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/’

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

Runs the full optimization routine.

Parameters

model¶ (LightningModule) – Model to fit.
train_dataloaders¶ (Union[Any, LightningDataModule, None]) – An iterable or collection of iterables specifying training samples. Alternatively, a LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.train_dataloader hook.
val_dataloaders¶ (Optional[Any]) – An iterable or collection of iterables specifying validation samples.
ckpt_path¶ (Optional[str]) – Path/URL of the checkpoint from which training is resumed. Could also be one of two special keywords "last" and "hpc". 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.
datamodule¶ (Optional[LightningDataModule]) – A LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.train_dataloader hook.

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

model¶ (Optional[LightningModule]) – The model to predict with.
dataloaders¶ (Union[Any, LightningDataModule, None]) – An iterable or collection of iterables specifying predict samples. Alternatively, a LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.predict_dataloader hook.
datamodule¶ (Optional[LightningDataModule]) – A LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.predict_dataloader hook.
return_predictions¶ (Optional[bool]) – Whether to return predictions. True by default except when an accelerator that spawns processes is used (not supported).
ckpt_path¶ (Optional[str]) – Either "best", "last", "hpc" or path to the checkpoint you wish to predict. If None and the model instance was passed, use the current weights. Otherwise, the best model checkpoint from the previous trainer.fit call will be loaded if a checkpoint callback is configured.

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.

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

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

model¶ (Optional[LightningModule]) – The model to test.
dataloaders¶ (Union[Any, LightningDataModule, None]) – An iterable or collection of iterables specifying test samples. Alternatively, a LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.test_dataloader hook.
ckpt_path¶ (Optional[str]) – Either "best", "last", "hpc" or path to the checkpoint you wish to test. If None and the model instance was passed, use the current weights. Otherwise, the best model checkpoint from the previous trainer.fit call will be loaded if a checkpoint callback is configured.
verbose¶ (bool) – If True, prints the test results.
datamodule¶ (Optional[LightningDataModule]) – A LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.test_dataloader hook.

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.

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

Perform one evaluation epoch over the validation set.

Parameters

model¶ (Optional[LightningModule]) – The model to validate.
dataloaders¶ (Union[Any, LightningDataModule, None]) – An iterable or collection of iterables specifying validation samples. Alternatively, a LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.val_dataloader hook.
ckpt_path¶ (Optional[str]) – Either "best", "last", "hpc" or path to the checkpoint you wish to validate. If None and the model instance was passed, use the current weights. Otherwise, the best model checkpoint from the previous trainer.fit call will be loaded if a checkpoint callback is configured.
verbose¶ (bool) – If True, prints the validation results.
datamodule¶ (Optional[LightningDataModule]) – A LightningDataModule that defines the :class:`~lightning.pytorch.core.hooks.DataHooks.val_dataloader hook.

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.

property callback_metrics: Dict[str, torch.Tensor]¶

The metrics available to callbacks.

This includes metrics logged via log().

..code-block:: python

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.

..code-block:: python

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]

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]¶

~lightning.pytorch.loggers.logger.Logger used.

..code-block:: python

for logger in trainer.loggers:
logger.log_metrics({“foo”: 1.0})

Type: The list of class
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 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]