Table of Contents
Shortcuts

Trainer

class lightning.pytorch.trainer.trainer.Trainer(*, accelerator='auto', strategy='auto', devices='auto', num_nodes=1, precision='32-true', logger=None, callbacks=None, fast_dev_run=False, 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, overfit_batches=0.0, val_check_interval=None, check_val_every_n_epoch=1, num_sanity_val_steps=None, log_every_n_steps=None, enable_checkpointing=None, enable_progress_bar=None, enable_model_summary=None, accumulate_grad_batches=1, gradient_clip_val=None, gradient_clip_algorithm=None, deterministic=None, benchmark=None, inference_mode=True, use_distributed_sampler=True, profiler=None, detect_anomaly=False, barebones=False, plugins=None, sync_batchnorm=False, reload_dataloaders_every_n_epochs=0, default_root_dir=None)[source]

Bases: object

Customize every aspect of training via flags.

Parameters

  • accelerator (Union[str, Accelerator]) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps”, “auto”) as well as custom accelerator instances.

  • strategy (Union[str, Strategy]) – Supports different training strategies with aliases as well custom strategies. Default: "auto".

  • devices (Union[List[int], str, int]) – The devices to use. Can be set to a positive number (int or str), a sequence of device indices (list or str), the value -1 to indicate all available devices should be used, or "auto" for automatic selection based on the chosen accelerator. Default: "auto".

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

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

  • logger (Union[Logger, Iterable[Logger], bool, None]) – 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 he first logger. Default: True.

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

  • 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.

  • 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.

  • 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.

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

  • 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.

  • 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.

  • num_sanity_val_steps (Optional[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.

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

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

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

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

  • accumulate_grad_batches (int) – Accumulates gradients over k batches before stepping the optimizer. Default: 1.

  • 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".

  • 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.

  • 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.

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

  • use_distributed_sampler (bool) – Whether to wrap the DataLoader’s sampler with torch.utils.data.DistributedSampler. If not specified this is toggled automatically for strategies that require it. By default, it will add shuffle=True for the train sampler and shuffle=False for validation/test/predict samplers. If you want to disable this logic, you can pass False and add your own distributed sampler in the dataloader hooks. If True and a distributed sampler was already added, Lightning will not replace the existing one. For iterable-style datasets, we don’t do this automatically.

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

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

  • barebones (bool) – Whether to run in “barebones mode”, where all features that may impact raw speed are disabled. This is meant for analyzing the Trainer overhead and is discouraged during regular training runs. The following features are deactivated: enable_checkpointing, logger, enable_progress_bar, log_every_n_steps, enable_model_summary, num_sanity_val_steps, fast_dev_run, detect_anomaly, profiler, log(), log_dict().

  • 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.

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

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

  • 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/’

Raises

  • TypeError – If gradient_clip_val is not an int or float.

  • MisconfigurationException – If gradient_clip_algorithm is invalid. If track_grad_norm is not a positive number or inf.

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

Runs the full optimization routine.

Parameters
Raises

TypeError – If model is not LightningModule for torch version less than 2.0.0 and if model is not LightningModule or torch._dynamo.OptimizedModule for torch versions greater than or equal to 2.0.0 .

For more information about multiple dataloaders, see this section.

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

For more information about multiple dataloaders, see this section.

Return type

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

Returns

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

Raises

  • TypeError – If no model is passed and there was no LightningModule passed in the previous run. If model passed is not LightningModule or torch._dynamo.OptimizedModule.

  • MisconfigurationException – If both dataloaders and datamodule are passed. Pass only one of these.

  • RuntimeError – If a compiled model is passed and the strategy is not supported.

See Lightning inference section for more.

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

Raises

AttributeError – If the model is not attached to the Trainer before calling this method.

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

For more information about multiple dataloaders, see this section.

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() etc. The length of the list corresponds to the number of test dataloaders used.

Raises

  • TypeError – If no model is passed and there was no LightningModule passed in the previous run. If model passed is not LightningModule or torch._dynamo.OptimizedModule.

  • MisconfigurationException – If both dataloaders and datamodule are passed. Pass only one of these.

  • RuntimeError – If a compiled model is passed and the strategy is not supported.

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

Perform one evaluation epoch over the validation set.

Parameters

For more information about multiple dataloaders, see this section.

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() etc. The length of the list corresponds to the number of validation dataloaders used.

Raises

  • TypeError – If no model is passed and there was no LightningModule passed in the previous run. If model passed is not LightningModule or torch._dynamo.OptimizedModule.

  • MisconfigurationException – If both dataloaders and datamodule are passed. Pass only one of these.

  • RuntimeError – If a compiled model is passed and the strategy is not supported.

property callback_metrics: Dict[str, torch.Tensor]

The metrics available to callbacks.

def training_step(self, batch, batch_idx):
    self.log("a_val", 2.0)


callback_metrics = trainer.callback_metrics
assert callback_metrics["a_val"] == 2.0
Return type

Dict[str, Tensor]

property checkpoint_callback: Optional[lightning.pytorch.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[lightning.pytorch.callbacks.checkpoint.Checkpoint]

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

Return type

List[Checkpoint]

property ckpt_path: Optional[Union[str, pathlib.Path]]

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

Return type

Union[str, Path, None]

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[lightning.pytorch.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[lightning.pytorch.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]

The estimated number of batches that will optimizer.step() during training.

This accounts for gradient accumulation and the current trainer configuration. This might sets up your training dataloader if hadn’t been set up already.

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

MisconfigurationException – If estimated stepping batches cannot be computed due to different accumulate_grad_batches at different epochs.

Return type

Union[int, float]

property global_step: int

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

This includes multiple optimizers (if enabled).

Return type

int

property is_global_zero: bool

Whether this process is the global zero in multi-node training.

def training_step(self, batch, batch_idx):
    if self.trainer.is_global_zero:
        print("in node 0, accelerator 0")
Return type

bool

property is_last_batch: bool

Whether trainer is executing the last batch.

Return type

bool

property log_dir: Optional[str]

The directory for the current experiment. Use this to save images to, etc…

def training_step(self, batch, batch_idx):
    img = ...
    save_img(img, self.trainer.log_dir)
Return type

Optional[str]

property logged_metrics: Dict[str, torch.Tensor]

The metrics sent to the loggers.

This includes metrics logged via log() with the logger argument set.

Return type

Dict[str, Tensor]

property logger: Optional[lightning.pytorch.loggers.logger.Logger]

The first Logger being used.

Return type

Optional[Logger]

property loggers: List[lightning.pytorch.loggers.logger.Logger]

The list of Logger used.

for logger in trainer.loggers:
    logger.log_metrics({"foo": 1.0})
Return type

List[Logger]

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 num_predict_batches: List[Union[int, float]]

The number of prediction batches that will be used during trainer.predict().

Return type

List[Union[int, float]]

property num_sanity_val_batches: List[Union[int, float]]

The number of validation batches that will be used during the sanity-checking part of trainer.fit().

Return type

List[Union[int, float]]

property num_test_batches: List[Union[int, float]]

The number of test batches that will be used during trainer.test().

Return type

List[Union[int, float]]

property num_training_batches: Union[int, float]

The number of training batches that will be used during trainer.fit().

Return type

Union[int, float]

property num_val_batches: List[Union[int, float]]

The number of validation batches that will be used during trainer.fit() or trainer.validate().

Return type

List[Union[int, float]]

property predict_dataloaders: Optional[Any]

The prediction dataloader(s) used during trainer.predict().

Return type

Optional[Any]

property progress_bar_callback: Optional[lightning.pytorch.callbacks.progress.progress_bar.ProgressBar]

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

Return type

Optional[ProgressBar]

property progress_bar_metrics: Dict[str, float]

The metrics sent to the progress bar.

This includes metrics logged via log() with the prog_bar argument set.

Return type

Dict[str, float]

property received_sigterm: bool

Whether a signal.SIGTERM signal was received.

For example, this can be checked to exit gracefully.

Return type

bool

property sanity_checking: bool

Whether sanity checking is running.

Useful to disable some hooks, logging or callbacks during the sanity checking.

Return type

bool

property test_dataloaders: Optional[Any]

The test dataloader(s) used during trainer.test().

Return type

Optional[Any]

property train_dataloader: Optional[Any]

The training dataloader(s) used during trainer.fit().

Return type

Optional[Any]

property val_dataloaders: Optional[Any]

The validation dataloader(s) used during trainer.fit() or trainer.validate().

Return type

Optional[Any]

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. Read PyTorch Lightning's Privacy Policy.