class pytorch_lightning.callbacks.ModelCheckpoint(dirpath=None, filename=None, monitor=None, verbose=False, save_last=None, save_top_k=1, save_weights_only=False, mode='min', auto_insert_metric_name=True, every_n_train_steps=None, train_time_interval=None, every_n_epochs=None, save_on_train_epoch_end=None)[source]

Bases: pytorch_lightning.callbacks.checkpoint.Checkpoint

Save the model periodically by monitoring a quantity. Every metric logged with log() or log_dict() in LightningModule is a candidate for the monitor key. For more information, see Checkpointing.

After training finishes, use best_model_path to retrieve the path to the best checkpoint file and best_model_score to retrieve its score.

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

    directory to save the model file.


    # custom path
    # saves a file like: my/path/epoch=0-step=10.ckpt
    >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')

    By default, dirpath is None and will be set at runtime to the location specified by Trainer’s default_root_dir argument, and if the Trainer uses a logger, the path will also contain logger name and version.

  • filename (Optional[str]) –

    checkpoint filename. Can contain named formatting options to be auto-filled.


    # save any arbitrary metrics like `val_loss`, etc. in name
    # saves a file like: my/path/epoch=2-val_loss=0.02-other_metric=0.03.ckpt
    >>> checkpoint_callback = ModelCheckpoint(
    ...     dirpath='my/path',
    ...     filename='{epoch}-{val_loss:.2f}-{other_metric:.2f}'
    ... )

    By default, filename is None and will be set to '{epoch}-{step}'.

  • monitor (Optional[str]) – quantity to monitor. By default it is None which saves a checkpoint only for the last epoch.

  • verbose (bool) – verbosity mode. Default: False.

  • save_last (Optional[bool]) – When True, saves an exact copy of the checkpoint to a file last.ckpt whenever a checkpoint file gets saved. This allows accessing the latest checkpoint in a deterministic manner. Default: None.

  • save_top_k (int) – if save_top_k == k, the best k models according to the quantity monitored will be saved. if save_top_k == 0, no models are saved. if save_top_k == -1, all models are saved. Please note that the monitors are checked every every_n_epochs epochs. if save_top_k >= 2 and the callback is called multiple times inside an epoch, the name of the saved file will be appended with a version count starting with v1.

  • mode (str) – one of {min, max}. If save_top_k != 0, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For 'val_acc', this should be 'max', for 'val_loss' this should be 'min', etc.

  • auto_insert_metric_name (bool) – When True, the checkpoints filenames will contain the metric name. For example, filename='checkpoint_{epoch:02d}-{acc:02.0f} with epoch 1 and acc 1.12 will resolve to checkpoint_epoch=01-acc=01.ckpt. Is useful to set it to False when metric names contain / as this will result in extra folders. For example, filename='epoch={epoch}-step={step}-val_acc={val/acc:.2f}', auto_insert_metric_name=False

  • save_weights_only (bool) – if True, then only the model’s weights will be saved. Otherwise, the optimizer states, lr-scheduler states, etc are added in the checkpoint too.

  • every_n_train_steps (Optional[int]) – Number of training steps between checkpoints. If every_n_train_steps == None or every_n_train_steps == 0, we skip saving during training. To disable, set every_n_train_steps = 0. This value must be None or non-negative. This must be mutually exclusive with train_time_interval and every_n_epochs.

  • train_time_interval (Optional[timedelta]) – Checkpoints are monitored at the specified time interval. For all practical purposes, this cannot be smaller than the amount of time it takes to process a single training batch. This is not guaranteed to execute at the exact time specified, but should be close. This must be mutually exclusive with every_n_train_steps and every_n_epochs.

  • every_n_epochs (Optional[int]) – Number of epochs between checkpoints. This value must be None or non-negative. To disable saving top-k checkpoints, set every_n_epochs = 0. This argument does not impact the saving of save_last=True checkpoints. If all of every_n_epochs, every_n_train_steps and train_time_interval are None, we save a checkpoint at the end of every epoch (equivalent to every_n_epochs = 1). If every_n_epochs == None and either every_n_train_steps != None or train_time_interval != None, saving at the end of each epoch is disabled (equivalent to every_n_epochs = 0). This must be mutually exclusive with every_n_train_steps and train_time_interval. Setting both ModelCheckpoint(..., every_n_epochs=V, save_on_train_epoch_end=False) and Trainer(max_epochs=N, check_val_every_n_epoch=M) will only save checkpoints at epochs 0 < E <= N where both values for every_n_epochs and check_val_every_n_epoch evenly divide E.

  • save_on_train_epoch_end (Optional[bool]) – Whether to run checkpointing at the end of the training epoch. If this is False, then the check runs at the end of the validation.


For extra customization, ModelCheckpoint includes the following attributes:



  • FILE_EXTENSION = ".ckpt"


For example, you can change the default last checkpoint name by doing checkpoint_callback.CHECKPOINT_NAME_LAST = "{epoch}-last"

If you want to checkpoint every N hours, every M train batches, and/or every K val epochs, then you should create multiple ModelCheckpoint callbacks.

If the checkpoint’s dirpath changed from what it was before while resuming the training, only best_model_path will be reloaded and a warning will be issued.

  • MisconfigurationException – If save_top_k is smaller than -1, if monitor is None and save_top_k is none of None, -1, and 0, or if mode is none of "min" or "max".

  • ValueError – If trainer.save_checkpoint is None.


>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import ModelCheckpoint

# saves checkpoints to 'my/path/' at every epoch
>>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
>>> trainer = Trainer(callbacks=[checkpoint_callback])

# save epoch and val_loss in name
# saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt
>>> checkpoint_callback = ModelCheckpoint(
...     monitor='val_loss',
...     dirpath='my/path/',
...     filename='sample-mnist-{epoch:02d}-{val_loss:.2f}'
... )

# save epoch and val_loss in name, but specify the formatting yourself (e.g. to avoid problems with Tensorboard
# or Neptune, due to the presence of characters like '=' or '/')
# saves a file like: my/path/sample-mnist-epoch02-val_loss0.32.ckpt
>>> checkpoint_callback = ModelCheckpoint(
...     monitor='val/loss',
...     dirpath='my/path/',
...     filename='sample-mnist-epoch{epoch:02d}-val_loss{val/loss:.2f}',
...     auto_insert_metric_name=False
... )

# retrieve the best checkpoint after training
checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
trainer = Trainer(callbacks=[checkpoint_callback])
model = ...


Saving and restoring multiple checkpoint callbacks at the same time is supported under variation in the following arguments:

monitor, mode, every_n_train_steps, every_n_epochs, train_time_interval, save_on_train_epoch_end

Read more: Persisting Callback State

file_exists(filepath, trainer)[source]

Checks if a file exists on rank 0 and broadcasts the result to all other ranks, preventing the internal state to diverge between ranks.

Return type:


format_checkpoint_name(metrics, filename=None, ver=None)[source]

Generate a filename according to the defined template.


>>> tmpdir = os.path.dirname(__file__)
>>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}')
>>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=0)))
>>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch:03d}')
>>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=5)))
>>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}-{val_loss:.2f}')
>>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.123456)))
>>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.12), filename='{epoch:d}'))
>>> ckpt = ModelCheckpoint(dirpath=tmpdir,
... filename='epoch={epoch}-validation_loss={val_loss:.2f}',
... auto_insert_metric_name=False)
>>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.123456)))
>>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{missing:d}')
>>> os.path.basename(ckpt.format_checkpoint_name({}))
>>> ckpt = ModelCheckpoint(filename='{step}')
>>> os.path.basename(ckpt.format_checkpoint_name(dict(step=0)))
Return type:



Called when loading a checkpoint, implement to reload callback state given callback’s state_dict.


state_dict (Dict[str, Any]) – the callback state returned by state_dict.

Return type:


on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)[source]

Save checkpoint on train batch end if we meet the criteria for every_n_train_steps

Return type:


on_train_epoch_end(trainer, pl_module)[source]

Save a checkpoint at the end of the training epoch.

Return type:


on_train_start(trainer, pl_module)[source]

Called when the train begins.

Return type:


on_validation_end(trainer, pl_module)[source]

Save a checkpoint at the end of the validation stage.

Return type:


setup(trainer, pl_module, stage)[source]

Called when fit, validate, test, predict, or tune begins.

Return type:



Called when saving a checkpoint, implement to generate callback’s state_dict.

Return type:

Dict[str, Any]


A dictionary containing callback state.


Saves the best_k_models dict containing the checkpoint paths with the corresponding scores to a YAML file.

Return type:


property state_key: str

Identifier for the state of the callback.

Used to store and retrieve a callback’s state from the checkpoint dictionary by checkpoint["callbacks"][state_key]. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.