lightning/docs/source/extensions/accelerators.rst

.. _accelerators:

############
Accelerators
############
Accelerators connect a Lightning Trainer to arbitrary accelerators (CPUs, GPUs, TPUs, etc). Accelerators
also manage distributed communication through :ref:`Plugins` (like DP, DDP, HPC cluster) and
can also be configured to run on arbitrary clusters or to link up to arbitrary
computational strategies like 16-bit precision via AMP and Apex.

An Accelerator is meant to deal with one type of hardware.
Currently there are accelerators for:

- CPU
- GPU
- TPU

Each Accelerator gets two plugins upon initialization:
One to handle differences from the training routine and one to handle different precisions.

.. testcode::

    from pytorch_lightning import Trainer
    from pytorch_lightning.accelerators import GPUAccelerator
    from pytorch_lightning.plugins import NativeMixedPrecisionPlugin, DDPPlugin

    accelerator = GPUAccelerator(
        precision_plugin=NativeMixedPrecisionPlugin(),
        training_type_plugin=DDPPlugin(),
    )
    trainer = Trainer(accelerator=accelerator)


We expose Accelerators and Plugins mainly for expert users who want to extend Lightning to work with new
hardware and distributed training or clusters.


.. image:: ../_static/images/accelerator/overview.svg


.. warning:: The Accelerator API is in beta and subject to change.
    For help setting up custom plugins/accelerators, please reach out to us at **support@pytorchlightning.ai**

----------


Accelerator API
---------------

.. currentmodule:: pytorch_lightning.accelerators

.. autosummary::
    :nosignatures:
    :template: classtemplate.rst

    Accelerator
    CPUAccelerator
    GPUAccelerator
    TPUAccelerator