How to use PyTorch Lightning to implement distributed training across multiple GPUs or nodes?

To implement distributed training across multiple GPUs or nodes using PyTorch Lightning, you can use the DistributedDataParallel (DDP) module. Here are the steps:

1. Choose a distributed training strategy: single-node multi-GPU training, or multi-node distributed training.
2. Import the necessary modules from PyTorch and PyTorch Lightning to set up distributed training.
3. Update your PyTorch Lightning LightningModule to work with distributed training. This involves wrapping your model with DDP and adding a few hooks for synchronization.
4. Create a Trainer instance with the distributed training settings, and pass in the DDP instance as the accelerator.
5. Train your model using model.fit() as usual.

Here’s an example code snippet for single-node multi-GPU training:

import torch
import pytorch_lightning as pl
from torch.nn.parallel import DistributedDataParallel as DDP

class MyModel(pl.LightningModule):
    def __init__(self):
        # define your model architecture here

        # Wrap your model with DDP
        self.model = DDP(self.model)

    def forward(self, x):
        # define your forward pass here

    def training_step(self, batch, batch_idx):
        # your training loop here

    def configure_optimizers(self):
        # define your optimizer

    def train_dataloader(self):
        # define your training data loader

# define your data module here
data_module = MyDataModule()

# set up distributed training
gpu_count = torch.cuda.device_count()
distributed_backend = 'ddp' if gpu_count > 1 else None
trainer = pl.Trainer(distributed_backend=distributed_backend, gpus=gpu_count)

# create your model
model = MyModel()

# train your model
trainer.fit(model, datamodule=data_module)

This example code provides a basic template for configuring single-node multi-GPU training with PyTorch Lightning. You can modify it based on your specific needs, and also refer to the official documentation for more information on setting up distributed training.

For multi-node distributed training, you will need to use a different initialization method and set up a DDPPlugin for communication between nodes.