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.. _checkpointing_advanced:
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Cloud-based checkpoints (advanced)
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Cloud checkpoints
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Lightning is integrated with the major remote file systems including local filesystems and several cloud storage providers such as
`S3 <https://aws.amazon.com/s3/> `_ on `AWS <https://aws.amazon.com/> `_ , `GCS <https://cloud.google.com/storage> `_ on `Google Cloud <https://cloud.google.com/> `_ ,
or `ADL <https://azure.microsoft.com/solutions/data-lake/> `_ on `Azure <https://azure.microsoft.com/> `_ .
PyTorch Lightning uses `fsspec <https://filesystem-spec.readthedocs.io/> `_ internally to handle all filesystem operations.
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Save a cloud checkpoint
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To save to a remote filesystem, prepend a protocol like "s3:/" to the root_dir used for writing and reading model data.
.. code-block :: python
# `default_root_dir`
trainer = Trainer(default_root_dir="s3://my_bucket/data/")
trainer.fit(model)
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Resume training from a cloud checkpoint
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To resume training from a cloud checkpoint use a cloud url.
.. code-block :: python
trainer = Trainer(default_root_dir=tmpdir, max_steps=3)
trainer.fit(model, ckpt_path="s3://my_bucket/ckpts/classifier.ckpt")
PyTorch Lightning uses `fsspec <https://filesystem-spec.readthedocs.io/> `_ internally to handle all filesystem operations.
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Modularize your checkpoints
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Checkpoints can also save the state of :doc: `datamodules <../extensions/datamodules_state>` and :doc: `callbacks <../extensions/callbacks_state>` .
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Modify a checkpoint anywhere
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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 `` .
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.. code :: python
class LitModel(pl.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"]
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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:
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.. code :: python
class LitCallback(pl.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"]
