]) – Overfit a fraction of training/validation data (float) or a set number of batches (int). Default: 0.0.

  • plugins (Union[PrecisionPlugin, ClusterEnvironment, CheckpointIO, LayerSync, str, List[Union[PrecisionPlugin, ClusterEnvironment, CheckpointIO, LayerSync, str]], None]) – Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins. Default: None.

  • precision (Union[Literal[64, 32, 16], Literal[‘64’, ‘32’, ‘16’, ‘bf16’]]) – Double precision (64), full precision (32), half precision (16) or bfloat16 precision (bf16). Can be used on CPU, GPU, TPUs, HPUs or IPUs. Default: 32.

  • max_epochs (Optional[int]) – Stop training once this number of epochs is reached. Disabled by default (None). If both max_epochs and max_steps are not specified, defaults to max_epochs = 1000. To enable infinite training, set max_epochs = -1.

  • min_epochs (Optional[int]) – Force training for at least these many epochs. Disabled by default (None).

  • max_steps (int) – Stop training after this number of steps. Disabled by default (-1). If max_steps = -1 and max_epochs = None, will default to max_epochs = 1000. To enable infinite training, set max_epochs to -1.

  • min_steps (Optional[int]) – Force training for at least these number of steps. Disabled by default (None).

  • max_time (Union[str, timedelta, Dict[str, int], None]) – Stop training after this amount of time has passed. Disabled by default (None). The time duration can be specified in the format DD:HH:MM:SS (days, hours, minutes seconds), as a datetime.timedelta, or a dictionary with keys that will be passed to datetime.timedelta.

  • num_nodes (int) – Number of GPU nodes for distributed training. Default: 1.

  • num_processes (Optional[int]) –

    Number of processes for distributed training with accelerator="cpu". Default: 1.

    Deprecated since version v1.7: num_processes has been deprecated in v1.7 and will be removed in v2.0. Please use accelerator='cpu' and devices=x instead.

  • num_sanity_val_steps (int) – Sanity check runs n validation batches before starting the training routine. Set it to -1 to run all batches in all validation dataloaders. Default: 2.

  • reload_dataloaders_every_n_epochs (int) – Set to a non-negative integer to reload dataloaders every n epochs. Default: 0.

  • replace_sampler_ddp (bool) – Explicitly enables or disables sampler replacement. If not specified this will toggled automatically when DDP is used. By default it will add shuffle=True for train sampler and shuffle=False for val/test sampler. If you want to customize it, you can set replace_sampler_ddp=False and add your own distributed sampler.

  • resume_from_checkpoint (Union[str, Path, None]) –

    Path/URL of the checkpoint from which training is resumed. If there is no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint, training will start from the beginning of the next epoch.

    Deprecated since version v1.5: resume_from_checkpoint is deprecated in v1.5 and will be removed in v2.0. Please pass the path to Trainer.fit(..., ckpt_path=...) instead.

  • strategy (Union[str, Strategy, None]) – Supports different training strategies with aliases as well custom strategies. Default: None.

  • sync_batchnorm (bool) – Synchronize batch norm layers between process groups/whole world. Default: False.

  • tpu_cores (Union[List[int], str, int, None]) –

    How many TPU cores to train on (1 or 8) / Single TPU to train on (1) Default: None.

    Deprecated since version v1.7: tpu_cores has been deprecated in v1.7 and will be removed in v2.0. Please use accelerator='tpu' and devices=x instead.

  • ipus (Optional[int]) –

    How many IPUs to train on. Default: None.

    Deprecated since version v1.7: ipus has been deprecated in v1.7 and will be removed in v2.0. Please use accelerator='ipu' and devices=x instead.

  • track_grad_norm (Union[int, float, str]) – -1 no tracking. Otherwise tracks that p-norm. May be set to ‘inf’ infinity-norm. If using Automatic Mixed Precision (AMP), the gradients will be unscaled before logging them. Default: -1.

  • val_check_interval (Union[int, float, None]) – How often to check the validation set. Pass a float in the range [0.0, 1.0] to check after a fraction of the training epoch. Pass an int to check after a fixed number of training batches. An int value can only be higher than the number of training batches when check_val_every_n_epoch=None, which validates after every N training batches across epochs or during iteration-based training. Default: 1.0.

  • enable_model_summary (bool) – Whether to enable model summarization by default. Default: True.

  • move_metrics_to_cpu (bool) – Whether to force internal logged metrics to be moved to cpu. This can save some gpu memory, but can make training slower. Use with attention. Default: False.

  • multiple_trainloader_mode (str) – How to loop over the datasets when there are multiple train loaders. In ‘max_size_cycle’ mode, the trainer ends one epoch when the largest dataset is traversed, and smaller datasets reload when running out of their data. In ‘min_size’ mode, all the datasets reload when reaching the minimum length of datasets. Default: "max_size_cycle".

  • inference_mode (bool) – Whether to use torch.inference_mode() or torch.no_grad() during evaluation (validate/test/predict).

fit(model, train_dataloaders=None, val_dataloaders=None, datamodule=None, ckpt_path=None)[source]

Runs the full optimization routine.

Parameters
Return type

None

predict(model=None, dataloaders=None, datamodule=None, return_predictions=None, ckpt_path=None)[source]

Run inference on your data. This will call the model forward function to compute predictions. Useful to perform distributed and batched predictions. Logging is disabled in the predict hooks.

Parameters
Return type

Union[List[Any], List[List[Any]], None]

Returns

Returns a list of dictionaries, one for each provided dataloader containing their respective predictions.

See Lightning inference section for more.

reset_predict_dataloader(model=None)[source]

Resets the predict dataloader and determines the number of batches.

Parameters

model (Optional[LightningModule]) – The LightningModule if called outside of the trainer scope.

Return type

None

reset_test_dataloader(model=None)[source]

Resets the test dataloader and determines the number of batches.

Parameters

model (Optional[LightningModule]) – The LightningModule if called outside of the trainer scope.

Return type

None

reset_train_dataloader(model=None)[source]

Resets the train dataloader and initialises required variables (number of batches, when to validate, etc.).

Parameters

model (Optional[LightningModule]) – The LightningModule if calling this outside of the trainer scope.

Return type

None

reset_val_dataloader(model=None)[source]

Resets the validation dataloader and determines the number of batches.

Parameters

model (Optional[LightningModule]) – The LightningModule if called outside of the trainer scope.

Return type

None

save_checkpoint(filepath, weights_only=False, storage_options=None)[source]

Runs routine to create a checkpoint.

Parameters

  • filepath (Union[str, Path]) – Path where checkpoint is saved.

  • weights_only (bool) – If True, will only save the model weights.

  • storage_options (Optional[Any]) – parameter for how to save to storage, passed to CheckpointIO plugin

Return type

None

test(model=None, dataloaders=None, ckpt_path=None, verbose=True, datamodule=None)[source]

Perform one evaluation epoch over the test set. It’s separated from fit to make sure you never run on your test set until you want to.

Parameters
Return type

List[Dict[str, float]]

Returns

List of dictionaries with metrics logged during the test phase, e.g., in model- or callback hooks like test_step(), test_epoch_end(), etc. The length of the list corresponds to the number of test dataloaders used.

tune(model, train_dataloaders=None, val_dataloaders=None, dataloaders=None, datamodule=None, scale_batch_size_kwargs=None, lr_find_kwargs=None, method='fit')[source]

Runs routines to tune hyperparameters before training.

Parameters
Return type

_TunerResult

validate(model=None, dataloaders=None, ckpt_path=None, verbose=True, datamodule=None)[source]

Perform one evaluation epoch over the validation set.

Parameters
Return type

List[Dict[str, float]]

Returns

List of dictionaries with metrics logged during the validation phase, e.g., in model- or callback hooks like validation_step(), validation_epoch_end(), etc. The length of the list corresponds to the number of validation dataloaders used.

property checkpoint_callback: Optional[pytorch_lightning.callbacks.checkpoint.Checkpoint]

The first ModelCheckpoint callback in the Trainer.callbacks list, or None if it doesn’t exist.

Return type

Optional[Checkpoint]

property checkpoint_callbacks: List[pytorch_lightning.callbacks.checkpoint.Checkpoint]

A list of all instances of ModelCheckpoint found in the Trainer.callbacks list.

Return type

List[Checkpoint]

property ckpt_path: Optional[str]

Set to the path/URL of a checkpoint loaded via fit(), validate(), test(), or predict(). None otherwise.

Return type

Optional[str]

property current_epoch: int

The current epoch, updated after the epoch end hooks are run.

Return type

int

property default_root_dir: str

The default location to save artifacts of loggers, checkpoints etc.

It is used as a fallback if logger or checkpoint callback do not define specific save paths.

Return type

str

property device_ids: List[int]

List of device indexes per node.

Return type

List[int]

property early_stopping_callback: Optional[pytorch_lightning.callbacks.early_stopping.EarlyStopping]

The first EarlyStopping callback in the Trainer.callbacks list, or None if it doesn’t exist.

Return type

Optional[EarlyStopping]

property early_stopping_callbacks: List[pytorch_lightning.callbacks.early_stopping.EarlyStopping]

A list of all instances of EarlyStopping found in the Trainer.callbacks list.

Return type

List[EarlyStopping]

property enable_validation: bool

Check if we should run validation during training.

Return type

bool

property estimated_stepping_batches: Union[int, float]

Estimated stepping batches for the complete training inferred from DataLoaders, gradient accumulation factor and distributed setup.

Examples:

def configure_optimizers(self):
    optimizer = ...
    scheduler = torch.optim.lr_scheduler.OneCycleLR(
        optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches
    )
    return [optimizer], [scheduler]
Return type

Union[int, float]

property global_step: int

The number of optimizer steps taken (does not reset each epoch).

This includes multiple optimizers and TBPTT steps (if enabled).

Return type

int

property is_last_batch: bool

Whether trainer is executing the last batch.

Return type

bool

property model: Optional[torch.nn.modules.module.Module]

The LightningModule, but possibly wrapped into DataParallel or DistributedDataParallel.

To access the pure LightningModule, use lightning_module() instead.

Return type

Optional[Module]

property num_devices: int

Number of devices the trainer uses per node.

Return type

int

property prediction_writer_callbacks: List[pytorch_lightning.callbacks.prediction_writer.BasePredictionWriter]

A list of all instances of BasePredictionWriter found in the Trainer.callbacks list.

Return type

List[BasePredictionWriter]

property progress_bar_callback: Optional[pytorch_lightning.callbacks.progress.base.ProgressBarBase]

An instance of ProgressBarBase found in the Trainer.callbacks list, or None if one doesn’t exist.

Return type

Optional[ProgressBarBase]

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