API References¶

Accelerator API¶

`datamodule`	LightningDataModule for loading DataLoaders with ease.
`decorators`	Decorator for LightningModule methods.
`hooks`	Various hooks to be used in the Lightning code.
`lightning`	nn.Module with additional great features.

`base`	Abstract base class used to build new callbacks.
`early_stopping`	Early Stopping
`gpu_stats_monitor`	GPU Stats Monitor
`gradient_accumulation_scheduler`	Gradient Accumulator
`lr_monitor`	Learning Rate Monitor
`model_checkpoint`	Model Checkpointing
`progress`	Progress Bars

`base`	Abstract base class used to build new loggers.
`comet`	Comet Logger
`csv_logs`	CSV logger
`mlflow`	MLflow Logger
`neptune`	Neptune Logger
`tensorboard`	TensorBoard Logger
`test_tube`	Test Tube Logger
`wandb`	Weights and Biases Logger

`TrainingTypePlugin`	Base class for all training type plugins that change the behaviour of the training, validation and test-loop.
`SingleDevicePlugin`	Plugin that handles communication on a single device.
`ParallelPlugin`	Plugin for training with multiple processes in parallel.
`DataParallelPlugin`	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.
`DDPPlugin`	Plugin for multi-process single-device training on one or multiple nodes.
`DDP2Plugin`	DDP2 behaves like DP in one node, but synchronization across nodes behaves like in DDP.
`DDPShardedPlugin`	Optimizer and gradient sharded training provided by FairScale.
`DDPSpawnShardedPlugin`	Optimizer sharded training provided by FairScale.
`DDPSpawnPlugin`	Spawns processes using the `torch.multiprocessing.spawn()` method and joins processes after training finishes.
`DeepSpeedPlugin`	Provides capabilities to run training using the DeepSpeed library, with training optimizations for large billion parameter models.
`HorovodPlugin`	Plugin for Horovod distributed training integration.
`RPCPlugin`	Backbone for RPC Plugins built on top of DDP.
`RPCSequentialPlugin`	Provides sequential model parallelism for `nn.Sequential` module.
`SingleTPUPlugin`	Plugin for training on a single TPU device.
`TPUSpawnPlugin`	Plugin for training multiple TPU devices using the `torch.multiprocessing.spawn()` method.

`PrecisionPlugin`	Base class for all plugins handling the precision-specific parts of the training.
`NativeMixedPrecisionPlugin`	Plugin for native mixed precision training with `torch.cuda.amp`.
`ShardedNativeMixedPrecisionPlugin`	Mixed Precision for Sharded Training
`ApexMixedPrecisionPlugin`	Mixed Precision Plugin based on Nvidia/Apex (https://github.com/NVIDIA/apex)
`DeepSpeedPrecisionPlugin`	Precision plugin for DeepSpeed integration.
`TPUHalfPrecisionPlugin`	Plugin that enables bfloats on TPUs
`DoublePrecisionPlugin`	Plugin for training with double (`torch.float64`) precision.

`ClusterEnvironment`	Specification of a cluster environment.
`LightningEnvironment`	The default environment used by Lightning for a single node or free cluster (not managed).
`TorchElasticEnvironment`	Environment for fault-tolerant and elastic training with torchelastic
`SLURMEnvironment`	Cluster environment for training on a cluster managed by SLURM.

Profiler to check if there are any bottlenecks in your code.

Trainer to automate the training.

Tuner class to tune your model

`cli`
`argparse`
`seed`	Helper functions to help with reproducibility of models.