:orphan:

.. _checkpointing_intermediate_1:

############################################### Customize checkpointing behavior (intermediate) ############################################### Audience: Users looking to customize the checkpointing behavior

Modify checkpointing behavior

For fine-grained control over checkpointing behavior, use the :class:~lightning.pytorch.callbacks.ModelCheckpoint object

.. code-block:: python

    from lightning.pytorch.callbacks import ModelCheckpoint

    checkpoint_callback = ModelCheckpoint(dirpath="my/path/", save_top_k=2, monitor="val_loss")
    trainer = Trainer(callbacks=[checkpoint_callback])
    trainer.fit(model)

    # Access best and last model checkpoint directly from the callback
    print(checkpoint_callback.best_model_path)
    print(checkpoint_callback.last_model_path)
    # Or via the trainer
    print(trainer.checkpoint_callback.best_model_path)
    print(trainer.checkpoint_callback.last_model_path)

Any value that has been logged via self.log in the LightningModule can be monitored.

.. code-block:: python

    class LitModel(L.LightningModule):
        def training_step(self, batch, batch_idx):
            self.log("my_metric", x)


    # 'my_metric' is now able to be monitored
    checkpoint_callback = ModelCheckpoint(monitor="my_metric")

Save checkpoints by condition

To save checkpoints based on a (when/which/what/where) condition (for example when the validation_loss is lower) modify the :class:~lightning.pytorch.callbacks.ModelCheckpoint properties.

When

When using iterative training which doesn't have an epoch, you can checkpoint at every N training steps by specifying every_n_train_steps=N.
You can also control the interval of epochs between checkpoints using every_n_epochs, to avoid slowdowns.
You can checkpoint at a regular time interval using the train_time_interval argument independent of the steps or epochs.
In case you are monitoring a training metric, we'd suggest using save_on_train_epoch_end=True to ensure the required metric is being accumulated correctly for creating a checkpoint.

Which

You can save the last checkpoint when training ends using save_last argument.
You can save top-K and last-K checkpoints by configuring the monitor and save_top_k argument.

.. testcode::

    from lightning.pytorch.callbacks import ModelCheckpoint


    # saves top-K checkpoints based on "val_loss" metric
    checkpoint_callback = ModelCheckpoint(
        save_top_k=10,
        monitor="val_loss",
        mode="min",
        dirpath="my/path/",
        filename="sample-mnist-{epoch:02d}-{val_loss:.2f}",
    )

    # saves last-K checkpoints based on "global_step" metric
    # make sure you log it inside your LightningModule
    checkpoint_callback = ModelCheckpoint(
        save_top_k=10,
        monitor="global_step",
        mode="max",
        dirpath="my/path/",
        filename="sample-mnist-{epoch:02d}-{global_step}",
    )

.. note::

    It is recommended that you pass formatting options to ``filename`` to include the monitored metric like shown
    in the example above. Otherwise, if ``save_top_k >= 2`` and ``enable_version_counter=True`` (default), a
    version is appended to the ``filename`` to prevent filename collisions. You should not rely on the appended
    version to retrieve the top-k model, since there is no relationship between version count and model performance.
    For example, ``filename-v2.ckpt`` doesn't necessarily correspond to the top-2 model.

You can customize the checkpointing behavior to monitor any quantity of your training or validation steps. For example, if you want to update your checkpoints based on your validation loss:

.. testcode::

    from lightning.pytorch.callbacks import ModelCheckpoint


    class LitAutoEncoder(LightningModule):
        def validation_step(self, batch, batch_idx):
            x, y = batch
            y_hat = self.backbone(x)

            # 1. calculate loss
            loss = F.cross_entropy(y_hat, y)

            # 2. log val_loss
            self.log("val_loss", loss)


    # 3. Init ModelCheckpoint callback, monitoring "val_loss"
    checkpoint_callback = ModelCheckpoint(monitor="val_loss")

    # 4. Add your callback to the callbacks list
    trainer = Trainer(callbacks=[checkpoint_callback])

What

By default, the ModelCheckpoint callback saves model weights, optimizer states, etc., but in case you have limited disk space or just need the model weights to be saved you can specify save_weights_only=True.

Where

By default, the ModelCheckpoint will save files into the Trainer.log_dir. It gives you the ability to specify the dirpath and filename for your checkpoints. Filename can also be dynamic so you can inject the metrics that are being logged using :meth:~lightning.pytorch.core.LightningModule.log.

.. testcode::

    from lightning.pytorch.callbacks import ModelCheckpoint


    # saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt
    checkpoint_callback = ModelCheckpoint(
        dirpath="my/path/",
        filename="sample-mnist-{epoch:02d}-{val_loss:.2f}",
    )

The :class:~lightning.pytorch.callbacks.ModelCheckpoint callback is very robust and should cover 99% of the use-cases. If you find a use-case that is not configured yet, feel free to open an issue with a feature request on GitHub and the Lightning Team will be happy to integrate/help integrate it.

Save checkpoints manually

You can manually save checkpoints and restore your model from the checkpointed state using :meth:~lightning.pytorch.trainer.trainer.Trainer.save_checkpoint and :meth:~lightning.pytorch.core.LightningModule.load_from_checkpoint.

.. code-block:: python

model = MyLightningModule(hparams)
trainer.fit(model)
trainer.save_checkpoint("example.ckpt")

# load the checkpoint later as normal
new_model = MyLightningModule.load_from_checkpoint(checkpoint_path="example.ckpt")

Manual saving with distributed training

In distributed training cases where a model is running across many machines, Lightning ensures that only one checkpoint is saved instead of a model per machine. This requires no code changes as seen below:

.. code-block:: python

trainer = Trainer(strategy="ddp")
model = MyLightningModule(hparams)
trainer.fit(model)

# Saves only on the main process
# Handles strategy-specific saving logic like XLA, FSDP, DeepSpeed etc.
trainer.save_checkpoint("example.ckpt")

By using :meth:~lightning.pytorch.trainer.trainer.Trainer.save_checkpoint instead of torch.save, you make your code agnostic to the distributed training strategy being used. It will ensure that checkpoints are saved correctly in a multi-process setting, avoiding race conditions, deadlocks and other common issues that normally require boilerplate code to handle properly.

Modularize your checkpoints

Checkpoints can also save the state of :doc:datamodules <../extensions/datamodules_state> and :doc:callbacks <../extensions/callbacks_state>.

Modify a checkpoint anywhere

When you need to change the components of a checkpoint before saving or loading, use the :meth:~lightning.pytorch.core.hooks.CheckpointHooks.on_save_checkpoint and :meth:~lightning.pytorch.core.hooks.CheckpointHooks.on_load_checkpoint of your LightningModule.

.. code-block:: python

class LitModel(L.LightningModule):
    def on_save_checkpoint(self, checkpoint):
        checkpoint["something_cool_i_want_to_save"] = my_cool_pickable_object

    def on_load_checkpoint(self, checkpoint):
        my_cool_pickable_object = checkpoint["something_cool_i_want_to_save"]

Use the above approach when you need to couple this behavior to your LightningModule for reproducibility reasons. Otherwise, Callbacks also have the :meth:~lightning.pytorch.callbacks.callback.Callback.on_save_checkpoint and :meth:~lightning.pytorch.callbacks.callback.Callback.on_load_checkpoint which you should use instead:

.. code-block:: python

import lightning as L


class LitCallback(L.Callback):
    def on_save_checkpoint(self, checkpoint):
        checkpoint["something_cool_i_want_to_save"] = my_cool_pickable_object

    def on_load_checkpoint(self, checkpoint):
        my_cool_pickable_object = checkpoint["something_cool_i_want_to_save"]

Resume from a partial checkpoint

Loading a checkpoint is normally "strict", meaning parameter names in the checkpoint must match the parameter names in the model or otherwise PyTorch will raise an error. In use cases where you want to load only a partial checkpoint, you can disable strict loading by setting self.strict_loading = False in the LightningModule to avoid errors. A common use case is when you have a pretrained feature extractor or encoder that you don't update during training, and you don't want it included in the checkpoint:

.. code-block:: python

import lightning as L

class LitModel(L.LightningModule):
    def __init__(self):
        super().__init__()

        # This model only trains the decoder, we don't save the encoder
        self.encoder = from_pretrained(...).requires_grad_(False)
        self.decoder = Decoder()

        # Set to False because we only care about the decoder
        self.strict_loading = False

    def state_dict(self):
        # Don't save the encoder, it is not being trained
        return {k: v for k, v in super().state_dict().items() if "encoder" not in k}

Since strict_loading is set to False, you won't get any key errors when resuming the checkpoint with the Trainer:

.. code-block:: python

trainer = Trainer()
model = LitModel()

# Will load weights with `.load_state_dict(strict=model.strict_loading)`
trainer.fit(model, ckpt_path="path/to/checkpoint")