.. _checkpointing_advanced:

################################## Cloud-based checkpoints (advanced) ##################################

Cloud checkpoints

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.

Save a cloud checkpoint

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` is the default path used for logs and checkpoints
trainer = Trainer(default_root_dir="s3://my_bucket/data/")
trainer.fit(model)

Resume training from a cloud checkpoint

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.