pytorch_lightning.lite.LightningLite

class pytorch_lightning.lite.LightningLite(accelerator=None, strategy=None, devices=None, num_nodes=1, precision=32, plugins=None, gpus=None, tpu_cores=None)

Bases: lightning_lite.lite.LightningLite, abc.ABC

Lite accelerates your PyTorch training or inference code with minimal changes required.

• Automatic placement of models and data onto the device.

• Automatic support for mixed and double precision (smaller memory footprint).

• Seamless switching between hardware (CPU, GPU, TPU) and distributed training strategies (data-parallel training, sharded training, etc.).

• Automated spawning of processes, no launch utilities required.

• Multi-node support.

Parameters:

• accelerator (Union[str, Accelerator, None]) – The hardware to run on. Possible choices are: "cpu", "cuda", "mps", "gpu", "tpu", "auto".

• strategy (Union[str, Strategy, None]) – Strategy for how to run across multiple devices. Possible choices are: "dp", "ddp", "ddp_spawn", "deepspeed", "ddp_sharded".

• devices (Union[List[int], str, int, None]) – Number of devices to train on (int), which GPUs to train on (list or str), or "auto". The value applies per node.

• num_nodes (int) – Number of GPU nodes for distributed training.

• precision (Literal[16, 32, 64, 'bf16']) – Double precision (64), full precision (32), half precision (16), or bfloat16 precision ("bf16").

• plugins (Union[PrecisionPlugin, ClusterEnvironment, CheckpointIO, str, List[Union[PrecisionPlugin, ClusterEnvironment, CheckpointIO, str]], None]) – One or several custom plugins

• gpus (Union[List[int], str, int, None]) –

  Provides the same function as the devices argument but implies accelerator="gpu".

  Deprecated since version v1.8.0: gpus has been deprecated in v1.8.0 and will be removed in v1.10.0. Please use accelerator='gpu' and devices=x instead.

• tpu_cores (Union[List[int], str, int, None]) –

  Provides the same function as the devices argument but implies accelerator="tpu".

  Deprecated since version v1.8.0: tpu_cores has been deprecated in v1.8.0 and will be removed in v1.10.0. Please use accelerator='tpu' and devices=x instead.

__init__(accelerator=None, strategy=None, devices=None, num_nodes=1, precision=32, plugins=None, gpus=None, tpu_cores=None)

Methods

__init__([accelerator, strategy, devices, ...])
all_gather(data[, group, sync_grads])	Gather tensors or collections of tensors from multiple processes.
autocast()	A context manager to automatically convert operations for the chosen precision.
backward(tensor, *args[, model])	Replaces loss.backward() in your training loop.
barrier([name])	Wait for all processes to enter this call.
broadcast(obj[, src])	rtype: TypeVar(TBroadcast)
load(filepath)	Load a checkpoint from a file.
no_backward_sync(module[, enabled])	Skip gradient synchronization during backward to avoid redundant communication overhead.
print(*args, **kwargs)	Print something only on the first process.
run(*args, **kwargs)	All the code inside this run method gets accelerated by Lite.
save(content, filepath)	Save checkpoint contents to a file.
seed_everything([seed, workers])	Helper function to seed everything without explicitly importing Lightning.
setup(model, *optimizers[, move_to_device])	Set up a model and its optimizers for accelerated training.
setup_dataloaders(*dataloaders[, ...])	Set up one or multiple dataloaders for accelerated training.
to_device(obj)	Move a torch.nn.Module or a collection of tensors to the current device, if it is not already on that device.

Attributes

device	The current device this process runs on.
global_rank	The global index of the current process across all devices and nodes.
is_global_zero	Whether this rank is rank zero.
local_rank	The index of the current process among the processes running on the local node.
node_rank	The index of the current node.
world_size	The total number of processes running across all devices and nodes.