docs/source-pytorch/extensions/plugins.rst
.. _plugins:
####### Plugins #######
.. include:: ../links.rst
Plugins allow custom integrations to the internals of the Trainer such as custom precision, checkpointing or cluster environment implementation.
Under the hood, the Lightning Trainer is using plugins in the training routine, added automatically depending on the provided Trainer arguments.
There are three types of plugins in Lightning with different responsibilities:
You can make the Trainer use one or multiple plugins by adding it to the plugins argument like so:
.. code-block:: python
trainer = Trainer(plugins=[plugin1, plugin2, ...])
By default, the plugins get selected based on the rest of the Trainer settings such as the strategy.
.. _precision-plugins:
Precision Plugins
We provide precision plugins for you to benefit from numerical representations with lower precision than 32-bit floating-point or higher precision, such as 64-bit floating-point.
.. code-block:: python
# Training with 16-bit precision
trainer = Trainer(precision=16)
The full list of built-in precision plugins is listed below.
.. currentmodule:: lightning.pytorch.plugins.precision
.. autosummary:: :nosignatures: :template: classtemplate.rst
DeepSpeedPrecision
DoublePrecision
HalfPrecision
FSDPPrecision
MixedPrecision
Precision
XLAPrecision
TransformerEnginePrecision
BitsandbytesPrecision
More information regarding precision with Lightning can be found :ref:here <precision>
.. _checkpoint_io_plugins:
CheckpointIO Plugins
As part of our commitment to extensibility, we have abstracted Lightning's checkpointing logic into the :class:~lightning.pytorch.plugins.io.CheckpointIO plugin.
With this, you have the ability to customize the checkpointing logic to match the needs of your infrastructure.
Below is a list of built-in plugins for checkpointing.
.. currentmodule:: lightning.pytorch.plugins.io
.. autosummary:: :nosignatures: :template: classtemplate.rst
AsyncCheckpointIO
CheckpointIO
TorchCheckpointIO
XLACheckpointIO
Learn more about custom checkpointing with Lightning :ref:here <checkpointing_expert>.
.. _cluster_environment_plugins:
Cluster Environments
You can define the interface of your own cluster environment based on the requirements of your infrastructure.
.. currentmodule:: lightning.pytorch.plugins.environments
.. autosummary:: :nosignatures: :template: classtemplate.rst
ClusterEnvironment
KubeflowEnvironment
LightningEnvironment
LSFEnvironment
SLURMEnvironment
TorchElasticEnvironment
XLAEnvironment