58 lines
2.7 KiB
ReStructuredText
58 lines
2.7 KiB
ReStructuredText
.. _remote_fs:
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##################
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Remote Filesystems
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##################
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PyTorch Lightning enables working with data from a variety of filesystems, including local filesystems and several cloud storage providers such as
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`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/>`_,
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or `ADL <https://azure.microsoft.com/solutions/data-lake/>`_ on `Azure <https://azure.microsoft.com/>`_.
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This applies to saving and writing checkpoints, as well as for logging.
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Working with different filesystems can be accomplished by appending a protocol like "s3:/" to file paths for writing and reading data.
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.. code-block:: python
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# `default_root_dir` is the default path used for logs and checkpoints
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trainer = Trainer(default_root_dir="s3://my_bucket/data/")
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trainer.fit(model)
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You could pass custom paths to loggers for logging data.
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.. code-block:: python
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from pytorch_lightning.loggers import TensorBoardLogger
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logger = TensorBoardLogger(save_dir="s3://my_bucket/logs/")
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trainer = Trainer(logger=logger)
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trainer.fit(model)
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Additionally, you could also resume training with a checkpoint stored at a remote filesystem.
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.. code-block:: python
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trainer = Trainer(default_root_dir=tmpdir, max_steps=3)
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trainer.fit(model, ckpt_path="s3://my_bucket/ckpts/classifier.ckpt")
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PyTorch Lightning uses `fsspec <https://filesystem-spec.readthedocs.io/>`_ internally to handle all filesystem operations.
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The most common filesystems supported by Lightning are:
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* Local filesystem: ``file://`` - It's the default and doesn't need any protocol to be used. It's installed by default in Lightning.
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* Amazon S3: ``s3://`` - Amazon S3 remote binary store, using the library `s3fs <https://s3fs.readthedocs.io/>`__. Run ``pip install fsspec[s3]`` to install it.
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* Google Cloud Storage: ``gcs://`` or ``gs://`` - Google Cloud Storage, using `gcsfs <https://gcsfs.readthedocs.io/en/stable/>`__. Run ``pip install fsspec[gcs]`` to install it.
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* Microsoft Azure Storage: ``adl://``, ``abfs://`` or ``az://`` - Microsoft Azure Storage, using `adlfs <https://github.com/fsspec/adlfs>`__. Run ``pip install fsspec[adl]`` to install it.
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* Hadoop File System: ``hdfs://`` - Hadoop Distributed File System. This uses `PyArrow <https://arrow.apache.org/docs/python/>`__ as the backend. Run ``pip install fsspec[hdfs]`` to install it.
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You could learn more about the available filesystems with:
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.. code-block:: python
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from fsspec.registry import known_implementations
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print(known_implementations)
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You could also look into :ref:`CheckpointIO Plugin <common/checkpointing:Customize Checkpointing>` for more details on how to customize saving and loading checkpoints.
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