Plugins¶
Plugins allow custom integrations to the internals of the Trainer such as a custom precision or distributed implementation.
Under the hood, the Lightning Trainer is using plugins in the training routine, added automatically depending on the provided Trainer arguments. For example:
# accelerator: GPUAccelerator
# training type: DDPPlugin
# precision: NativeMixedPrecisionPlugin
trainer = Trainer(gpus=4, precision=16)
We expose Accelerators and Plugins mainly for expert users that want to extend Lightning for:
New hardware (like TPU plugin)
Distributed backends (e.g. a backend not yet supported by PyTorch itself)
Clusters (e.g. customized access to the cluster’s environment interface)
There are two types of Plugins in Lightning with different responsibilities:
TrainingTypePlugin¶
Launching and teardown of training processes (if applicable)
Setup communication between processes (NCCL, GLOO, MPI, …)
Provide a unified communication interface for reduction, broadcast, etc.
Provide access to the wrapped LightningModule
PrecisionPlugin¶
Perform pre- and post backward/optimizer step operations such as scaling gradients
Provide context managers for forward, training_step, etc.
Gradient clipping
Futhermore, for multi-node training Lightning provides cluster environment plugins that allow the advanced user to configure Lighting to integrate with a 4. Custom cluster.
Create a custom plugin¶
Expert users may choose to extend an existing plugin by overriding its methods …
from pytorch_lightning.plugins import DDPPlugin
class CustomDDPPlugin(DDPPlugin):
def configure_ddp(self):
self._model = MyCustomDistributedDataParallel(
self.model,
device_ids=...,
)
or by subclassing the base classes TrainingTypePlugin
or
PrecisionPlugin
to create new ones. These custom plugins
can then be passed into the Trainer directly or via a (custom) accelerator:
# custom plugins
trainer = Trainer(strategy=CustomDDPPlugin(), plugins=[CustomPrecisionPlugin()])
# fully custom accelerator and plugins
accelerator = MyAccelerator(
precision_plugin=CustomPrecisionPlugin(),
training_type_plugin=CustomDDPPlugin(),
)
trainer = Trainer(accelerator=accelerator)
The full list of built-in plugins is listed below.
Warning
The Plugin API is in beta and subject to change. For help setting up custom plugins/accelerators, please reach out to us at support@pytorchlightning.ai
Training Type Plugins¶
Base class for all training type plugins that change the behaviour of the training, validation and test- loop. |
|
Plugin that handles communication on a single device. |
|
Plugin for training with multiple processes in parallel. |
|
Implements data-parallel training in a single process, i.e., the model gets replicated to each device and each gets a split of the data. |
|
Plugin for multi-process single-device training on one or multiple nodes. |
|
DDP2 behaves like DP in one node, but synchronization across nodes behaves like in DDP. |
|
Optimizer and gradient sharded training provided by FairScale. |
|
Optimizer sharded training provided by FairScale. |
|
Spawns processes using the |
|
Provides capabilities to run training using the DeepSpeed library, with training optimizations for large billion parameter models. |
|
Plugin for Horovod distributed training integration. |
|
Plugin for training on a single TPU device. |
|
Plugin for training multiple TPU devices using the |
Precision Plugins¶
Base class for all plugins handling the precision-specific parts of the training. |
|
Base Class for mixed precision. |
|
Plugin for Native Mixed Precision (AMP) training with |
|
Native AMP for Sharded Training. |
|
Mixed Precision Plugin based on Nvidia/Apex (https://github.com/NVIDIA/apex) |
|
Precision plugin for DeepSpeed integration. |
|
Plugin that enables bfloats on TPUs. |
|
Plugin for training with double ( |
|
Native AMP for Fully Sharded Training. |
|
Cluster Environments¶
Specification of a cluster environment. |
|
The default environment used by Lightning for a single node or free cluster (not managed). |
|
An environment for running on clusters managed by the LSF resource manager. |
|
Environment for fault-tolerant and elastic training with torchelastic |
|
Environment for distributed training using the PyTorchJob operator from Kubeflow |
|
Cluster environment for training on a cluster managed by SLURM. |